Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
Dr. Chun-Hsin Wang, Chung Hua University, Taiwan
Prof. Wilaiporn Lee, King Mongkut’s University of Technology North, Thailand
Dr. Zhi-Qiang Yao, XiangTan University, China
Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
Prof. Yeonseung Ryu, Myongji University, Korea
Dr. Kyuchang Kang, ETRI, Korea
Prof. Plamena Zlateva, BAS(Bulgarian Academy of Sciences), Bulgaria
Dr. Pasi Ojala, University of Oulu, Finland
Prof. CheonShik Kim, Sejong University, Korea
Dr. Anna bruno, University of Salento, Italy
Prof. Jesuk Ko, Gwangju University, Korea
Dr. Saba Mahmood, Air University Islamabad Pakistan, Pakistan
Prof. Zhiming Cai, Macao University of Science and Technology, Macau
Prof. Man Soo Han, Mokpo National Univ., Korea
Mr. Jose Gutierrez, Aalborg University, Denmark

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
Dr. Noor Zaman, King Faisal University, Al Ahsa Hofuf, Saudi Arabia
Dr. Srinivas Mantha, SASTRA University, Thanjavur, India
Dr. Shahriar Mohammadi, KNTU University, IranProf. Beonsku An, Hongik University, korea
Dr. Guanbo Zheng, University of Houston, USA
Prof. Sangho Choe, The Catholic University of Korea, korea
Dr. Gyanendra Prasad Joshi, Yeungnam University, korea
Dr. Tae-Gyu Lee, Korea Institue of Industrial Technology(KITECH), korea
Prof. Ilkyeun Ra, University of Colorado Denver, USA
Dr. Yong Sun, Beijing University of Posts and Telecommunications, China
Dr. Yulei Wu, Chinese Academy of Sciences, China
Mr. Anup Thapa, Chosun University, korea
Dr. Vo Nguyen Quoc Bao, Posts and Telecommunications Institute of Technology, Vietnam
Dr. Harish Kumar, Bhagwant institute of technology, India
Dr. Jin REN, North china university of technology, China
Dr. Joseph Kandath, Electronics & Commn Engg, India
Dr. Mohamed M. A. Moustafa, Arab Information Union (AIU), Egypt
Dr. Mostafa Zaman Chowdhury, Kookmin University, Korea
Prof. Francis C.M. Lau, Hong Kong Polytechnic University, Hong Kong
Prof. Ju Bin Song, Kyung Hee University, korea
Prof. KyungHi Chang, Inha University, Korea
Prof. Sherif Welsen Shaker, Kuang-Chi Institute of Advanced Technology, China
Prof. Seung-Hoon Hwang, Dongguk University, Korea
Prof. Dal-Hwan Yoon, Semyung University, korea
Prof. Chongyang ZHANG, Shanghai Jiao Tong University, China
Dr. H K Lau, The Open University of Hong Kong, Honh Kong
Prof. Ying-Ren Chien, Department of Electrical Engineering, National Ilan University, Taiwan
Prof. Mai Yi-Ting, Hsiuping University of Science and Technology, Taiwan
Dr. Sang-Hwan Ryu, Korea Railroad Research Institute, Korea
Dr. Yung-Chien Shih, MediaTek Inc., Taiwan
Dr. Kuan Hoong Poo, Multimedia University, Malaysia
Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

Editor Guide
 Introduction for Editor or Reviewer
All the editor group members are to be assigned as a evaluator(editor or reviewer) to submitted journal
papers at the discretion of the Editor-in-Chief. It will be informed by eMail with a Member Login ID and
Password.
Once logined the Website via the Member Login menu in left as a evaluator, you can find out the paper
assigned to you. You can evaluate it there. All the results of the evaluation are supposed to be shown in
the Author Homepage in the real time manner. You can also enter the Author Homepage assigned to
you by the Paper ID and the author's eMail address shown in your Evaluation Webpage. In the Author
Homepage, you can communicate each other efficiently under the peer review policy. Please don't missit!
All the editor group members are supposed to be candidates of a part of the editorial board, dependingon their contribution which comes from history of ICACT TACT as an active evaluator. Because the main
contribution comes from sincere paper reviewing role.

 Role of the Editor
The editor's primary responsibilities are to conduct the peer review process, and check the final cameraready manuscripts for any technical, grammatical or typographical errors.
As a member of the editorial board of the publication, the editor is responsible for ensuring that the
publication maintains the highest quality while adhering to the publication policies and procedures of
the ICACT TACT(Transactions on the Advanced Communications Technology).
For each paper that the editor-in-chief gets assigned, the Secretariat of ICACT Journal will send theeditor an eMail requesting the review process of the paper.
The editor is responsible to make a decision on an "accept", "reject", or "revision" to the Editor-in-Chief
via the Evaluation Webpage that can be shown in the Author Homepage also.

 Deadlines for Regular Review
Editor-in-Chief will assign a evalaution group( a Editor and 2 reviewers) in a week upon receiving acompleted Journal paper submission. Evaluators are given 2 weeks to review the paper. Editors are
given a week to submit a recommendation to the Editor-in-Chief via the evaluation Webpage, once all or
enough of the reviews have come in. In revision case, authors have a maximum of a month to submit
their revised manuscripts. The deadlines for the regular review process are as follows:

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Evalution Procedure

Deadline

Selection of Evaluation Group
Review processing
Editor's recommendation
Final Decision Noticing

1 week2 weeks
1 week
1 week Making Decisions on Manuscript
Editor will make a decision on the disposition of the manuscript, based on remarks of the reviewers. The
editor's recommendation must be well justified and explained in detail. In cases where the revision isrequested, these should be clearly indicated and explained. The editor must then promptly convey thisdecision to the author. The author may contact the editor if instructions regarding amendments to themanuscript are unclear. All these actions could be done via the evaluation system in this Website. The
guidelines of decisions for publication are as follows:

Decision

Description

AcceptAn accept decision means that an editor is accepting the paper withno further modifications. The paper will not be seen again by theeditor or by the reviewers.
The manuscript is not suitable for the ICACT TACT publication.
The paper is conditionally accepted with some requirements. A
revision means that the paper should go back to the original
reviewers for a second round of reviews. We strongly discourage
editors from making a decision based on their own review of themanuscript if a revision had been previously required.

RejectRevision Role of the Reviewer
Reviewer Webpage:
Once logined the Member Login menu in left, you can find out papers assigned to you. You can alsologin the Author Homepage assigned to you with the paper ID and author's eMail address. In there you
can communicate each other via a Communication Channel Box.

Quick Review Required:
You are given 2 weeks for the first round of review and 1 week for the second round of review. You must
agree that time is so important for the rapidly changing IT technologies and applications trend. Pleaserespect the deadline. Authors undoubtedly appreciate your quick review.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Anonymity:
Do not identify yourself or your organization within the review text.

Review:
Reviewer will perform the paper review based on the main criteria provided below. Please provide
detailed public comments for each criterion, also available to the author.
z How this manuscript advances this field of research and/or contributes something new to theliterature?
z Relevance of this manuscript to the readers of TACT?
z Is the manuscript technically sound?
z Is the paper clearly written and well organized?
z Are all figures and tables appropriately provided and are their resolution good quality?
z Does the introduction state the objectives of the manuscript encouraging the reader to read on?
z Are the references relevant and complete?

Supply missing references:
Please supply any information that you think will be useful to the author in revision for enhancing
quality of the paperor for convincing him/her of the mistakes.

Review Comments:
If you find any already known results related to the manuscript, please give references to earlier papers
which contain these or similar results. If the reasoning is incorrect or ambiguous, please indicate
specifically where and why. If you would like to suggest that the paper be rewritten, give specificsuggestions regarding which parts of the paper should be deleted, added or modified, and pleaseindicate how.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Journal Procedure
Dear Author,¾ You can see all your paper information & progress.
¾ Step 1. Journal Full Paper Submission
Using the Submit button, submit your journal paper through ICACT Website, then you will get newpaper ID of your journal, and send your journal Paper ID to the Secretariat@icact.org for the review andeditorial processing. Once you got your Journal paper ID, never submit again! Journal Paper/CRF
Template¾ Step 2. Full Paper Review
Using the evaluation system in the ICACT Website, the editor, reviewer and author can communicate
each other for the good quality publication. It may take about 1 month.

¾ Step 3. Acceptance Notification
It officially informs acceptance, revision, or reject of submitted full paper after the full paper review
process.

Status
Acceptance
Revision
RejectActionGo to next Step.
Re-submit Full Paper within 1 month after Revision Notification.
Drop everything.

¾ Step 4. Payment Registration
So far it's free of charge in case of the journal promotion paper from the registered ICACT conference
paper! But you have to regist it, because you need your Journal Paper Registration ID for submission of
the final CRF manuscripts in the next step's process. Once you get your Registration ID, send it to
Secretariat@icact.org for further process.

¾ Step 5. Camera Ready Form (CRF) Manuscripts Submission
After you have received the confirmation notice from secretariat of ICACT, and then you are allowed tosubmit the final CRF manuscripts in PDF file form, the full paper and the Copyright Transfer Agreement.
Journal Paper Template, Copyright Form Template, BioAbstract Template,

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Journal Submission Guide
All the Out-Standing ICACT conference papers have been invited to this "ICACT Transactions on the
Advanced Communications Technology" Journal, and also welcome all the authors whose conference
paper has been accepted by the ICACT Technical Program Committee, if you could extend new contents
at least 30% more than pure content of your conference paper. Journal paper must be followed toensure full compliance with the IEEE Journal Template Form attached on this page.

¾ How to submit your Journal paper and check the progress?
Step 1. Submit Using the Submit button, submit your journal paper through ICACT

Step 2. Confirm

Step 3. Review

Website, then you will get new paper ID of your journal, and send yourjournal Paper ID to the Secretariat@icact.org for the review and editorial
processing. Once you got your Journal paper ID, never submit again! Using
the Update button, you can change any information of journal paper
related or upload new full journal paper.
Secretariat is supposed to confirm all the necessary conditions of yourjournal paper to make it ready to review. In case of promotion from theconference paper to Journal paper, send us all the .DOC(or Latex) files ofyour ICACT conference paper and journal paper to evaluate the differenceof the pure contents in between at least 30% more to avoid the self
replication violation under scrutiny. The pure content does not include any
reference list, acknowledgement, Appendix and author biography
information.
Upon completing the confirmation, it gets started the review process thru
the Editor & Reviewer Guideline. Whenever you visit the Author
Homepage, you can check the progress status of your paper there fromstart to end like this, " Confirm OK! -> Gets started the review process > ...", in the Review Status column. Please don't miss it!

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Volume. 10 Issue. 1

5J

1 Early Detection of LDDoS Attacks in IoT Utilizing Locality Sensitive Incremental TSVM

1336

MethodXiaochun Yin*, Zengguang Liu**, Deyong Liu***, Zhenge Liu*****Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science & Technology, Shouguang, Weifang,
Shandong, China**College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao,
Shandong, China***Shandong Software Engineering Technology Center, Weifang University of Science & Technology, Shouguang,
Weifang, Shandong, China****School of Software & Microelectronics, Peking University, Beijing, China

Volume. 10 Issue. 2

6J

1 Fixed–Point Arithmetic for Implementing Massive MIMO Systems

1345

Mi Tian, Mihai Sima, and Michael McGuire
Department of Electrical and Computer Engineering, University of Victoria P.O. Box 1700 Stn CSC, Victoria, B.C. V8W
2Y2, Canada

Volume. 10 Issue. 3

6J

1 Automatic Vocabulary Grouping and Deep Combination for News Credibility and

1356

Reliability Evaluation Corresponding to Specific Language
Ming-Shen Jian*, Rong-Bin Deng**, Chen-Wei Fang***, Hua-Yu Wu****, Wen-Hsiang Hsieh*****
Cloud Computing and Intelligent System Lab., Dept. of CSIE, National Formosa University Yunlin County, Taiwan 632

Volume. 10 Issue. 4

6J

1 A Novel Fully Distributed EPON-Based 5G RAN Architecture Modeling with Handover

1364

Analysis
Syed R. Zaidi*, Ajaz Sana*, and Shahab Hussain***Dept. of Engineering, Physics & Technology, Bronx Community College of the City University of New York, USA** Mobile Networks Department, Nokia Corporation, 1 Robbins Rd., Westford, MA 01886, USA

2 A Method for Controlling Scan Rate Based on Estimated Retransmission Rate of1374

Background Traffic
Kenta SUZUKI, Takuya KURIHARA, Kazuto YANO, Yoshinori SUZUKI
Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)Volume. 10 Issue. 5

6J
1385

1 Ransomware Detection Using Open-source Tools
Sun-Jin Lee, Hye-Yeon Shim, Yu-Rim Lee, Tae-Rim Park, Il-Gu Lee
Department of Future Convergence Technology Engineering, Sungshin Women’s University, South Korea

2 Dimension Dependent Effective Index Analysis for a Nano-scale Silicon Waveguide in

1392

Transverse ModeA. T. C. Chen*, R. Petra*, K. S. K. Yeo* and M. Rakib Uddin*** Electrical and Electronic Engineering Programme Area, Faculty of Engineering, Universiti Teknologi Brunei(UTB), Gadong, Brunei Darussalam** The State University of New York Research Foundation, State University of New York Polytechnic Institute,
Fuller Road, Albany, New York 12203

Volume. 10 Issue. 6

6J

1 An Efficient hole Recovery Method in Wireless Sensor Networks

1399

Mary Wu
Dept. Of Computer Culture, Yongnam Theological University and Seminary, Korea

2 End-to-End Routing Algorithm Based on Max-Flow Min-Cut in SDN Controllers

1405

Nada Alzaben*, Daniel W. Engels*** Dept. of Computer Science, College of Computer Science and Information, Princess Nourah Bint Abdulrahman
University, Riyadh, Saudi Arabia**AT&T Center for Virtualization, Southern Methodist University, Dallas, USA

Copyright ⓒ 2021 GiRI (Global IT Research Institute)


ICACT
Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1336

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=KHQJH/LX*Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science & Technology, Shouguang,
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Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021
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Copyright ⓒ 2021 GiRI (Global IT Research Institute)

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Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1340

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Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1345

Fixed–Point Arithmetic for Implementing
Massive MIMO Systems
Mi Tian, Mihai Sima, and Michael McGuire
Department of Electrical and Computer Engineering, University of Victoria
P.O. Box 1700 Stn CSC, Victoria, B.C. V8W 2Y2, Canada
tianmi66@uvic.ca, msima@ece.uvic.ca, mmcguire@uvic.ca

Abstract—Massive MIMO base stations are expensive to build
due to the requirement for a large number of RF transceivers
and high-resolution analog-to-digital converters. A way to reducethe implementation cost is to build the base stations with
inexpensive hardware, resulting in the received signals to becoarsely quantized. First, the required signal power neededto achieve different receiver Bit-Error Rate (BER) levels isdetermined, as well as the extra signal power needed dueto the quantization for given BER levels. To implement thedata detection and decoding process in real time, ﬁxed-point
arithmetic with reduced precision is used. This article also
reports the minimum wordlength needed to maintain the BER
at acceptable levels. Speciﬁcally, the eigenvalue decomposition,
which is the most computationally demanding portion of thereceiver algorithm, can be calculated with wordlengths of 7 and10 bits for eigenvectors and eigenvalues, respectively.
Index Terms—Massive MIMO, ﬁxed-point arithmetic

I. I NTRODUCTION
The number of Receive Antennas (Receivers), R, deployed
in a Massive Multiple-Input Multiple-Output (MMIMO) basestation is much larger than the number of Transmit Antennas(Transmitters), T , where only one transmit antenna per mobile
user is considered in this article. Since large-scale computationis needed for data detection and decoding based on measurements from high-resolution Analog-to-Digital Converters(ADC) connected to highly-linear RF front-end ampliﬁers foreach antenna, the cost of a MMIMO base station is very high.
A way to reduce the cost of the implementation is to build
the base stations with inexpensive low-end hardware [1], suchas simple RF receivers feeding the received signals to coarse(1-, 2-, or 3-bit) ADCs [2], [3].
The conceptual equations of the detection process are given
below, where X is the T×1 transmitted complex signal vector,
Y is the R × 1 received complex signal vector, H is theManuscript received January 23, 2021. This work was supported in part by
the University of Victoria, British Columbia, Canada, under a PhD fellowship,
and a follow-up of the invited journal to the accepted and presented paper
entitled “Massive MIMO in Fixed-Point Arithmetic” of the 23rd International
Conference on Advanced Communication Technology (ICACT2021).
Mi Tian is a postdoctoral fellow in the Department of Electrical andComputer Engineering at University of Victoria, British Columbia, Canada(e-mail: tianmi66@uvic.ca).
Mihai Sima is an associate professor in the Department of Electrical andComputer Engineering at University of Victoria, British Columbia, Canada(corresponding author, phone: +1-250-721-8680, e-mail: msima@ece.uvic.ca).
Michael McGuire is an associate professor in the Department of Electricaland Computer Engineering at University of Victoria, British Columbia,
Canada (e-mail: mmcguire@uvic.ca).

R ×T channel transfer matrix, where each entry is modeled
as an independent identically distributed as complex circulary
symmetric Gaussian random value, N is an R × 1 complex
noise vector, and N  is a T ×1 complex noise vector.
Y = H ·X +N ⇒ H H·Y = (H H · H) · X +H H · N (1)    ΘN
The multiplication of both sides by H H converts the R × T
system to a T × T system, which is less expensive to solve.
It is known that the Minimum Mean Square Error (MMSE)
estimate of X from the reduced-order system has the samemean squared error as the solution of the original system [18].
The estimation of X through the solution of this linear systemis the most computationally demanding portion of the receiveralgorithm for reasonably high values of T . In this process,two major operations need to be performed: (i) calculationof the Hermitian matrix Θ = H H · H, and (ii) EigenValue
Decomposition (EVD) [4] of matrix Θ = Q · Λ · QH followedby solving the linear system by matrix multiplications.
The challenge is to perform these linear-algebra operations
in real time on inexpensive hardware, such as reasonably sized
Field-Programmable Gate Arrays (FPGA). To achieve such
performance, only ﬁxed-point arithmetic [5], [6] will be used.
The channel matrix, H, and the received signal vector, Y ,will be coarsely quantized with B = 1, B = 2, or B = 3 bits.
This article expands our previous work [31] and reports the
FPGA ﬁxed-point arithmetic implementation details together
with the minimum wordlength needed in the data detection
and decoding process to maintain the BER at acceptable levels.
Our contributions are as follows.
1) Strategy to efﬁciently load the estimated channel matrix,
H, into FPGA and efﬁciently calculate the matrix Θ.
2) FPGA implementation of the squared Euclidean norm
and the square root operations with reduced precisionused in converting Θ ﬁrst into a Hessenberg form andthen into a diagonal form.
3) Assessment of the minimum wordlength needed to complete the eigenvalue decomposition of matrix Θ throughthe Francis-Kublanovskaya algorithm.
4) Assessment of the BER and the extra signal power which
is needed to compensate for the information loss due toquantization for different coarse quantization levels.
The article is organized as follows. Background information
is presented in Section II. Coarse quantization in Massive

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

MIMO uplink communications is analyzed in Section III.
The detection process implemented in ﬁxed-point arithmetic
is presented in Section IV. Section V concludes the article.
II. BACKGROUNDThis section provides the fundamentals of MMIMO iterative
channel estimation and simulation software for coarsely quantized MMIMO communications used in our previous work. It
also outlines the basic FPGA architectural features.
A. Iterative Channel Estimation for MMIMOIn MMIMO a large number of sufﬁciently-spaced receive
antennas results in a large probability of a high-capacity communications channel from each mobile user’s transmitter to thebase station [7]. This allows MMIMO systems to communicate
over random multipath propagation channels with transmission
power nearly as low as ideal Additive White Gaussian Noise(AWGN) channels. To take advantage of this situation, thereceiver requires accurate estimates of radio Channel State
Information (CSI), which in turn leads to the requirement fora large number of pilot symbols to be used when standard
channel estimation techniques are employed [8].
Recently we have shown that the channel state information
can be accurately estimated from coarsely quantized measurements using an iterative procedure, where data symbols
detected and decoded in previous iterations are used as additional (virtual) pilot symbols [9]. For the ﬁrst iteration, only thepilot symbols are used to estimate the channel, since no priorknowledge of the transmitted data is available. For the secondand following iterations, channel estimation is enhanced byusing the estimated data symbols from the previous iterations
as virtual pilot symbols, to get a lower error estimate of thechannel parameters. Our simulation results indicate that an
accurate estimation is achieved in three iterations or less.
B. Simulation Software for Coarsely Quantized MMIMO
In our previous work [9] the linear system shown in Equation (1) was solved in 64-bit ﬂoating-point arithmetic [10] using the standard MATLAB® linear solution mechanism, where = H + · Y , where H + is the pseudo-inverse, is coded asX
X = H\Y , as shown in Equations (2) and (3). While thesestandard solution computations give accurate results, direct
implementation of these solutions for use in MMIMO receivers
would require computational hardware with prohibitive cost inbase stations. The backslash operator method was also applied
for estimating the channel matrix, H.
= H\YY = H ·X +N ⇒ X(2)
 = Θ \ (H H · Y ) (3)HH · Y = Θ · X + N  ⇒ X
To reduce the latency of estimating X, we have recently
proposed to solve the linear system in ﬁxed-point arithmetic
with reduced precision [31] through an eigenvalue decomposition of the Hermitian matrix Θ, as shown in Equation (4).
It should be observed that the eigenvalue decomposition is
intrinsically stable, since unitary matrix Q is a rotation matrix,
which means that all its elements range from −1.0 to +1.0.
 = Q · Λ−1 · QH · H H · Y (4)Θ = Q · Λ · QH ⇒ X

1346

C. Coarse Quantization
When signal x is in the range of the quantization range i,ii≤ x < qmax
, its reported quantized value is q(x) = qi .
qmin
The values of qi for all the intervals i, which minimize2
the mean square error, E[|q(x) − x| ], where E [·] is the
expectation operator, are easily shown
be the mean value
 of
 to
i
i
[11].
≤ x < qmax
the signal in each interval qi = E x|qmin
An independent Additive White Gaussian Noise (AWGN)
channel with mean μ = 0 and standard deviation, σ = 1,
is assumed for each receive antenna in this article. Table I
presents the probabilities, quantization thresholds, and thecorresponding bin-average values for B = 1-bit, B = 2-bit,
and B = 3-bit quantization processes for a Gaussian randomvariable with zero mean and unity standard deviation.
TABLE IP ROBABILITIES , QUANTIZATION THRESHOLDS , AND THE
CORRESPONDING BIN - AVERAGE VALUES .
B

Probabilities

1

50%

2

25%

3

12.5%

Quantizationthresholds
00±0.674489572
0±0.318634923±0.674489572±1.150349346

Bin-averagevalues
±0.797884561±0.324662678±1.271106444±0.157972012±0.491353344±0.895384581±1.646828306

In ﬁxed-point representation, it is beneﬁcial to scale up
the smallest magnitude of the bin-average values, since thiswill simplify the implementation and slightly increase theprecision. For example, in a B = 2 quantization process,0.324662678 is promoted to 1.000000000, and 1.271106444 is
promoted to 1.271106444/0.324662678 = 3.915160350. The
other quantization processes will follow a similar pattern. The
updated values are presented in Table II.
TABLE IIQ UANTIZATION THRESHOLDS , SCALE FACTORS , AND THE
CORRESPONDING BIN - AVERAGE VALUES .
B
1
2
3

Quantizationthresholds
00±0.674489572
0±0.318634923±0.674489572±1.150349346

Scale factor
0.797884561
0.324662678
0.157972012

Bin-averagevalues±1.000000000±1.000000000±3.915160350±1.000000000±3.110382264±5.667995047±10.424810617

It is apparent that the measurements of the channel output
can take only a small number of different values (for example,±1.0000 and ±3.9152 in a process with B = 2 quantizationbits). The estimated channel coefﬁcients, the entries of H, arealso coarsely quantized to reduce the computation cost in thereceiver. Since the quantized values have equal probability, theentropy of the quantized signal is maximized, which in turnmaximizes the upper bound of the mutual information between
the received signal and the transmitted signal when their joint
distribution is not known [12].

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

D. FPGA Architecture
Modern FPGA architectures consist of ﬁve types of modules: I/O Blocks (IOB), Conﬁgurable Logic Blocks (CLB),
Digital Signal Processing (DSP) slices, Block Random Access
Memories (BRAM), and a conﬁgurable Interconnection Network. The CLBs belonging to the ﬁne-grained fabric are organized as a 2D array, where each CLB includes a conﬁgurable
Look-Up Table (LUT) to implement bit-level logic functions,dedicated carry logic to support arithmetic operations suchas binary and ternary adders [27], dedicated multiplexors,
and Flip-Flops (FF). Software support for the ﬁne-grained
fabric includes macros and primitives, such as CARRY4 which
concatenates four LUTs to build a 4-bit binary / ternary adderor subtractor, MUXF7 and MUXF8 which instantiate 2-to-1
multiplexors, BRLSHFT4 and BRLSHFT8 which instantiate
4-bit and 8-bit barrel shifters, etc. The DSP slices belong to thecoarse-grained fabric and support the implementation of large
units such as 25 × 18 two’s-complement multipliers, 48-bit
acumulators, dual 24-bit or quad 12-bit adders, subtractors,
and accumulators. The conﬁgurable interconnection network
connects these the modules together to complete the implementation of digital circuits.
Modern FPGAs also integrate Block Random Access Memories (BRAM) on chip. The BRAM in the Virtex-7 family [29]can operate as either one 36 Kb dual-port RAM, which canbe conﬁgured as 32K × 1, 16K × 2, 8K × 4, 4K × 9, 2K × 18,
1K × 36, or 512 × 72, or two independent 18 Kb dual-port
RAMs, where each RAM can be conﬁgured as 16K×1, 8K×2,
4K × 4, 2K × 9, 1K × 18, or 512 × 36. These BRAMs canbe used as large storage area or as large LUTs with multiple
outputs to implement logic functions.
III. M ASSIVE MIMO U PLINK C OMMUNICATIONS WITHC OARSE Q UANTIZATIONIn this article, we consider the receivers in the base stationsof massive MIMO communications systems. The number oftransmitters (or users) is denoted as T , each of which has
one antenna. The base station is equipped with R antennas.
The transmit and receiver antennas are spaced so that thechannel propagation gains from each pair of transmit andreceive antennas are independent. A spacing greater than
one half of a wavelength between antennas can accomplish
this independence. For a communication frequency of 1 GHz,
which corresponds to a wavelength of λ = 30 cm, an arrayof 256 antennas√can be organized as a square with the side
length equal to 256 × 30
2 = 240 cm = 2.4 m. Such an arraycan be mounted, for example, on the roof of a city building.
If the measured signal on the receiver antennas for sample
time n is held in a length R vector denoted as y [n], with thefull received signal being denoted as
L−1

y [n] =

h [l] x [n − l] + v [n] ,

(5)

l=0

where h [l] is the R-by-T matrix holding channel gains fromthe transmitters to receivers with the entry in column t on row
r specifying the propagation gain from transmitter t to receive
antenna r for the delay of l sample periods, then x [n] is a

1347

length T vector specifying the transmitted signals at sample
time n, v [n] is a length R vector giving the measurement
noise at sample n, and L is the length of the channel impulse
responses in sample periods. The problem of the receiver is
to estimate the vectors x [n] from the received signals y [n].
A method of accomplishing this in modern wireless systems,
such as advanced Wireless Local Area Networks (WLANs) or
5G advanced radio networks, is the use of Frequency Domain
Equalization (FDE) [17]. For FDE, the received signals for all
receiver antennas are ﬁrst split into blocks of N samples. The
key to OFDM is to compute the Discrete Fourier Transform(DFT) of the received signal y [n] as
N −1

y [n] exp − j

Y [k] =
n=0

2πkn
N

for k = 0 . . . N − 1 (6)where N is the length of the DFT where a value of N = 256
is often used. If a cyclic preﬁx of CP > L samples fromthe end of each block of N samples is copied and prepended
to the start by the transmitter, as is performed in the OFDM
signalling method used in most modern wireless standards,
then a convolution in time is converted into a multiplicationin the DFT domain. Thus, the relation from (5) becomes:
Y [k] = H [k] X [k] + V [k] for k = 0, . . . , N − 1(7)where H [k], X [k], and V [k] are the DFTs of time-domain
signals h [l], x [n], and v [n], respectively (Equation 1 is
one out of N instances of Equation 7 for an unspeciﬁed
frequency index k). It is apparent that the MIMO signallingover multipath radio channels becomes a simple matrix-vector
multiplication in the frequency domain. The advantage of
MIMO-OFDM signalling with FDE is that the equalization of
the multiple antenna, multipath radio propagation channel canbe reduced to linear algebra [18] where the methods describedabove can be applied.
For MIMO systems, each antenna r = 1, . . . , R requires aradio receiver. In Massive MIMO (MMIMO) systems, where
R is very large, this can lead to a prohibitive cost. If high resolution Analog-to-Digital Converters (ADCs) and highly-linear
RF ampliﬁers are used, they can make up a large proportionof this cost. However, we can show that only inexpensive
devices are needed for many MMIMO systems. This article
considers an implementation of MMIMO in terms of Bit-Error
Rate (BER) using both demodulation linked with an error
correction code as opposed to [1]–[3] which only present
capacity calculations. Here we present a demonstration of a
MMIMO system performance showing that highly quantizedmeasurements with lower precision calculations can give acceptable BER performance.
For the sake of presentation, the numbers of transmit andreceive antennas will be powers of 2. A MMIMO system ina Standard Conﬁguration (SC), as we propose to name it, has
T = 16, R = 128. Other conﬁgurations can be of practicalinterest, for example Extended Users (EU) with T = 32, R =128, Extended Base Station (EBS) with T = 16, R = 256,
Extended Conﬁguration (EC) with T = 32, R = 256, andMaximum Conﬁguration (MC) with T = 64, R = 256, butthey will not be considered in this article.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

The number of taps L required to model the radio channel
for acceptable receiver performance is determined by thedifference in propagation distance between the shortest radio
signal path and the longest radio signal path with signiﬁcantreceived power. This distance is directly related to the so-called
delay spread of the radio propagation channel after division by
the radio signal propagation speed. The tolerated propagation
distance difference is given by L × Ts × c where Ts is the
sampling time and the c is radio signal propagation speed
which is approximately 3 × 108 m/s. For the radio systemsof greatest interest, such as 5G cellular and advanced wireless
local area networks, L = 9 and Ts ≈ 10−7 seconds [19]. This
indicates that the system described in this article tolerates adifference of propagation distance up to 9 × 10−7 × 3 × 108 =
270 meters, which is sufﬁcient for most urban microcells orindoor networking applications.
An MMIMO system in Standard Conﬁguration was simulated using 4-QPSK modulation having a standard rate 1/2
convolutional error corrrection code with a constraint lengthof 7. We have assumed that the receiver has access to ideal
channel state information, since it has been shown that withpilot-signals and iterative receiver algorithms, the channel
estimation error can be made smaller than the measurement
noise [9]. In practice, several methods exist for MIMO channel
estimation [20]. The radio channel from each transmitter toreceive antenna has a random propagation path with a lengthof L = 9 sample periods. In the Standard Conﬁguration of
R = 128 and T = 16 for a total received energy over
all receivers per data bit over measurement noise density ateach receiver, Eb /N0 , of 5.15 dB an acceptable Bit Error
Rate (BER) after coding of about 10−6 is achieved using asimulated ideal ADC with zero quantization error [9]. This
provides an ideal system ﬁgure of merit to compare our systems with quantized measurements with. Given that R/T = 8,
the signal-to-noise ratio (SNR) on each receive antenna isequal to 5.15 dB − 10 log 8 = 5.15 dB − 9 dB = −3.85 dB.
This indicates that the noise power is signiﬁcantly greater than
the information signal power on each individual antenna, sothat if a high resolution ADC is employed most of its output
bits are nearly independent of the information signal. This
provides the motivation for the use of coarse quantization, as
for high-resolution quantizers the bits of lower signiﬁcance aremostly independent of the data signal.
The system simulations are run with lower resolution ADCs
to see the effects on the BER. For 4-bit quantization (B = 4)
at 5 dB of Eb /N0 , the BER is increased to 1.2 × 10−6 with anadditional 0.05 dB of power being required to match the BER
performance of 10−6 for ideal non-quantized measurements.
For 3-bit quantization (B = 3), the BER is increased to
2.2 × 10−6 with an additional 0.22 dB of power required tomatch the BER performance of ideal non-quantized measurements. For 2-bit quantization (B = 2), the BER is increased toabout 2×10−5 with an additional 0.85 dB of power required tomatch the performance of ideal non-quantized measurements.
Finally, for 1-bit quantization (B = 1), the BER is increased to
4 × 10−3 with an additional 3 dB of power required to match
the performance of ideal non-quantized measurements. It isapparent that there is not much gain in the BER to operate

1348

above a 4-bit quantization. The additional 3 dB of power
needed by a 1-bit quantization can be compensated out by doubling the number of receive antennas (which is mechanicallyfeasible). A good trade-off is the 2-bit quantization (B = 2),
which is used in this article as a case study to discuss theimplementation of the data detection and decoding process.
IV. D ETECTION IN F IXED -P OINT A RITHMETICIn this section the major operations of the receiver algorithm implemented in ﬁxed-point arithmetic (calculating
the Hermitian matrix Θ, its Hessenberg decomposition, andthe eigenvalue decomposition through a numerically stablealgorithm) are discussed, and numerical results for Standard
Conﬁguration (T = 16, R = 128) are presented.
A. Calculating Θ = H H· H in Fixed-Point Arithmetic
The computational pattern for calculating the matrix Θ =H H · H is sum-of-products, and the computation budget
consists of T 2 inner products of R-element complex vectors.
The inner product of two R-element complex-valued column
vectors X and Y
X = [ x1 + j x1 , . . . , xR + j xR ]T
 T
Y = [ y1 + j y1 , . . . , yR
+ j yR](8)

is given by
X H· Y = [ (x1 y1 + x1 y1 ) + . . . + (xR yR+ xR yR
)]
 
 
 


)]
− j [ (x1 y1 − x1 y1 ) + . . . + (xR yR − xR yR(9)In coarse quantization with B = 2-bit precision, only
the values ±1.0000 and ±3.9152 can occur, as shown in
Subsection II-C. Their encoding can be, for example, 10, 11,00, and 01. It is apparent that these codes do not represent
numerical values; they are merely labels. As a result, arithmetic cannot be directly implemented, and parallel counters[21]–[26] cannot be directly used to perform the multioperand
additions required for the calculation of inner products. This
problem is addressed below. In any case, the FPGA I/O datatransfer time is signiﬁcantly reduced due to this encoding style.
To calculate X H· Y , the products of two B-bit quantizedvalues (such as x1 y1 ) are ﬁrst calculated. The number of
product values is also small, reaching 6 for B = 2 (±1.0000,±3.9152, and ±15.3288, which can be encoded with 3 bits).
Each product is a 4-bit logic function (2 bits for each of themultiplicands and multipliers). Its three bits can be calculated
in parallel with three LUTs (one LUT per bit). It should beobserved that these products are also labels corresponding tovalues, not the standard numerical values.
Collapsing these products is performed in two stages. In
the ﬁrst stage, BRAMs, which are each conﬁgured as a
4K × 9 memory element, are used as look-up tables. Four
3-bit products are concatenated to form a 12-bit address into
a BRAM, which in turn returns their sum. The maximum
4-product sum is 4×15.3288, which is a 7-bit quantity (sign bit
included), if only the whole part is retained. However, since a
BRAM provides a 9-bit output, two additional bits of precision

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

R × T × B bits. In standard conﬁguration with B = 16-bit
precision, a number of 2 × 128 × 16 × 16 = 65536 bits will
have to be downloaded and stored into FPGA. Given that the
XC7VX1140T, which is the largest FPGA in the top of theline family of Virtex-7, has only 1100 I/O pins, all those bits
cannot be downloaded in parallel. The beneﬁts of the coarse
quantization are apparent, as both the channel state transfer
time and storage capacity requirement are highly reduced.
A channel matrix H in the standard conﬁguration has
R×T = 128×16 = 2048 complex-valued elements. Even withcoarse quantization, the number of FPGA I/O pins is too small
to accomodate the transfer of all these elements in parallel.
A single column, which includes R = 128 complex-valued
elements (or 2 R = 256 real-valued elements), would require2 × 256 = 512 I/O pins with a coarse quantization with
B = 2 bits. Since the XC7VX1140T FPGA, which is the
top of the line in the Virtex-7 family, has 1100 I/O pins, it is
difﬁcult to download the entire matrix H in parallel, since
not many pins would remain available for other tasks. As
a result, multiplexing is needed. It is mentioned that Serializer / Deserializer (SERDES) techniques [28] are intentionally
not used in order to keep the digital hardware of each receive
antenna as simple and inexpensive as possible.
H H row / H column downloading order
123

T
1
22
333

1
2
3
T

HH
R elements

R elements

are available at no additional cost, making the sum a 9-bit
quantity (sign bit included).
The advantage of using BRAMs is that the collapsing offour products into one larger sum is performed simultaneouslywith their conversion from labels to numerical values. In
Equation (9) it is apparent that 2R = 256 3-bit products perreal component (and 2R = 256 3-bit products per imaginary
component) need to be collapsed. This means that 2 × 2R
4 =128 BRAM accesses per complex-valued inner product areneeded. In the second stage, the remaining 2R
4 = 64 sumsin each of the real and imaginary components will be added
together by a tree of ternary ripple-carry adders [27]. The
ﬁnal sum is a 9 + log2 64 = 15-bit quantity (2 fractional
bits and 1 sign bit are assigned to the value) in the extreme
case that all values have maximum magnitude and add up
constructively. This translates into a tree of 22+8+3+1 = 34
ternary adders with the wordlength of 15 bits for each realand imaginary component, which requires an estimate of
2 × 15 × 34 = 1020 LUTs. As a result, the latency of an innerproduct is the sum of a LUT delay (for calculating the initial
3-bit products), a BRAM delay (for calculating the sum of four
products), and the delays of four 15-bit ripple-carry adders(for collapsing the remaining sums). The two fractional bitsof the ﬁnal sum are removed through right shift and rounding;thus, the ﬁnal sum is a 13-bit integer. Since Θ is Hermitian,its diagonal components are real, so the diagonal imaginary
components do not need to be calculated. In addition, theentries above the main diagonal are the complex conjugatesof the entries below the main diagonal, which means that the
number of operations can be further reduced.
Due to their length and random nature, data signals ofdifferent users are likely to have very low correlation with eachother. For this reason, Θ is diagonal dominant. A sample of
the reduced-wordlength matrix Θ in a MMIMO with R = 128
and T = 16 is shown below. It is observed that its diagonal
elements are real-valued and positive; in comparison, thereal and imaginary components of off-diagonal elements are
signiﬁcantly smaller in magnitude. Statistics collected overone million samples of matrix Θ indicate that the largest
off-diagonal magnitude has an average of 49 and a standard
deviation of 6. This means that most off-diagonal values canbe represented with 7 bits (sign bit included). The overﬂowing
off-diagonal values will be saturated to ±63.
⎛⎞
289 5 + j 4 −11 − j 2
7 + j 6 ···⎜ 5−j 4244
3+j 1
6 − j 8 · · ·⎟⎜⎟⎜−11 + j 2 3 − j 1
211 −9 − j 13 · · ·⎟
Θ =⎜⎟⎜ 7−j 6 6+j 8−9 + j 13
208 · · ·⎟
⎝
⎠
..
..
..
..
..
.
.
.
.
.
(10)
It is apparent that a wordlength of 9 bits provides sufﬁcientprecision to store each element of the matrix Θ. For higher
quantization levels a longer wordlength may be required.
However, if needed, a reduction of the wordlength could
be performed in such cases by right shifting followed bytruncation or rounding.
The channel matrix, H, has R × T complex-valued elements. This translates into a storage requirement of 2 ×

1349

TTT

T

H

Fig. 1. Calculating Θ = H H· H.

Figure 1 shows the strategy of calculating Θ = H H· H.
The elements of each column of H are loaded into the FPGA
in parallel through separate I/O pins, and the columns areloaded sequentially. Column 1 of H (Row 1 of H H ) is ﬁrst
downloaded, allowing the inner product producing the top-leftelement, which is labeled 1, to be calculated. Column 2 ofH (Row 2 of H H ) is downloaded next, allowing the innerproducts producing elements labeled 2 to be calculated. Afterthe last column of H (last row of H H ) is downloaded, theinner products producing elements labeled T in the output
matrix can be calculated. It is observed that each of theelements in a row of Θ can be calculated in parallel. The
ability of exploiting this data-level parallelism depends on the
FPGA logic capacity.
The BRAM is a true dual-port memory, which means that
only 128
2 = 64 BRAM units are needed for each inner product.
This means that T · 64 = 16 · 64 = 1024 BRAMs are needed
for calculation of the entire matrix Θ. This hardware costis signiﬁcant. The number of BRAMs can be reduced by
calculating the ﬁrst eight columns of Θ in a ﬁrst phase, andthe last eight columns of Θ in a second phase. This techniquewill reduce the number of BRAM units to 1024
2 = 512, but will

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

require iterating through the last eight columns of H again.
The matrix’s upper triangular portion is the complex conjugateof the lower triangular portion, and thus computing its values
does not require additional operations.
Each inner product is implemented with a pipeline of threestages: (i) LUT plus BRAM, (ii) two 15-bit ripple-carryadders, and (iii) two 15-bit ripple-carry adders. There are
T = 16 rows, out of which 8 will be accessed twice,
as mentioned in the previous paragraph. This means that16 + 8 + (3 − 1) = 26 pipeline stages are needed to complete
the calculation of Θ. Additional stages will be needed byhousekeeping routines. The source code was implemented in
VHDL and Xilinx Design Constraints (XDC) were carefully
speciﬁed. Simulations carried out with Vivado [30] indicatethat matrix Θ can be calculated with a clock frequency of atleast 100 MHz and with a latency of 50 cycles. The hardware
utilization is 512 BRAMs, 23190 LUTs, and 8464 ﬂip-ﬂops.
B. Hessenberg Decomposition of Θ in Fixed-Point Arithmetic
It is well known that a Hessenberg form is preserved
during a similarity transformation [4]. For this reason, aconversion from a Hermitian form, Θ, to a Hessenberg form,
Ahess = Qhess · Θ · QH
hess , is beneﬁcial to be performed prior
to applying the Francis-Kublanovskaya recursion, since thiswill signiﬁcantly reduce the operation count [4]. It is observedthat the reduced off-diagonal wordlength is highly beneﬁcial
for the ﬁxed-point arithmetic since only off-diagonal elements
are rotated in the Hessenberg conversion. For example, theﬁrst-column vector v (diagonal element is excluded), wherev T = [5 − j 4 , −11 + j 2 , 7 − j 6 , . . . ], is converted into avector w, where wT = [59−j 62 , 0 , 0 , . . . ]. This vector still
has a small magnitude compared to diagonal elements, whichmeans that the Hessenberg form is also diagonal dominant.
Moreover, since the off-diagonal elements of a Hessenberg
form decrease in magnitude over Francis-Kublanovskaya iterations [13]–[15], there is no danger of overﬂow in any of
those elements. According to our simulations, a wordlengthof 8 bits can represent with sufﬁcient precision the unitarymatrices, Qhess , leading to Hessenberg and diagonal forms.
Calculating the Hessenberg / triagonal form of the Hermitian matrix Θ is performed with Householder reﬂectors. A
Householder reﬂector of a complex-valued column vectorz = [z1 , z2 , . . . ]T , where zi ∈ C, ∀i, is expressed in termsof a Hermitian unitary matrix, F , which is constructed as:
F = I −2
where:v · vH,vH · v(11)

root operation. Since these two operations are on the critical
path of the computation, it is critical to minimize their latencies. This will be addressed in the next two subsections.
C. Squared Euclidean Norm
To simplify the presentation, the Squared Euclidean Normof a K-element complex-valued vector X with elements
Xre,k + j Xim,k is written as the Squared Euclidean Norm ofa 2K-element real-valued vector with elements Xi :F · z = sgn(z1 ) z e1(13)

In the above equations it is apparent that a Householder
reﬂector relies on the squared Euclidean norm and the square

2K

Xi2

(14)

i=1

The two’s complement representation of each element Xi isgiven by:Xi = si xi,N −1 . . . xi,1 xi,0 =(15)= −2N r si + 2(N −1) r xi,N −1 · · · + 2r xi,1 + xi,0

where xi,N −1 , . . . , xi,1 , xi,0 are digits in a numeral systemof radix r and si is the sign bit. In coarsely quantized MMIMO
systems, the precision of the off-diagonal elements of matrix
Θ is on the order of a few bits. It is apparent that a two’s
complement representation with r = 3 (octal digits) and a
precision of N = 2 digits plus a sign bit (which corresponds
to a 7 bit two’s complement integer) is sufﬁcient to encode
those off-diagonal elements shown in Equation 10. Therefore,
the square of an element Xi is given by:
Xi2 = (−22·3 si + 23 xi,1 + xi,0 )2 = 212 si + 26 x2i,1+ x2i,0 − 210 si xi,1 − 27 si xi,0 + 24 xi,1 xi,0(16)

where, obviously, s2i = si . The last product, xi,1 xi,0 , can be
decomposed in a similar fashion. Assume that bi,02 , bi,01 , andbi,00 are the three bits of the octal digit xi,0 :
xi,0 = bi,02 bi,01 bi,00(17)

Then, one can write:
xi,1 xi,0 = 22 bi,02 xi,1 + 2 bi,01 xi,1 + bi,00 xi,1

(18)

It is observed that the negative terms in Equation (16) andall the terms in Equation (18) are products of an octal digit(xi,1 and xi,0 ) by a binary digit (si , bi,02 , bi,01 , and bi,00 ). The
importance of this representation will become apparent below.
The sum of squares in Equation (14) can be written as
2K

2K

Xi2 = 212− 210

si + 26

i=1

2K



2K
i=1



+ 25

x2i,1 +

  
Σ2

si xi,1 − 27

Σ4

2K
i=1

  
Σ1

i=1

A Householder reﬂector forces all vector elements with the
exception of the ﬁrst one to zero, as shown below.

2
2
Xre,k
+ Xim,k
=

k=1

i=1

(12)

K

X2 =

v = sgn(z1 ) z e1 − z
e1 = [1, 0, . . . ]Tz1(complex signum)
sgn(z1 ) =
|z1 |

1350


2K
i=1




Σ5

i=1

2K

bi,02 xi,1
i=1





x2i,0

  
Σ3

si xi,0 + 26

bi,01 xi,1 + 24

Σ7

2K




Σ6



2K

bi,00 xi,1
i=1




Σ8

(19)



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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

In a Householder reﬂector for matrix Θ, K ≤ T − 1. In
MMIMO standard conﬁguration T = 16; therefore, K ≤ 15.
The sum Σ1 (which is a 5-bit quantity) is calculated with aparallel counter in two stages. First, sums of six si bits arecalculated with three 6-input LUTs per sum, since each sucha sum is a 3-bit quantity; thus, no carry propagation occurs= 15 LUTs. In
and the hardware requirement is 3 × 2K6
= 5 resulting 3-bit sums can be
the second stage the 2K
6collapsed with two ternary ripple-carry adders [27] requiring
4 and 5 LUTs, respectively. According to our VHDL code
synthesized and simulated with Vivado [30], ternary adders are
instantiated out of behavioral code only if the bitwidth ≥ 7.
For bitwidth ≤ 6, ternary adders are instantiated by codingwith Xilinx primitives.
Sums Σ2 and Σ3 (11-bit quantities) have the same computational pattern. Σ2 , for example, can be expanded asKΣ2 =x2(2j−1),1 + x2(2j),1


Σ2,j

j=1 

(20)

Since x(2j−1),1 and x(2j),1 are octal digits, each sum Σ2,j(a 7-bit quantity) is a 6-input logic function and can be
implemented with seven 6-input LUTs (no carry propagation
occurs). 7 × K = 105 LUTs are needed in this stage. In thesecond stage K = 15 7-bit arguments are to be collapsed.
This is achieved by a tree of 5 + 1 + 1 = 7 ternary ripple-carryadders, which require up to 7 × 11 = 77 LUTs.
The sums Σ4 , Σ5 , Σ6 , Σ7 , and Σ8 are 8-bit quantities andhave the same computational pattern, which is sum of productsof an octal digit by a binary digit. Σ4 , for example, can be
expanded as
 2K
3 

Σ4 =

s3j−2 x(3j−2),1 + s3j−1 x(3j−1),1 + s3j x(3j),1



Σ4,j
(21)
It is apparent that each sum Σ4,j is a 5-bit value, and can be
regarded as having three octal arguments (x(3j−2),1 , x(3j−1),1 ,x(3j),1 ) and three mask bits (s3j−2 , s3j−1 , s3j ). It can be
implemented with a 4-bit ternary ripple-carry adder, that is,with a CARRY4 primitive and four 6-input LUTs in which
the mask bits will drive three out of those six inputs. 4 ×
2K= 40 LUTs are needed in this stage. In the second3= 10 four-bit arguments are to be collapsed. This
stage 2K
3
is achieved by a tree of 3 + 1 + 1 = 5 ternary ripple-carryadders, which requires up to 5 × 8 = 40 LUTs.
In the ﬁnal stage, sums Σ1 , Σ2 , Σ3 , Σ4 , Σ5 , Σ6 , Σ7 , andΣ8 are collapsed to give the squared Euclidean norm, X2
(a 16-bit quantity). This is achieved by a tree of 3 + 1 = 4
ternary ripple-carry adders, which requires up to 4 × 16 =64 LUTs. Long carry propagation over 12 bits, as it is apparent
in Equation (19), occurs only in this ﬁnal stage.
The source code was implemented in VHDL and Xilinx
Design Constraints (XDC) were carefully speciﬁed. Simulations carried out with Vivado [30] indicate that the squared
Euclidean norm can be calculated with a clock frequency of
100 MHz and with a latency of 5 cycles with timing slack
j=1

1351

to spare. The hardware utilization includes 1160 LUTs and235 ﬂip-ﬂops. This is a good result compared to the behavioral solution in implementing Equation (14) by collapsing
2K = 30 squared 6-bit operands, whose latency would beapproximately 50% larger that the proposed solution andwould require over 2000 LUTs.
D. Square Root Operation
A number of T − 2 = 14 Householder reﬂectors need
to be calculated for each matrix Θ. Since the square root
operation is on the critical path of the Householder reﬂector,
it is imperative to reduce its latency. Thus, recursive bit-level
solutions, such as the Convergence Computing Method [32],would be too slow for a MMIMO application. A parallel
implementation in ﬁxed-point / integer arithmetic is sought.
Statistics collected over one million samples of matrix
Θ and the resulting Hessenberg form indicate that thesum-of-squares of lower off-diagonal elements in a column
can be represented with a bitwidth of 16 bits or less. In integer
arithmetic, when only the integer bits are retained, the squareroot of 16-bit argument has a wordlength of 8 bits. In the mostgeneral case, the square root of an integer X has a integer part,Y , and a fractional part, {Y }:√X = Y = Y + {Y } , where {Y } ∈ [0, 1)(22)
In order to keep the truncation error in the range (−0.5, 0.5),
the square root implemented in integer arithmetic is approximated as:√Y
if {Y } < 0.5
X ≈(23)
Y + 1 if {Y } > 0.5
It is apparent that the fractional part, {Y }, can never be equal
to 0.5, since in that case the argument X, which is an integer,
would have a fractional part {X} = 0.25:√
X = Y = Y + 0.5
⇒

⇒
2

0.25
X = ( Y + 0.5) = Y 2 + Y + 
{X}

(24)

This fractional part is small. Therefore, with a very good
approximation:Y 2 + Y ≈ Y + 0.5(25)
As a result, Equation (23) can be replaced by Equation (26),which in turn can be more easily converted into LUT conﬁguration information. For example, consider the thresholdin the square root domain Y = 251.5 ⇒Y = 251.
This translates into a threshold in the argument domain of
Y 2 + Y =√63252. Then, the argument values X ≥ 63252
will map into √X = 252, and the argument values X < 63252
will map into X = 251. In a second example, the thresholdin the square root domain Y = 131.5 ⇒Y = 131translates into a threshold in the argument domain of Y 2 +Y = 17292.
Then, the argument values X ≥ 17292 will√
map into X = 132, and the argument values X < 17292

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021√
will map into X = 131. All the other thresholds will follow
a similar pattern.
√Y
if X < Y 2 + Y(26)
X ≈Y + 1 if X ≥ Y 2 + Y
In a brute force implementation, the integer part of thesquare root is retrieved from a large lookup table built with
BRAM units. Since the argument and square root are 16-bit
and 8-bit integers, respectively, the LUT size needs to be
216 bytes or 64 KB. Such a large LUT is implemented with
sixteen 32K × 1 BRAMs (two BRAMs per output bit). The
square root latency is one BRAM delay, thus it is very short,
but the hardware cost is large. A technique to reduce the LUT
size is presented next.
It is observed that Y 2 + Y is an even number. Thismeans that only 15 bits are in fact needed to form the LUT
address, and the square root approximation can be written as:√Y
if (X >> 1) < [( Y 2 + Y ) >> 1]
X ≈Y + 1 if (X >> 1) ≥ [( Y 2 + Y ) >> 1](27)
Consider the same example of the threshold in the square root
domain Y = 251.5 ⇒ Y = 251. By removing the leastsigniﬁcant bit, argument values(X >> 1) ≥ (63252 >>√1) = 31626 will map into X = 252,
√ and argument values(X >> 1) < 31626 will map into X = 251. This strategy
reduces the number of BRAMs from sixteen to eight, thus
halving the cost of the implementation.
In the described implementation, it is apparent that the
maximum rounding error is slightly above 0.5, since that small
fraction of 0.25 in Equation (24) is lost in approximating
X ≈ Y 2 + Y . In order to limit the maximum rounding
error to exactly 0.5, the approximation in Equation (26) needs
to be rewritten as:√Y
if X ≤ Y 2 + Y(28)
X ≈Y + 1 if X > Y 2 + Y
Considering the same numerical example, that means√ that theargument values X > 63252 will have to map into X = 252,
and√ the argument values X ≤ 63252 will have to map into
X = 251. In this case, the right shift operation needs to beaccompanied with rounding, such that 63253 >> 1 = 31627.
The implementation is shown in Figure 2. The latency of this
implementation is given by the delay of a 15-bit adder and a
BRAM delay, which is a very low. The hardware cost is eight
BRAMs plus a 15-bit adder.
X

16

15

15

Large
LUT
32K × 88√X

1 (LSB)

Fig. 2. Square root implemented with a large LUT.

The squared Euclidean norm and the square root, which
are on the critical path of the Householder reﬂector implementation, as it is apparent in Equations (11) and (12), areimplemented with LUTs and BRAMs. The application of a1352

Householder reﬂector, Fi · Θ, where i = 1, 2, . . . T − 2, andthe accumulation of the successive Householder reﬂectors into
a global unitary matrix, Qhess , is implemented with DSP slices.
E. Francis-Kublanovskaya Algorithm in Fixed-Point Arithmetic
The eigenvalue decomposition of matrix Θ is performed in
two steps: (i) initial transformation of Θ into a Hessenberg
matrix Ahess , and (ii) diagonalization of Ahess through theFrancis-Kublanovskaya (FK) recursion [13]–[15], in which
each iteration consists of a QR decomposition followed by areverse product. The QR decomposition is performed throughrotating the 2-element vertical vector in each column such thatthe off-diagonal element in that column is forced to zero. Thisrotation can also be implemented with a Householder reﬂector.
Since the FK algorithm can diagonalize the matrix Θ
directly, although with a much larger number of operations, thematrices Θ and Ahess are used interchangeably to simplify theremaining part of the presentation. At iteration k, the matrix
Θ(k) (where Θ(0) = Θ) is decomposed into the product of aunitary matrix Q(k) and an upper triangular matrix R(k) . By
multiplying R(k) by Q(k) , a similarity transformation of Θ(k)is obtained, as shown in Equation 29.
Θ(k+1) = R(k) · Q(k) = Q(k) H · Θ(k) · Q(k)

(29)

(k)

If k → ∞, then the matrix Θ
tends to a diagonal matrix,
Λ, whose elements are the eigenvalues of the initial Θ:Θ = Q(n) H · . . . · Q(k) H · . . . · Q(0) H



QH
·

·

Λ

·

Q(0) · . . . · Q(k) · . . . · Q(n) (30)



Q

The off-diagonal elements of matrix Θ (or the Hessenberg
form Ahess ) decrease in magnitude over FK iterations. As aresult, there is no danger of overﬂow in any of those elements,
and the wordlength needed to represent diagonal elements
would sufﬁce for representing the off-diagonal elements, too.
The unitary matrix Q of the eigenvalue decomposition of
the Hermitian matrix Θ, and all unitary matrices Q(k) of
the QR decompositions, are essentially rotation matrices. As
a result, all their elements range between −1.0 and +1.0
(thus, there is no danger of encountering an overﬂow). In our
ﬁxed-point arithmetic implementation, a scale factor equal to2qwl−1 is embedded into the matrix Q, so that its wordlengthis (qwl − 1) magnitude bits + 1 sign bit = qwl bits. The
matrices Θ and Λ are already in ﬁxed-point representation,
their wordlengths being equal, θwl = λwl. As a result, theeigenvalue decomposition can be written as:
2qwl−1 Q
2qwl−1 QH·Λ·⇒Θ = Q·Λ·QH ⇒ Θ =qwl−1
2
2qwl−1⇒ 22·(qwl−1) Θ = 2qwl−1 Q · Λ · 2qwl−1 QH  
Q
QH(31)
where Q and QH is the unitary matrix and its Hermitian transpose in ﬁxed-point representation (that is, with the scale factor

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

embedded). Similarly, Θ(k) is the ﬁxed-point representation of
Θ(k) , and an FK iteration in the ﬁxed-point domain can be
written as:Θ(k) = Q(k) · R(k) ⇒ Θ(k) = Q(k) · R(k)⇒
Θ(k+1) = R(k) · Q(k)⇒

Θ(k+1) = Θ(k+1) >> (qwl − 1)(32)

It is mentioned that the last right shift operation may be
performed with or without rounding.
The convergence of the Francis-Kublanovskaya algorithm
can be signiﬁcantly improved by introducing shifts in the
diagonal values of matrices Θ(k) [4]. There are three common
shifts used in FK algorithms: (i) the Rayleigh quotient shift,which is equal to the last diagonal element; (ii) the Wilkinson
shift, which is equal to the eigenvalue of the lower rightmost
2-by-2 sub-matrix of Θ(k) ; and (iii) the Francis double shift,which applies a complex conjugate pair of shifts. In our
simulations, the Rayleigh quotient shift has been used due
to its implementation simplicity.
A challenge of designing a MMIMO base station is tominimize the wordlengths θwl and qwl, while the bit-error
rate is maintained at acceptable levels. As mentioned in previous paragraphs, our simulation software using ﬂoating-point
arithmetic used in prior work [9] has been converted to use
ﬁxed-point arithmetic. To estimate the transmitted vector X,
the linear system shown in Equation (1) is solved through aneigenvalue decomposition rather than the MATLAB® backslash operator. The wordlength of matrix Q was set to qwl = 6
and qwl = 7 bits, and the wordlength of matrix Θ was
set to θwl = 9, θwl = 10, and θwl = 11. The numberof quantization bits were set to B = 1, B = 2, and thenB = 3. Extensive simulation runs were attempted, and BER
performance ﬁgures were recorded.
In the system we have evaluated, each user independently
transmits a single-carrier signal with a modulation of order
4 QPSK using 256-symbol blocks with a cyclic preﬁx of
16 samples. An error correction code consisting of a rate 21
convolutional code applied over 32 single-carrier blocks isused. The simulated random multipath fading channel had
a maximum delay of 9 symbol periods. The channel stateis assumed to be stable over each set of 32 single-carrier
blocks, where for each set the user transmits 2 pilots per
block with content known a priori at the receiver which is used
for initial channel estimation. A joint channel estimation/datadetection/data decoding algorithm as described in [9] is used
to provide the ﬁnal result.
The BER ﬁgures versus the wordlength for implementing
the eigenvalue decomposition in ﬁxed-point arithmetic with
reduced precision are presented in Table III. For reference, theBER ﬁgures corresponding to a double-precision ﬂoating-point
implementation are also provided. It is apparent that for acoarse quantization with B = 1 bit, neither the ﬂoating-point
nor the ﬁxed-point implementations achieve a satisfactory
BER in a small number of iterations. For a coarse quantization
with B = 2 or B = 3, three to four iterations are sufﬁcientto achieve a good BER. It is observed that an additional

1353

iteration is generally needed in ﬁxed-point implementation
over the ﬂoating-point implementation to match their BERs;
for example, BER(B = 2, qwl = 6, θwl = 9, i = 3) = 0.0259(ﬁxed-point), whereas BER(B = 2, qwl = 64, θwl = 64, i =2) = 0.0236 (ﬂoating-point).
TABLE III
BER VERSUS WORDLENGTH FOR R = 128, T = 16, Eb /N0 = 5 dB
( ONLY THE FIRST THREE ITERATIONS ARE SHOWN ).
B

1

2

3

Wordlength
Iteration
qwl
θwl
i=1
i=2
i=3
Double-Precision Floating-Point Arithmetic
64
640.45810.32350.2116
Fixed-Point Arithmetic
690.47170.39900.26706100.47130.34430.25036
110.4706
0.34610.2482790.47050.39300.26667
100.47410.34420.25167
110.47230.35200.2539Double-Precision Floating-Point Arithmetic
64
640.28540.02360.0078Fixed-Point Arithmetic
690.32840.08230.02596
100.32250.03950.01126
110.32030.03110.01257
90.32770.07940.03037
100.32450.03990.01077
110.3206
0.03150.0106
Double-Precision Floating-Point Arithmetic
64
640.18490.03960.0066
Fixed-Point Arithmetic
690.23800.03790.00916100.22650.03890.00896
110.22260.04530.01047
90.23420.03540.00837100.22550.03710.00907110.2201
0.04360.0119It is apparent that for a quantization with B = 2 bits, thematrix Q’s wordlength, qwl, has little impact on BER. As an
example, BER(B = 2, qwl = 6, θwl = 9, i = 2) = 0.0823,
and BER(B = 2, qwl = 7, θwl = 9, i = 2) = 0.0794. On
the other hand, the matrix Θ’s wordlength, θwl, has a muchstronger impact on BER. For example, BER(B = 2, qwl =6, θwl = 10, i = 2) = 0.0395, which a signiﬁcant decrease
from 0.0823. It should be observed that the BER does not
improve signiﬁcantly for θwl ≥ 11. A MMIMO system with
B = 3 is robust enough to operate with θwl = 9, but thecost of the implementation will be higher due to the increased
complexity of the implementation in ﬁxed-point arithmetic.
Based on these considerations, it is apparent that a goodtrade-off in implementing a MMIMO base station is a coarse
quantization with B = 2 bits, a Q’s wordlength qwl = 7 bits,
and a Θ’s wordlength θwl = 10 bits. With such a reduced qwl,
vector rotations can be implemented in inexpensive hardware,such as an array of reasonably sized FPGAs. A reduced θwlallows the extensive use of FPGA Digital-Signal Processing
slices to perform the reverse products in the FK recursion, as
well as complete the calculation of the solution of the linear
system shown in Equation (1) through matrix multiplication,as outlined in Equation (4). Previous works are in line withthese claims [16].

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

V. C ONCLUSIONSFor 2-bit quantization (B = 2), an additional 0.85 dB ofsignal power is required to match the performance of ideal
non-quantized measurements. The eigenvalue decomposition,
the most computationally demanding portion of a MMIMO
receiver algorithm, can be implemented in ﬁxed-point arithmetic with wordlengths of 7 and 10 bits for eigenvectors andeigenvalues, respectively, at the expense of a small degradationof the Bit-Error Rate achieved in a double-precision ﬂoating-point implementation. An inexpensive set of reasonably
sized FPGAs further reduce the cost of implementing massive
MIMO base stations, which is a key impediment to theirwidespread deployment. In future work, the wordlength of theﬁxed-point implementation will be assessed in the presence of
signal contamination from adjacent networks.
R EFERENCES[1] C. Mollén, “High-End Performance with Low-End Hardware. Analysis of Massive MIMO Base Station Transceivers,” Ph.D. dissertation,
Linköping University, Linköping, Sweden, 2018.
[2] C. Mollén, J. Choi, E. G. Larsson, and J. Robert W. Heath, “Uplink
Performance of Wideband Massive MIMO with One-Bit ADCs,” IEEE
Transactions on Wireless Communications, vol. 16, no. 1, pp. 87–100,
January 2017.
[3] ——, “Achievable Uplink Rates for Massive MIMO with Coarse Quantization,” in Proceedings of the 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2017). New Orleans,Louisiana: IEEE, March 2017, pp. 6488–6492.
[4] G. H. Golub and C. F. van Loan, Matrix Computations, 3rd ed. The
Johns Hopkins University Press, 1996.
[5] B. Parhami, Computer Arithmetic: Algorithms and Hardware Designs.
Oxford University Press, 2000.
[6] M. Ercegovac and T. Lang, Digital Arithmetic, Morgan Kaufmann, 2003.
[7] A. Chockalingam and B. S. Rajan, Large MIMO Systems. Cambridge
University Press, 2014.
[8] M. K. Ozdemir and H. Arslan, “Channel Estimation for Wireless OFDM
systems,” IEEE Communications Surveys and Tutorials, vol. 9, no. 2,
pp. 18–48, July 2007.
[9] Z. Zhang, M. McGuire, and M. Sima, “Iterative Channel Estimation forLarge Scale MIMO with Highly Quantized Measurements in 5G,” in The
28th European Signal Processing Conference (EUSIPCO 2020). Amsterdam, The Netherlands: European Association for Signal Processing(EURASIP), January 2021, pp. 1643–1647.
[10] IEEE Standard for Floating-Point Arithmetic. IEEE STD 754-2019,
IEEE Computer Society, July 2019.
[11] J. M. Mendel, Lessons in Estimation Theory for Signal Processing,Communications, and Control, 2nd ed. Prentice-Hall, 1995.
[12] T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed.
Wiley-Interscience, 2006.
[13] J. Francis, “The QR transformation – Part 1,” The Computer Journal,
vol. 4, no. 3, pp. 265–272, October 1961.

1354[14] ——, “The QR transformation – Part 2,” The Computer Journal, vol. 4,
no. 4, pp. 332–345, January 1962.
[15] V. N. Kublanovskaya, “On some algorithms for the solution of thecomplete eigenvalue problem,” USSR Comput. Math. and Math. Physics,
no. 3, pp. 637–657, March 1961.
[16] G. Xu, Y. Li, J. Yuan, R. Monroe, S. Rajagopal, S. Ramakrishna, Y. H.
Nam, J.-Y. Seol, J. Kim, M. M. U. Gul, A. Aziz, and J. Zhang, “Full Dimension MIMO (FD-MIMO): Demonstrating Commercial Feasibility,”IEEE Journal on Selected Areas in Communications, vol. 35, no. 8, pp.
1876–1886, August 2017.
[17] A. Molisch, Wireless Communications, 2nd ed. West Sussex, United
Kingdom: John Wiley & Sons, 2011.
[18] A. Chockalingam and B. Sundar Rajan, Large MIMO Systems. Cambridge, United Kingdom: Cambridge University Press, 2014.
[19] A. Zaidi, F. Athley, J. Medbo, U. Gustavsson, G. Durisi, and X. Chen,
5G Physical Layer: Principles, Models and Technology Components.
Academic Press, 2018.
[20] M. Ozdemir and H. Arslan, “Channel estimation for wireless OFDM
systems,” IEEE Communications Surveys Tutorials, vol. 9, no. 2, pp.
18–48, 2007.
[21] J. Robert F. Jones and J. Earl E. Swartzlander, “Parallel Counter
Implementation,” Journal of VLSI Signal Processing Systems for Signal,
Image and Video Technology, vol. 7, no. 3, pp. 223–232, October 1994.
[22] H. Parandeh-Afshar, P. Brisk, and P. Ienne, “Efﬁcient Synthesis ofCompressor Trees on FPGAs,” in Proceedings of the 13th Asia andSouth Paciﬁc Design Automation Conference (ASP-DAC 2008), Seoul,
Korea, March 2008, pp. 138–143.
[23] ——, “Exploiting Fast Carry-Chains of FPGAs for Designing Compressor Trees,” in Proceedings of the 19th International Conference on
Field Programmable Logic and Applications (FPL’09), Prague, Czech
Republic, August-September 2009, pp. 242–249.
[24] T. Matsunaga, S. Kimura, and Y. Matsunaga, “Multi-Operand Adder
Synthesis on FPGAs using Generalized Parallel Counters,” in Proceedings of the 15th Asia and South Paciﬁc Design Automation Conference(ASP-DAC 2010), Taiwan, January 2010, pp. 337–342.
[25] H. Parandeh-Afshar, A. Neogy, P. Brisk, and P. Ienne, “Compressor
Tree Synthesis on Commercial High-Performance FPGAs,” ACM Transactions on Reconﬁgurable Technology and Systems, vol. 4, no. 4, pp.
39:1–39:19, December 2011.
[26] B. Khurshid and R. N. Mir, “High Efﬁciency Generalized ParallelCounters for Look-Up Table Based FPGAs,” International Journal of
Reconﬁgurable Computing, vol. 2015, September 2015.
[27] J. M. Simkins and B. D. Philofsky, “Structures and Methods for Implementing Ternary Adders / Subtractors in Programmable Logic Devices,”
U.S. Patent 7,274,211 B1, September 2007.
[28] Xilinx, Inc., “All Programmable 7 Series – Product Selection Guide,”
User Guide XMP101, v1.7, February 2018.
[29] Xilinx, Inc., “7 Series FPGAs Memory Resources: User Guide,” User
Guide UG473, v1.13, February 2019.
[30] Xilinx, Inc., “Vivado Design Suite – HLx Editions,” ver. 2018.3, 2018.
[31] M. Tian, M. Sima, and M. Michael McGuire, “Massive MIMO in
Fixed–Point Arithmetic,” in Proceedings of the 23rd International
Conference on Advanced Communications Technology (ICACT 2021),
PyeongChang, Korea, February 2021, pp. 91–95.
[32] Chen TC (1972) Automatic Computation of Exponentials, Logarithms,
Ratios, and Square Roots. IBM Journal of Research and Development
16(4):380–388

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1355

Mi Tian was born in the People’s Republicof China in 1988 and received her B.S. degree
in optical communications engineering from Jilin
University, Changchun, Jilin, China, in 2010, her
M.S. degree in analogue and digital integrated circuitdesign from Imperial College London, U.K., in2011, and her Ph.D. degree from the University ofVictoria, British Columbia, Canada, in 2021. She iscurrently a postdoctoral fellow in the Department
of Electrical and Computer Engineering at the same
institution. Her research interests include wirelesscommunication systems, hardware implementation on FPGAs of massive
MIMO communication systems.
Mihai Sima (S’00–M’04) was born in Romania in
1964 and received his B.Eng. degree in electronics
engineering from Polytechnic Institute of Bucharest,
Romania in 1989, and his Ph.D. degree in computerengineering from Delft University of Technology,
The Netherlands, in 2004. Since 2003, he has been afaculty member in the Department of Electrical andComputer Engineering at the University of Victoria,
British Columbia, Canada. His research interests
include computer architecture and engineering,reconﬁgurable computing, embedded systems,
circuit design, and hardware security.
Michael McGuire (S’95–M’97) was born in Canada
in 1970 and received his B.Eng. in computerengineering from the University of Victoria, British
Columbia, Canada, in 1995, his M.A.Sc. in electricalengineering from the same institution in 1997, and
his Ph.D. from the University of Toronto in 2003.
Since 2003, he has been a faculty member in the
Department of Electrical and Computer Engineering
at the University of Victoria. Dr. McGuire’s researcharea is in the application of signal processing
to communications, developing new techniques forFaster-than-Nyquist signaling, iterative channel estimation/data detection, andwireless radio localization suitable for advanced wireless systems.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1356

Automatic Vocabulary Grouping and Deep
Combination for News Credibility andReliability Evaluation Corresponding to Specific
Language
Ming-Shen Jian*, Rong-Bin Deng**, Chen-Wei Fang***, Hua-Yu Wu****, Wen-Hsiang Hsieh*****
Cloud Computing and Intelligent System Lab., Dept. of CSIE, National Formosa University
Yunlin County, Taiwan 632
jianms@nfu.edu.tw, 40743162@gm.nfu.edu.tw, 40743206@gm.nfu.edu.tw, 40743212@gm.nfu.edu.tw,40743160@gm.nfu.edu.tw
Abstract—Considering the spread of the intentional false
news, to be aware of the new news is important. In this research,
the Automatic Deep Vocabulary Grouping and Combination
for News Credibility and Reliability Evaluation is proposed
which includes Key Vocabularies Merging Method and False
News Warning Method. By merging different algorithms, theproposed system could cluster and group the fake news
according to the found features of various fake news content.
The proposed system could find and verify the features of thespecific intentional false news according to the proposed deep
combination evaluation function. After suitable verification andrecursive checking, the minimum groups for clustering the fakenews could be given. Based on the collected features of thevarious intentional false news, the new coming news can beclassified as the false news due to the feature matching
percentage. According to the verification, all the collected andproved fake news could be found and clustered into thecorresponding fake news groups.
Keyword—False News, Cluster, Web Crawler, Cloud
Computing———————————————————————
Manuscript received August 23, 2021 ", and a follow-up of the invitedjournal to the accepted & presented paper entitled " Automatic VocabularyGrouping and Deep Combination for News Credibility and Reliability
Evaluation Corresponding to Specific Language " of the 21th International
Conference on Advanced Communication Technology (ICACT2021)." This
work is Final File 20210125_finalpaper.pdf and was supported in part by the
Cloud Computing and Intelligent System Lab., Dept. of CSIE, National
Formosa University.
Ming-Shen Jian is with the Cloud Computing and Intelligent System Lab.,
Dept. of CSIE, National Formosa University- Taiwan (corresponding author
to provide phone: 886-922-916612; fax: 886-563-60306; e-mail:jianms@nfu.edu.tw).
Rong-Bin Deng is with the Cloud Computing and Intelligent System Lab.,
Dept. of CSIE, National Formosa University- Taiwan
Chen-Wei Fang is with the Cloud Computing and Intelligent System Lab.,
Dept. of CSIE, National Formosa University- Taiwan
Hua-Yu Wu is with the Cloud Computing and Intelligent System Lab.,
Dept. of CSIE, National Formosa University- Taiwan
Wen-Hsiang Hsieh is with the Cloud Computing and Intelligent System
Lab., Dept. of CSIE, National Formosa University- Taiwan

I. INTRODUCTIOND

UE to the online community or social media, various
information can be exchanged quickly. Without
verification, all the information broadcasted between people
may be under misrepresentation [1]. Considering theanonymous using or none-recognition social media user,
false news or fictitious information could be made for
specific purpose. The false news could spread faster than thetrue story or clarification news. Although most news can besearched and verified through the search engine such as
Google [2], Yahoo [3], etc., to find the correct informationfrom various sources and various news is still difficult.
There were different methods proposed to detect the false
news based on the supervision [4]. To find and detect thefalse new, the content of the news could be the importantfactor [5]. In addition, to take the background or profiles of
the user into consider could be also the solution for false
news finding [6]. Based on the crowd-sourcing approach, thefalse news could be verified according to the publicannotations or replies. However, when the false news areintentionally given or spread, public news readers may not
differentiate true or false news [7]. Furthermore, if the false
news is written maliciously, it's more difficult to classify thenews due to some organizations or groups would
intentionally spread the false news.
In addition, some languages would make the verification ofnews more difficult. For example, the possible combinationof Chinese vocabularies would make the meanings
completely different [11]. Currently, by using the specifickeywords, the search engines would find the possible
information for verification according to the statics of
keywords. Hence, considering the probability of the
keywords included in the found information or web pages,
different keywords could be combined as the new potential
vocabularies [8-10]. In other words, by adaptively combining
the key words, the features of the news can be described anddefined.
Considering the rapidly changing of the online news andinformation spread through the online media or social
community, the feature keywords or vocabularies arechanged quickly. Furthermore, the content of false news
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

could be various and huge. To continuously collect the datafrom social media is needed. In addition, to manage big datafrom the online media is also required. Corresponding to thebig data management, the Hadoop/MapReduce[10,15,19,20]
and Spark were proposed [25]. All the vocabularies aretreated as the key. Then, the total among of the keys
(vocabularies) appeared in the article or web page can beevaluated and counted as the value. Hence, each online article
or news can be represented as (key, value) corresponding to
all the vocabularies appeared.
According to the proposed research, by adaptively
combining the found vocabularies, the feature of the similar
online information can be found [10]. However, different
false news would be various in different content. Some false
news may appear only few hours, some may cause several
days spreading with huge data. Therefore, to collect the hugedata online for feature of the intentionally spread false news
is an important issue.
In this research, the first step is to collect the proved false
news from the third party impartial agency. To collect theverified data or information, the automatic data collection
based on the online crawler is needed. Corresponding to the
vocabulary combination procedure, the suitable length of
keywords for the same false news could be found. In otherwords, the suitable features of the same feature can be found.
By comparing different types of false news and theevaluation based on the dynamic multiple dimension K-mean
algorithm, the features or groups which intentionally spreadthe false news could be found.
This paper is presented as follows. Section 2 gives therelated works. Then, the proposed procedure for news
credibility and reliability prediction is given in section 3.
Section 4 provides the conclusion.

II. RELATED WORKS
An easy way to comply with the conference paper
formatting requirements is to use this document as a templateand simply type your text into it.
These days, dur to the online media including video
streaming, social media, or online forum, people could
receive various information more quickly. However, someintentional false news would cause damage from the health tothe vote. Some research were proposed to differentiate thetrue and false news. In addition, corresponding to the various
languages, the complete vocabulary in the sentence or article
should be taken into consideration [10]. There were
procedures proposed based on the language corpus [9,11,12].
According to the vocabularies stored in the corpus, thevarious vocabularies could be identified and recorded as the
text-pattern. However, the information or new vocabulary
comes every day. To automatically collect the information
online is important.
Hence, in this research, the web crawler [13,14,20] is used
to collect the online information included in the web pages.
Based on the web crawler, the application could continuously
collect the data from the Internet. From different searchengines or web pages, the information or potential news canbe gathered.
After obtaining the data from the Internet by crawler, thedata or vocabularies should be evaluated. Since the total

1357

amount of the collected data could be huge, to parallel
process the big data based on the distributed system is needed.
Hadoop/MapReduc was the data analysis application
proposed by Apache or Google [10,15,19,20]. Since the data
size from the Internet could be huge, the Hadoop Distributed
File System or Google File System was proposed for dividing
the huge data into blocks and distributed storing [16-19]. All
the corresponding information about data storage can be
managed based on the HBase, the database based on thecloud environment. The HBase is the noSQL type database.
Hence, the length of each data can be various instead of the
traditional fixed data length in database. In other words,
although the size of information or data from different
network sources could be different, the HBase on cloud could
still provide the storage space and record it with better
performance.
Suppose that to analyze the complete collected data iscalled Job. Then, the data will be divided into several
partitions for parallel processing, called Tasks. Each job
could be divided into multiple tasks. These tasks are mapped
to multiple computing nodes or virtual machines [26]. Afterthe computing and evaluation, each report of the task fromthe computing node could be sent back and summarized.
Based on the Hadoop/MapReduce, each found vocabulary
from the collected information or data will be represented as'key'. The total amount of occurrences corresponding to this'key' vocabulary can be counted and represented as the 'value'.
In other words, the report related to the data after analysis
could be represented as the set: {key, value}. Finally,
according to the reduce procedure, the value with the same
key will be added up. At last, only one report corresponding
to each information collected from Internet could be given.
Since the content of the co0llected information from theInternet could be various, the data size after analysis would
be different and huge. In addition, to execute theHadoop/MapReduce program also requires huge computing
resource. Hence, considering the computing ability, the cloudcomputing environment is needed. Via network connection
and virtualization technology, the virtualized computing
resource such as CPU, memory, and storage space, could bemerged into the computing resource pool. When a user needs
the computing resource, the computing resource in the
resource pool can be integrated as the virtual machine for theuser. If no more using the virtual machine, the computing
resource can be returned to the resource pool for further using.
Therefore, the computing resource for services such as IaaS,
PaaS, and SaaS, can be rapidly used [27]. In other words, if
the configuration and environment of the virtual machine ison demand defined, the user could obtain the cloud services
such as SaaS more directly and quickly. Therefore, the cloudcomputing could be the solution to repeatedly andcontinuously analyze the information gathered from theInternet.
After analysis related to the collected information, thefeature of the content should be found. In addition, there arevarious news, information, and data from Internet. The
features between different information or data could be
different. Therefore, to group the similar information or datafrom these collected data is important. Then, the possible
features of this grouped data such as the false news makers or
spreaders could be found. To find the possible relationship
Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

between different data, the clustering algorithms based on
K-means were proposed. According to the group analysis
with vector quantization, different groups with different
features could be found and divided by the following
equation:ൌ σൌͳ σ  ‫  א‬ሺ െ Ɋ ሻʹ

Start

Web Crawler
Proved False News

(1)

Hadoop/MapReduce for Big
Data

Suppose that there are total k randomly selected group
centres from P1 to Pk . The all found features x are measured
the distance between x and group centres. Finally, all the xwhich is included in the group Si with the shortest distance
between x and Pi are called members of group i. In otherwords, when the minimized J is found, all the x could be
individually classified to its nearest group with the centre ɊǤVocabulary/Seg
ment

Big Data in
HBase

Key Vocabularies Merging Method

Merge

III. PROPOSED SYSTEM
In this research, to continuously collect the proved news,
especially proved false news, is very important for training
the proposed system. Therefore, some specific online
websites or news proof data sources are on demand recordedin the proposed web crawler procedure which is based on the
HTTP protocol [20,21]. By using the selenium [22], Google
search Engine [2], or Taiwan FactCheck Center [28], thecorresponding news (proved false news) could be collected.
Then, the false news spread through the websites, social
media, forum, etc., could be further collected. In other words,
the false spreading paths, media, content, etc., can besearched and found.
The false news studied in this research is spreading in
Chinese language. Due to the characteristics of the Chinese,the vocabularies in the content should be divided according to
the ambiguity resolution rules such as Maximum Matching[23] (mmsegj). Each chunk which includes the neighbour
vocabularies would follow the rules: largest average andsmallest variance of word length, largest degree of thesummation related to morphemic freedom corresponding tothe one-character word, and maximum matching [24].
Therefore, each independent data or information from web
page or social media could be divided into multiple key
vocabularies.
However, to find the key vocabularies, the huge data fromthe Internet should be managed. Hence, in this research, theHadoop/MapReduce procedure is implemented based on thecloud
environment.
ByloopedmmsegjandHadoop/MapReduce procedures, the largest average lengthof key vocabularies could be found. However, considering
the intentionally spread false news today, new vocabularies,
names, or new homophonic or metaphor vocabularies would
appear. Therefore, in this research, the Key Vocabularies
Merging method is proposed. By sorting, grouping andcombining different found vocabularies according to the on
demand defined limitations and considerations, the key
vocabularies could be deeply searched through theverification via search engines. The flowchart can be shownas follows.

1358

No

Yes

Yes

New Vocabulary/Segment
ExistedNo
False News Warning Method

K-means
GroupedFeatures of
False News

End
Fig. 1. The flowchart of the proved false news system training.

In this research, the proved false news in Chinese language
could be collected from Taiwan FactCheck Center [28].
Since the news was already proved as the false news, thecorresponding data or information could be the training dataof the proposed system. According to the mmsegj andHadoop/MapReduce procedures, the largest average lengthof vocabularies or frequently appeared specific vocabularies
could be found and sorted. Then, the clustered and foundvocabularies should be proved and verified as the features ofthe specific false news. In this research, the similar content of
the proved false news from different news sources could becollected and analysed together. Therefore, these similarintentionally spread false news could be used to train the
proposed system and cluster the possible features of thecollected false news. To find the available and possible
features of the similar false news, the Key Vocabularies
Merging Method is proposed.
A. Key Vocabularies Merging Method
In this research, the Key Vocabularies Merging Method is
proposed for extending the found key vocabularies. Based on
the mmsegj algorithm for Chinese vocabulary recognition,
the basic segments or simple vocabularies can be found.
Considering the intentionally spread false news, the feature
vocabularies may be combined from various foundvocabularies. For example, the vocabulary could be
combined from the name, location, food, even foreign
language. Hence, in this research, the Key Vocabularies
Merging Method is proposed for further extended key
vocabulary searching.
After analysing the similar proved false news, the initial
key vocabularies which represented and sorted according tovalue of the set, {key, value}, can be obtained. Then, supposethat there are total v found initial vocabularies or segments
according to the mmsegj algorithm. The occurrences of the ith

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

vocabulary which represented as vi can be shown as Oc(vi) =value. Hence, the Key Vocabularies Merging could evaluate
and decide to merge two vocabularies by following thedecision function based on difference percentage shown as
follows:
ห ሺ  ሻെ ൫  ൯ห ൑ Ƚ
(2) ൌ ൝ ሺ  ሻ 
where  ്  and Ƚ is the on demand given value. If theresult of the merge evaluation is true, the ith vocabulary andthe jth vocabulary will be merged as the new vocabulary.
If there are new vocabulary or segment merged according
to the proposed decision function, these new merged
vocabulary or segment should be verified by the following
two checking steps. First step, these new vocabulary will betested through the search engines online including different
language vocabulary checking. If the new merged vocabulary
is existed in the general articles, then this new merged
vocabulary should occur frequently. Second step, the newmerged vocabulary should also occur in the all original
proved similar false news. In other words, the occurrence ofthe new merged vocabulary should be satisfied the function
shown as follows: ሺ ሻ ൒  ሺ ሻ ൒  ൫ ൯ (3)where the  ሺ ሻ ൒  ൫ ൯ . If these two steps can besatisfied, this new merged vocabulary or segment can berecorded. To find the longest feature vocabulary or segment,
the found merged vocabulary and segment will be checkedaccording to the proposed Key Vocabularies Merging
Method again. When no new merged vocabulary can satisfy
the two checking steps, the Key Vocabularies Merging
Method stops.
New Merged vocabulary/
Key Vocabularies
Merging Method Stop

Merging
vocabulary

Initial Key
Vocabulary
Two checking
steps
Fig. 2. The cycle of the proposed Key Vocabularies Merging Method.

Therefore, based on the proposed Key Vocabularies
Merging Method, the key vocabulary can be extended.
Suppose that the false news intentionally broadcasted would
be written corresponding to the specific person, specific fakenews or sentences, specific organizations, etc. Then, theprobabilities of individual word or vocabularies should bevery closer to another frequently appearing word or
vocabulary. For example, the personal name "杻⚳䐄" inChinese could be divided as two vocabularies: "杻⚳" and "䐄". The first vocabulary is the proper nouns. However,
initially the complete personal name is not defined as theproper nouns in the database. Therefore, after several cyclesof the proposed Key Vocabularies Merging Method, these
two independent vocabularies, "杻⚳" and "䐄", could befound that the probability of appearance corresponding to1359

these two vocabularies are almost the same since originally it
is a personal name. According to Key Vocabularies Merging
Method, the key vocabulary "杻⚳" can be extended withanother key vocabulary " 䐄 " as " 杻 ⚳ 䐄 ". Especially,
according to two checking steps of Key Vocabularies
Merging Method, the new found key vocabulary can beproved as the new merged and useful key vocabulary as thefeature of the false news. In addition, the new merged key
vocabulary can be recorded into the Chinese proper nouns
database.
Since it is difficult to find the new potential vocabularies
excluding in the database, in this research, additional method:jieba algorithm, is used for extending the vocabularies. Jieba
algorithm is used to find the possible and potential
vocabulary segmentation of Chinese language [29].
Jieba algorithm is the method to find the vocabulary
segmentation in Chinese [29]. In this research, to find thepossible segment of Chinese words, two possible methods areused: 1) Rule Segmentation and 2) Statistical Segmentation.
Based on jieba algorithm, a vocabulary dictionary tree
would be used for words comparing and matching by rulesegmentation. In other words, if the segments are alreadyrecorded in the dictionary, the results of matching could
identify the found segments. Considering the vocabularies
used in the social community media, new created words are
frequently appeared. It means that only matching the
vocabulary tree by rule segmentation is not enough.
Therefore, the Statistical Segmentation is also included forthe new vocabularies or segments finding. By counting theappearance frequency of the potential new vocabularies, thepotential new created vocabularies could be found due to thefrequent using in the text content from various sources. In
other words, with the statistical results, the high frequently
appeared words could be identified and recognized as the
new vocabulary. The new created new vocabulary will berecorded into the database for matching by Rule
Segmentation in the future. Hence, the new createdvocabularies used in the fake news or social media could befound and extended more automatically. In this research, theMapReduce for evaluating the various key segments is used.
Since the fake news spread today would include the personalnames or some homophonic vocabularies created by social
media, the found potential new vocabularies would bevarious with huge amount. Therefore, to evaluate and obtainthe statistical results of different text content from various
sources would request huge computing resource. In thisresearch, by following the MapReduce procedure, thestatistical results could be obtained through counting the{Key, Value} format which Key is the potential vocabulary
and the Value indicate the appearance times.
In addition to the Statistical Segmentation, the directed
acyclic graph is used to structure the possible vocabularies
according to the word graph scanning based on the prefix
dictionary. The maximum probability of the dynamic
programming path could be found. By using the Hidden
Markov Models (HMM), the words excluded in the
dictionary database could be learned and found. Hence, eventhe new words or vocabularies are created according to the
pronounce or other non-traditional grammar method, thestatistical results of the similar content from various news
sources could still be used to identify and recognize the new
Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

feature vocabularies.
After recursively processing the proposed Key
Vocabularies Merging Method and the jieba algorithm withHidden Markov Models (HMM), the hidden vocabularies
and txt segmentations could be found and identified. The
possible feature key vocabularies could be merged andextended by different segments.
B. Clustering Method
After finding the vocabularies and counting the appearanceof each vocabulary, the features of the specific fake news
should be found and defined. Since the similar fake news
would have similar key vocabularies, these vocabularies
could be used to cluster or group the fake news. In thisresearch, term frequency-inverse document frequency(TF-IDF) method is used. Term frequency-inverse document
frequency is a statistical method for information explorationand text mining through the weighting techniques [30,31].
Each found vocabulary will be evaluated for its importance ofthe text content or corpus. Suppose that the term frequency(TF) of the key vocabulary is high if this specific vocabulary
is important in the fake news. In other words, if the words are
not important in these fake news, the appearance frequencyof the words is decrease in inverse proportion called inverse
document frequency (IDF).
Due to the term frequency-inverse document frequency
method, the important vocabularies could be filtered with lesscommon words. In other words, after filtering multiple
similar fake news, the specific key vocabularies could bepossibly found since these specific key vocabularies would
be important in the same or similar fake news. In other words,
the similar fake news would include the similar specific key
vocabularies. In this research, these similar or the same
specific key vocabularies are called features of the specificfake news. Suppose that the features of different fake news
related to various topics would be also different. Then, after
term frequency-inverse document frequency method, thefake news with the similar key vocabularies or features
should be grouped or clustered as the same topic or purposeof the fake news. In this research, the Scikit-learn [32,33] isused for enhance the accuracy of classification and clustering.
Scikit-learn is the machine learning method which uses
Numpy to efficiently perform the linear algebra and array
operations. Scikit-learn provides various classification,
regression and clustering algorithms, such as support vector
machine, gradient boosting, term frequency-inverse
document frequency, k-means clustering, DBSCAN, etc. In
this research, the value k for the k-means clustering in eq. (1)should be assigned and given. To find the suitable value k,
the Elbow Method [34] is used. Based on Elbow Method, theelbow point is used to find the minimized value of k which to
be used for k-means algorithm. Hence, by recursively
executing the Scikit-learn method and Elbow Method, thefake news which were already verified could be clustered andgrouped automatically with less manual operation according
to the found k. In other words, the when the value k is defined,
the total numbers or the fake news groups or clusters is also
defined. In addition, when the numbers or the fake news
groups is defined, the key vocabularies related to each groups
could be called as the features of the specific fake news group.
In other words, after Scikit-learn method and Elbow Method,1360

the groups and the corresponding features are defined andfound.
C. False News Warning Method
After Key Vocabularies Merging Method, the features ofthe proved similar false news can be found. By analysing
various types of the proved false news, different features
related to these various false news could be obtained.
Therefore, based on the K-means algorithm, the intentional
false news even the news sources can be grouped. After
K-means grouping, the possible types of false news andpossible spreading news sources could be found.
Therefore, based on the classification of the various
intentional false news groups, the features of each group
could be collected. In other words, these features could be
used to represent or indicate the similar false news. Finally,
through comparing and matching the features, the new news
can be analysed and predicted.
Suppose that there are total fg features of the gth false news
group. In addition, by K-means algorithm, the new news
would be classified and grouped to the gth false news group.
Then, comparing the found features of the new coming news,
there would be fn features of the new news that included or
the same in the gth false news group. Therefore, in thisresearch, the new news could be evaluated as the possible
intentional false news by following function:  ൈ ͳͲͲΨ ൒ Ⱦ(4) ൌ ቊ 
where the Ⱦ is the on demand given value for theintentional false news warning.
Start
Non-Proved
News

Hadoop/MapReduce for Big
Data
Vocabulary/Seg
ment
K-means
GroupedFeatures of
False News

False News Warning Method

False News
Warning

Yes

False News
Warning

No

End
Fig. 3. The flowchart of the false news warning.

IV. VERIFICATION AND RESULTS
Following is the implementation example. Based on
mmsegj algorithm for Chinese vocabulary recognition, theChinese vocabularies presented in the website or web page
could be separated and recognized as Figure 4. According tothe {key, value} structure, the initially found key
vocabularies and the corresponding appearance probability
are checked. Some proper nouns are already recognized.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)However, many key vocabularies are recognized as the single
Chinese word. Based on the proposed Key Vocabularies
Merging Method, the two vocabularies could be merged
according to equation 1. In figure 4, for example, theappearance probabilities of "大" and "⛹" are almost thesame. According to equation 1, these two vocabularies could
be merged as "大⛹" or "⛹大". However, after next cycle ofthe Key Vocabularies Merging Method, due to that the "大⛹" is the independent proper nouns, and "⛹大" is not theindependent proper nouns, the appearance probability
differences between "大⛹" and "⛹大" will be very huge. In
other words, only the "大⛹" can be reserved and survived forthe further feature searching.
大⛹⛹大Fig. 4. The example of the implemented Key Vocabularies Merging Method.

In this research, to cluster and group the fake news which
are collected by the web crawler, the scikit-learn for and thejieba algorithm are connected by the python program. The
collected fake news and the related features are stored in the
noSQL database on cloud. To provide the visual presentationand interface, the python matplotlib is also used. In the
beginning, the specific topics of news from the Taiwan Fact
Check Center are all already proved and recognized as thefake news. According to this recognized topic, the normal
search engines such as Google or Yahoo are used to collectthe similar news. Based on the proposed Key Vocabularies
Merging
Method,jieba
algorithm,
andterm
frequency-inverse document frequency (TF-IDF) method,
the total found feature vocabularies or segmentations arevarious and huge. To cluster and group the collected fakenews which are corresponding to the fake news proved by
Taiwan Fact Check Center, the K-means algorithm integrated
with the Elbow methods is recursively executed. According
to Elbow Method, elbow point could be found around 4 withless differences of summation of squared distance when thevalue k is increased. The result is shown as Figure 5.

Summation of Squared Distance

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1361

8
6
4
2
0
1

2

3

4

5

6

7

8

9

10

Value of K
Fig. 5. The found value K based on Elbow Method according to thecorresponding squared distance.

In the beginning of the verification, there are four different
topics of fake news from the Taiwan Fact Check Center arecollected. Since according to the proposed methods the final
groups could be clustered into correct 4 groups, the related
news from the search engine could be also recognized andclustered. In this research, there are total 34 fake news fromvarious sources corresponding to the four main fake news
topics are collected as the data for verifying the classification.
There are 9 fake news are proved similar or the same as fakenews group 1; 10 fake news are proved similar or the same as
fake news group 2; 9 fake news are proved similar or thesame as fake news group 3; and 6 fake news are proved
similar or the same as fake news group 4. To verify the
proposed system, these all 34 fake news are initially given
without grouping and definitions. The proposed system
receives these fake news without understanding the numbersof topics and the numbers of fake news in each fake news
groups.
After the recursively processing the proposed Key
Vocabularies Merging Method, jieba algorithm, and term
frequency-inverse document frequency (TF-IDF) methodwith Elbow Method, the related fake news could be clusteredinto the same group by evaluation the square distance
between each fake news and the group center. In addition,
due to the Elbow Method, the minimum numbers of thegroups could be also decided according to the obtained
square distance. Finally, the related fake news could becorrectly clustered into the corresponding groups. Figure 6
presents the average distance between each fake news groupand the different group centers. The average distance of the 9
fake news which clustered into the fake news group 1 is only
about 0.1 square distance from the cluster center 1 but morethan 0.4 square distance to any other cluster centers. The
average distance of the 10 fake news which clustered into thefake news group 2 is only about 0.1 square distance from thecluster center 2 but more than 0.6 square distance to any othercluster centers. The average distance of the 9 fake news
which clustered into the fake news group 3 is only about 0.13
square distance from the cluster center 3 but more than 0.58
square distance to any other cluster centers. The average
distance of the 6 fake news which clustered into the fakenews group 4 is only about 0.05 square distance from thecluster center 4 but more than 0.47 square distance to any
other cluster centers. In other words, the proposed algorithm
and processes could really recognize and cluster the fakenews correctly.
Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

Average Distance between Cluster Centerand Fake News Groups

1.2

Squared Distance1

Fake NewsGroup 1

0.8
0.6

Fake NewsGroup 2

0.4

Fake NewsGroup 3

0.2

Fake NewsGroup 40
Cluster1 Cluster2 Cluster3 Cluster4

Fig. 6. The average square distance between the fake news and the cluster
center.

Therefore, according to the proposed Key Vocabularies
Merging Method, jieba algorithm, term frequency-inverse
document frequency (TF-IDF) method with Elbow Method,
the trained fake news data could be correctly recognized.
Considering the purposes of the malicious organizations orusers, the fake news would be frequently announced or
created related to some specific person or topics. In otherwords, the rapidly announced fake news from the sameorganizations or groups could be recognized and found.

V. DISCUSSION
Although according to the proposed system the proved
fake news could be recognized and clustered, to predict the
new coming news is still difficult. First, the proposed system
can recognized and cluster the existed and proved fake news.
Based on the collected huge amount of the proved fake news,
the recognition and clustering could be correct. However, theaccuracy of the prediction should depend on huge data foundfrom the Internet. It means that the prediction is difficult
when there is only a few data collected from the Internet. In
addition, since the new coming news is not identified andverified, the proposed system could only differentiate thisnews according to the related information collected andannounced in the past. In other words, if the most of the news
disseminated by the social media or organization is true news,
then according to the proposed system the new coming news
would be easily recognized as true news. In opposition, if
most of the news disseminated by the social media ororganization is fake news, the new coming news would be
identified and clustered as fake news even it is really true.
The found features would result in the bias when cluster andrecognize the news from specific media or group. In otherwords, the history of the media or organization itself would
affect the prediction result. The media or group with more
proved fake news in the past would be easily recognized andclustered as the fake news in the future.
Second, due to the verification and the proof of each news,
some information or messages hidden in the news would
need time for searching and finding. Some inside information
could be only be proved and recognized after long time.
Therefore, to prove the news true or fake itself is an difficult

1362

issue. In addition, only more information found then thecurrent news could be identified as the true or fake news in
the future. Therefore, to directly verify and recognize a news
is true or fake is not easy especially when the related
information is not enough. Finally, the credibility of themedia or organization still plays the main and important role.
However, due to some political purposes, the fake news
would be created and broadcasted by specific malicious
media organization and groups. These malicious parties
would actively spread the fake news. According to the
proposed system, these groups, individual person, evenmedia organization could be found and evaluated. In the
future, some more tags could be found and defined for thesemalicious fake news spreaders. In other words, the credibilityof these malicious fake news spreaders could be evaluatedand given the warning according to the new spreading
history.

VI. CONCLUSION
In this research, the proposed Key Vocabularies Merging
Method could find the features of the specific intentional
false news. Based on the term frequency-inverse document
frequency (TF-IDF) method with Elbow Method, the proved
false news or fake news could be correctly clustered into thecorresponding groups. In addition, the numbers of the groups
related to the false news could be automatically increased. In
other words, according to the analysis of the historic proved
news, the repeatedly found news could be recognized andverified.
Based on the proposed False News Warning Method,
different intentional false news could be grouped. According
to the comparing with the various groups of the intentional
false news, the false news warning could be given
corresponding to the new news.
ACKNOWLEDGMENT
Thanks for the support of Cloud Computing and Intelligent
System Lad. (CCIS Lab.) of National Formosa University.
REFERENCES
[1]
[2]
[3]
[4]

[5]
[6]
[7]
[8]

[9]

Soroush Vosoughi, Deb Roy, Sinan Aral, " The spread of true andfalse news online," Science, Vol. 359, Issue 6380, pp. 1146-1151,
2018. DOI: 10.1126/science.aap9559
http://www.google.com
http://tw.yahoo.com
S. Yang, K. Shu, S. Wang, R. Gu, F. Wu, and H. Liu. "Unsupervised
fake news detection on social media: A generative approach." Proc.
of 33rd AAAI Conference on Artificial Intelligence, 2019. DOI:
https://doi.org/10.1609/aaai.v33i01.33015644
W. Y Wang, ” liar, liar pants on fire”: A new benchmark dataset for
fake news detection. arXiv preprint arXiv:1705.00648. 2017.
C. Castillo, M. Mendoza, B. Poblete, "Information credibility on
twitter." In Proc. of the 20th international conference on World wideweb, 675–684. ACM, 2011.
C. F. Bond Jr, B. M. DePaulo, "Accuracy of deception judgments."
Personality and social psychology Review 10(3) pp.214–234, 2006.
J. Kim, B. Tabibian, A. Oh, B.S cholkopf, M. Gomez Rodriguez,"Leveraging the crowd to detect and reduce the spread of fake news
and misinformation." In Proc. of the 11th ACM International
Conference on Web Search and Data Mining, pp.324–332. ACM
2018.
K.J. Chen & Ming-Hong Bai, "Unknown Word Detection for
Chinese by a Corpus-based Learning Method," International Journalof Computational linguistics and Chinese Language Processing,
1998, Vol.3, No.1, pages 27-44

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

[10]

[11]
[12]

[13]
[14]
[15]
[16]
[17]

[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]

[27]
[28]
[29]

[30]

[31]

[32]

[33]

[34]

Ming-Shen Jian, Wei-Cheng Hong, Sheng-Che Tsai, Yu-Wei Chen,
Chih-Ling Fan "Based on Automatic Correlation Keyword Grouping
and Combination Based Deep Information Search Corresponding toSpecific Language Big Data -- Case of Leisure Recreation " ICACT,
pp.372-377, Feb. 2020.
Wei-Yun Ma & K.J. Chen, "A bottom-up Merging Algorithm for
Chinese Unknown Word Extraction," Proc. of ACL workshop on
Chinese Language Processing, pages 31-38, 2003.
http://120.108.221.55/profchwu/ml/%E6%95%99%E6%9D%90/%
E6%96%87%E4%BB%B6%E5%88%86%E9%A1%9E/%E4%B8%AD%E7%A0%94%E9%99%A2%E8%A9%9E%E6%80%A7%E
5%AE%9A%E7%BE%A9.pdf
Chandni Saini, Vinay Arora, “Information retrieval in web crawling:
A survey,” International Conference on Advances in Computing,Communications and Informatics (ICACCI), 2016.
M. S. Jian, et al, "Cloud Based Agriculture Safety Inspection with
Multiple Standard Sources," Proc. of IEEE ICACT, pp. 201-206, Feb.
2018.
Yu-Chen Wang, “The Structure of the Big Data Based on the Hadoop
Platform”, thesis, National Chi Nan University, Taiwan, 2015.
Zhi-Kai Liao, “Fault-Tolerant Management Framework for Hadoop
Distributed File System”, thesis, Tamkang University, Xinbei,
Taiwan, 2013.
Jia-Chun Lin, “Study of Job Execution Performance, Reliability,
Energy Consumption, and Fault Tolerance in the MapReduce
Framework”, thesis, National Chiao Tung University, Hsinchu,
Taiwan, 2015.
Man-Ning Wu, “Data Analysis Results Storge Design in HBase,
Chung Yuan Christian University”, thesis, Taoyuan, Taiwan, 2015.
Hadoop–Introduction,
Available
online:https://www.tutorialspoint.com/hadoop/hadoop_introduction.htm
Ming-Shen Jian, Yi-Chi Fang , Yu-Kai Wang, Chih Cheng, " Big
Data Analysis in Hotel Customer Response and Evaluation based on
Cloud," Proc. of IEEE ICACT, pp.791-795, Feb. 2017.
Fredrik Erlandsson, Roozbeh Nia, Martin Boldt, Henric Johnson, S.
Felix Wu, " Crawling Online Social Networks," Proc. of 2015 2nd
ENIC, pp.9-16, Sept. 2015
https://www.seleniumhq.org/
Chih-Hao Tsai, "MMSEG: A Word Identification System for
Mandarin Chinese Text Based on Two Variants of the Maximum
Matching Algorithm," http://technology.chtsai.org/mmseg/
https://www.itread01.com/articles/1476609057.html
https://spark.apache.org/
Ming-Shen Jian, Ming-Sian You, ĀCloud Based Hybrid Evolution
Algorithm for NP-Complete Pattern in Nurse Scheduling Problem,āInternational Journal of Innovation, Management and Technology,
Vol. 7, No. 5, pp.234-237, 2016
Ming-Shen Jian, "Intelligent System Based on Cloud and IOT," Mar.
2019, EHGBooks Publishing, (ISBN: 978-1-62503-503-5)
https://tfc-taiwan.org.tw/articles/report
Zhang, X.; Wu, P.; Cai, J.; Wang, K. A Contrastive Study of Chinese
Text Segmentation Tools in Marketing Notification Texts. Journal of
Physics: Conference Series. Vol. 1302, No.2, 2010. doi:
10.1088/1742-6596/1302/2/022010
Hakim, A. A.; Erwin, A.; Eng, K. I.; Galinium, M.; Muliady, W.
Automated document classification for news article in Bahasa
Indonesia based on term frequency inverse document frequency(TF-IDF) approach. 2014 6th International Conference on
Information Technology and Electrical Engineering (ICITEE).
pp.1-4, 2014. doi: 10.1109/ICITEED.2014.7007894.
Havrlant, L.; Kreinovich, V. A Simple Probabilistic Explanation ofTerm Frequency-Inverse Document Frequency (tf-idf) Heuristic (andVariations Motivated by This Explanation). International Journal of
General Systems. Vol. 46, No.1, pp.27-36, 2017. doi:
10.1080/03081079.2017.1291635
Hishamuddin, M. N. F.; Hassan, M. F.; Tran, D. C.; Mokhtar, A. A.
Improving Classification Accuracy of Scikit-learn Classifiers with
Discrete Fuzzy Interval Values. 2020 International Conference on
Computational Intelligence (ICCI), pp.163-166, 2020. doi:
10.1109/ICCI51257.2020.9247696.
Brites, D. and Wei, M. PhishFry - A Proactive Approach to Classify
Phishing Sites Using SCIKIT Learn. 2019 IEEE Globecom
Workshops(GC
Wkshps),pp.1-6,2019.
doi:
10.1109/GCWkshps45667.2019.9024428.
Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.
Integration K-Means Clustering Method and Elbow Method For
Identification of The Best Customer Profile Cluster. 2018 IOP Conf.
Ser.: Mater. Sci. Eng. 336, 2017.

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Ming-Shen Jian was born in Kaohsiung City,
Taiwan in 1978. He received the B.S. from the
National Chiao Tung University, HsinChu, andPh.D degrees in Computer Science andEngineering from the National Sun Yat-sen
University, Kaohsiung, Taiwan in 2007.
From 2018, he was an Associate Professor anddirector with the National Formosa University
Cloud Computing and Intelligent System
Laboratory. Currently he is also an IEEE Senior Member. Since 2009, he has
been an Assistant Professor with the Computer Science and Information
Engineering Department, National Formosa University. He is the author offour books, more than 50 articles, and at least 15 invention patents. His
research interests include IOT development and application, Big Data,
Optimal Solution, Intelligent System, and Cloud Computing. He was a
Secretary of the Taiwan Association of Cloud Computing. Dr. Jian was arecipient of the IEEE sponsored international conference Paper Award in
2016, 2017, and 2018.
Rong-Bin Deng was born in 2000, Taiwan. Currently
he is an B.S. degree student of Dept. Computer Scienceand Information Engineering at National Formosa
University. His current research interests are in the area
related to Web Service, and Cloud Computing. He joins
the Cloud Computing and Intelligent System Lad.
(CCIS Lab.) from 2019.
Chen-Wei Fang was born in 1999, Taiwan. Currently
he is an B.S. degree student of Dept. Computer Scienceand Information Engineering at National Formosa
University. His current research interests are in the area
related to IoT, and Cloud Computing. He joins the
Cloud Computing and Intelligent System Lad. (CCISLab.) from 2019.
Hua-Yu Wu was born in 1999, Taiwan. Currently he isan B.S. degree student of Dept. Computer Science andInformation Engineering at National Formosa University.
His current research interests are in the area related to Big
Data, and Cloud Computing. He joins the Cloud
Computing and Intelligent System Lad. (CCIS Lab.) from2019.
Wen-Hsiang Hsieh was born in 2000, Taiwan. Currently
he is an B.S. degree student of Dept. Computer Scienceand Information Engineering at National Formosa
University. His current research interests are in the area
related to Web Service, and Cloud Computing. He joins
the Cloud Computing and Intelligent System Lad. (CCISLab.) from 2019.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1364

A Novel Fully Distributed EPON-Based 5G RAN
Architecture Modeling with Handover Analysis
Syed R. Zaidi*, Ajaz Sana*, and Shahab Hussain***

Dept. of Engineering, Physics & Technology, Bronx Community College of the City University of New York, USA**
Mobile Networks Department, Nokia Corporation, 1 Robbins Rd., Westford, MA 01886, USAsyed.zaidi@bcc.cuny.edu, ajaz.sana@bcc.cuny.edu , shahab.hussain@nokia.com

Abstract— Traditional mobile backhaul Radio AccessNetwork (RAN) employ centralized data and control plane
scheme. In this work, we propose a novel Passive Optical
Network (PON) based next-generation mobile backhaul RAN
architecture in a distributed scheme that enables theredistribution of some of the intelligence currently centralized
in the Mobile Packet Core (MPC) platform out into the accessnodes of the RAN. Specifically, this work proposes a fully
distributed ring-based EPON architecture that enables the
support of a converged PON-5G option 3X access networking
transport infrastructure to seamlessly backhaul both mobile
and wireline multimedia traffic and services. Performance
analysis show that the proposed distributed architecture show
better results in throughput, latency and handover analysis.

Hence, a cost-effective fiber supported all-packet-based
mobile backhaul RAN architecture that is compatible withthese inherently distributed and packet-oriented NG RAN
architectures, is needed to efficiently scale current mobile
backhaul networks. However, deploying a new fiber-based
mobile backhaul infrastructure is a costly proposition mainly
due to the significant cost associated with digging the trenches
in which the fiber is to be laid.
The 5G System can use non-standalone solution with dual
connectivity to 4G. That approach is called Option 3. As
shown in Fig. 1. 5G can be deployed as a standalone solution
without LTE.

Keywords- 5G, EPON, LTE, Option 3x, PON, TDM

T

I.

EPC

INTRODUCTION

HE only viable solution to support extraordinary growthof mobile backhaul to provision the emerging 5G trafficthat includes 5G and cellular Long-Term Evolution (LTE),
requires rapid migration from today’s legacy circuit-switched
T1/E1 wireline and microwave backhaul technologies to a
new fiber-supported, all-packet-based mobile backhaul
infrastructure [1-4]. Mobile backhaul sometimes referred to,as the Radio Access Network (RAN), is used to backhaul
traffic from individual base stations (BSs) to the Core
Network (CN). In contrast with the typically centralized
2G/3G RAN infrastructure, the 5G architecture specifically
5G option 3x has fundamentally different RAN design
requirements.

———————————————————————
Manuscript received Jan 20, 2021. This work is a follow-up of the invitedjournal to the accepted paper entitled "An efficient & Cost-Effective EPONBased Next Generation 5G Mobile Backhaul RAN Architecture" of the 23rd
International Conference on Advanced Communication Technology(ICACT2021).
Syed Rashid Zaidi, he is an assistant professor at the Bronx Community
College of the City University of New York, USA. (corresponding author,
phone:+1-718-289-5100;
fax:+1-718-289-6403;
e-mail:syed.zaidi@
bcc.cuny.edu).
Ajaz Sana, he is assistant professor at the Bronx Community College of
the City University of New York, USA. (e-mail: ajaz.sana@ bcc.cuny.edu).
Shahab Hussain, he is a senior professional at the Nokia Corporation,
USA. (e-mail: shahab.hussain@ nokia.com).

4G -eNb

5G -gNB

UE
User + Control
Plane

User
Plane

Fig. 1. 5G Option 3x

This approach is called Option 2 in 3GPP. The very first
5G networks must use Option 3 because it is available in
3GPP 6 months before Option 2. Dual connectivity has also
other benefits. It allows to combine 4G and 5G data rates
together and it allows to reuse existing Evolved Packet Core
(EPC). Option 3 is a dual connectivity deployment with EUTRA as the anchor Radio Access Technology (RAT) and

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

NR as the secondary RAT in a non-standalone configuration
based on the existing EPC. Both 5G base stations (gNodeB)
and 4G base stations (eNodeB) are connected to the EPC. The
control plane goes via LTE. In Option 3X the gNB is
deployed to support the user traffic and the control traffic goes
through the 4G eNB. The option 3X seems preferred bymajority operators for enhanced mobile broadband.
This underlying potential prompted many carriers aroundthe world to consider the use of the fiber-based Passive
Optical Network (PON) access infrastructure as an all-packetbased converged fixed-mobile optical access networking
transport architecture to backhaul both mobile and typical
wireline traffic backhaul RAN architecture. A PON connects
a group of Optical Network Units (ONUs) located at thesubscriber premises to an Optical Line Terminal (OLT)
located at the service provider’s facility. While the economies
for commercially deploying TDM-PON in the access arena asa near-term converged fixed-mobile optical networking
transport infrastructure are quite convincing, however,
several technical issues must be addressed first before
mainstream TDM-based PONs could be used as viable opticalaccess networking technology that enables the support of atruly unified PON-5G access networking architecture or justa 5G mobile.
The most notable issue is, TDM-PON is a centralized
access architecture– relying on a component at the distant
OLT to arbitrate upstream traffic, while 5G is a distributed
architecture where, in particular, the 5G 3x option with gNB
is anchored on 4G eNB requires a new distributed RAN
architecture and further create a requirement to fully meshing
the BSs (the X2 interface for 5G-LTE BS-BS handoffs
requires a more meshed architecture) [1-3, 9]. The major
weakness is that mainstream PONs are typically deployed astree topologies and the tree-based topology can neither
support the distributed access architecture norintercommunication among the access nodes (ONUs)
attached to the PON. The key challenge in devising a truly
unified PON-5G access architecture is how to reconcile thetraditionally centralized PON’s architecture and NCM
operations with the typically distributed 5G’s architecture andNCM operations. Though numerous hybrid Fiber-Wirelessnetwork architectures have been expected to utilize the fiberbased PON access infrastructure to backhaul mobile traffic[5-8], most of these architectures, however, have utilized thetypically centralized tree-based PON topology, which can
only support a centralized RAN architecture. Since both
wireless and wireline segments of these hybrid architectures
are assumed to be centralized, the key design requirements
and challenges associated with overlaying a fully distributed
mobile RAN segment (e.g., 5G/LTE) over a typically

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centralized PON-based wireline segment, still remainunresolved. The purpose of this paper is to propose a novel,simple, and cost effective PON-based 5G mobile backhaul
RAN architecture that enables redistribution of some of theintelligence (e. g., bandwidth/QoS provisioning) currently
centralized in the Mobile Packet Core (MPC) platform out
into the access nodes of the RAN [9-11]. Specifically, thisproject devises a fully distributed ring-based EPON
architecture that enables the support of a converged PON-5G
LTE access networking transport infrastructure to seamlessly
backhaul both mobile and wireline multimedia traffic andservices. We quantify the merits of utilizing a distributed
EPON-based 5G option 3x RAN architecture and those of
traditional mobile 5G backhaul infrastructure. The salientfeature of the proposed architecture is that it supports a fully
distributed control plane that enables intercommunication
among the access nodes (ONUs/BSs) as well as signaling,scheduling algorithms, and handoff procedures that operate ina distributed manner. We outline some of the key technicalrequirements associated with devising a truly unified fixedmobile 5G LTE access transport architecture that is built on
top of a typically centralized PON infrastructure.
The proposed architecture supports several key networking
features that significantly improves the performance of both
the RAN and MPC in terms of handoff capability, overall
network throughput and latency, and QoS support. Though
we have chosen Ethernet-based PON and5G as representativetechniques for fixed PON and 5G mobile access technologies,
the proposed architecture and related operation principles arealso applicable to other PON and 5G access networks such as
GPON and 5G/LTE.
The rest of the paper is organized as follows: Section II
gives an overview of the standalone ring-based EPON
architecture and Section III discusses how to evolve the ringbased architecture to an all-packet-based converged fixedmobile optical access networking transport infrastructure.
Section IV presents some of the key salient features enabledby the proposed architecture. Section V reports simulation
results and Section VI offers some concluding remarks.
II.

OVERVIEW OF THE STANDALONE RING-BASEDEPON ARCHITECTURE

Fig. 2. illustrates the proposed standalone ring-based PON
architecture [12]. An OLT is connected to N ONUs via a 20
km trunk feeder fiber, a passive 3-port optical circulator, anda short distribution fiber ring.
The ONUs are joined with point-to-point links in a closed
loop around the access ring. The links are unidirectional. Bothdownstream (DS) and upstream (US) signals (combined
signal)Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1366

802.3 ah interface
DBA & Schedulingcontrolmessages

2x1 CWDM
Combiner
(ePC)
SGW /
OLT

ONU-1

BS with 802.3ah interface
2

DS 10-20 km1
US

BS admission
control module3

BS (eNB)

Ring ~1 km
ONU-N

20:80 Splitter

Upstream
Downstream

BS (gNB)

Up+ Down stream

Fig. 2. Standalone Ring-based Architecture

ONU Architecture and Operation
E-O

Processing
DS
Traffic

Processing US traffic

US RX

Transmission of local US
Traffic along with regenerated
US traffic

US TX

DS RX
10 %
10:90
1x2 Splittern:(1-n)90 %2x1 CWDM
Combiner

1x2 CWDM DMUX

Incoming
SignalE

A

Outgoing
SignalFig. 3. ONU Architecture

are transmitted in one direction only. The US signal istransmitted sequentially bit by bit around the ring from one node
to the next where it is terminated, processed, regenerated, andretransmitted at each node (ONU). Since US transmission isbased on a TDMA scheme, inter-ONU traffic (LAN data andcontrol messages) is transmitted along with upstream traffic
destined to the OLT (MAN/WAN data) within the same preassigned time slot. Thus, in addition to the conventionaltransceiver maintained at each ONU (a Oup US transmitter (Tx)and a Od DS receiver), this approach requires an extra receiver(Rx) tuned at Oup to process the received US/LAN signal.
DS signal is coupled with the ring at port 2 of the opticalcirculator. After recombining it with the re-circulated US signal
via the 2x1 CWDM combiner placed on the ring directly after
the optical circulator, the combined signal then circulates aroundthe ring (ONU1 through ONUN) in a Drop-and-Go fashion,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

where the DS signal is finally terminated at the last ONU. The
US signal emerging from the last ONU is split into two replicas
via the 20:80 1x2 passive splitter (Fig. 2) placed on the ring
directly after the last ONU. The first replica (80 %) is directed
towards the OLT via circulator ports 1 and 3, where it is thenreceived and processed by the US Rx (housed at the OLT),
which accepts only MAN/WAN traffic, discards LAN traffic,
and process the control messages, while the second replica (20%) is allowed to recirculate around the ring having been
recombining with the DS signal via the 2x1 CWDM combiner.
The detailed ONU architecture is shown in Fig. 3. Each ONU
attaches to the ring via the input port of a 1x2 CWDM DMUX
housed at each ONU (incoming signal at point A in Fig. 3.) andcan transmit data onto the ring through the output port of a 2x1
CWDM combiner (outgoing signal at point E in Fig. 3.). At each
ONU, the incoming combined signal is first separated into its
two constituent: DS and US signals via the 1x2 CWDM DMUX
housed at the ONU. As can be seen from Fig. 3., the separated
US signal is then received and processed via the US Rx housed
at the ONU, where it is regenerated and retransmitted along with
the ONU’s own local control and data traffic.
As can also be seen from Fig. 3, the separated DS signal iscoupled with the input port of the (10: 90) 1x2 passive splitter,
which splits the DS signal into a small (10%) “Drop-signalportion” and a large (90%) “Express-signal-portion”. The small
portion (Drop-Signal) is then received and processed by the DS
Rx housed at the ONU. The remaining large portion emerging
from the 90% output splitter’s port (Express-Signal) is furthertransmitted through the ring to the next ONU, where it is, once
again, partially split and detected at the corresponding DS Rx
and partially transmitted towards the rest of the ring. Note that
the Express-Signal recombines again with the retransmitted US
signal (all previous ONU’s regenerated US signals plus its own
US signal) via the 2x1 CWDM combiner to form the outgoingcombined signal (incoming signal for next ONU) that circulates
around the ring.
III. PROPOSED EPON-BASED CONVERGED FIXEDMOBILE OPTICAL ACCESS NETWORKING ARCHITECTURE
The standalone ring-based EPON architecture can be evolved
around an all-packet-based converged fixed-mobile opticalaccess networking transport infrastructure (or just 5G 3x mobile
backhaul RAN) by simply interconnecting (overlying) the
ONUs with the 5G and 4G’s BSs and the OLT with serving
access gateway (SGW). Under this simple overlay(independent) model, the PON and 5G/4G systems are operated
independently where the RAN system is assumed to have its
own NCM operations, independent of those for the PON. The
BS is assumed to be collocated with an ONU or treated as a
generic user attached to it. The ONU and BS can beinterconnected as long as they support a common standard
interface. Thus, the OLT, SGW, ONUs, and 5G/4G BSs, are all
assumed to support a common standard interface (e.g., 802.3ah
Ethernet interface). Each ONU is assumed to have two different

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Ethernet port ranges; the first port range will support wired
users, while the second port range will support mobile users. The
port ranges will be used by the ONUs to identify anddifferentiate between mobile users versus fixed users.
A. Fully Distributed Control Plane
This work uses the control and management messages definedby the IEEE 802.3ah multi-point control protocol (MPCP)
standard [13] that facilitate the exchange of control andmanagement information between the ONUs/BSs and OLT. The
protocol relies on two Ethernet control messages, GATE (from
OLT to ONUs/BSs) and REPORT (from ONUs/BSs to OLT and
between ONUs/BSs) messages in its regular operation. Directcommunication among ONUs/BSs is achieved via the US
wavelength channel {control messages along with both LAN
and US data share the same US channel bandwidth (in-band
signaling)}, which is terminated, processed, regenerated, andretransmitted at each ONU. Since control messages areprocessed and retransmitted at each node, the ONUs can directly
communicate their US/LAN queue status and exchange
signaling and control information with one another in a fully
distributed fashion. Likewise, BSs can also directly
communicate the status of their queues and radio resources andexchange signaling and control messages with one another. The
control plane utilized among the ONUs/BSs can thus support a
distributed PON-4G RAN architecture, where each access node
(ONU/BS) deployed around the ring has now truly physicalconnectivity and is, thus, capable of directly communicatingwith all other access nodes, in conformity with 4G standards.
Each access node maintains a database about the states of its
queue and the state of every other ONU/BS’s queue on the ring.
This information is updated each cycle whenever the ONU
receives new REPORT messages from all other ONUs. During
each cycle, the access nodes sequentially transmit their
REPORT messages along with both US and LAN data inascending order within their granted timeslots around the ringfrom one node to the next, where each REPORT message isfinally removed by the source ONU after making one trip aroundthe ring. The REPORT message typically contains the desired
size of the next timeslot based on the current ONU’s buffer
occupancy. Note that the REPORT message contains theaggregate bandwidth of both fixed and mobile data buffered ateach ONU’s/BS’s queue (requested size of next timeslot).
An identical dynamic bandwidth allocation (DBA) module,
which resides at each access node (ONU/BS), uses the REPORT
messages during each cycle to calculate a new US timeslot
assignment for each ONU. ONUs sequentially andindependently run instances of the same DBA algorithm
outputting identical bandwidth allocation results each cycle. The
execution of the algorithm at each ONU starts immediately
following the collection of all REPORT messages. Thus, all
ONUs must execute the DBA algorithm prior to the expirationof the current cycle so that bandwidth allocations scheduled forthe next cycle are guaranteed to be ready by the end of thecurrent cycle. Once the algorithm is executed, the ONUs

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

sequentially and orderly transmit their data without any
collisions, eliminating the OLT's centralized task of processing
requests and generating grants for bandwidth allocations.
B. Dynamic Bandwidth Allocation and QoS Support &Mapping
Based on bandwidth demands, ONUs can be classified into
two groups, namely: (i) lightly loaded ONUs that havebandwidth demands less than BMAX (ii) heavily loaded ONUs
that have bandwidth demands more than BMAX . BMAX is defined
as follows in equation (1)
Bmax

1
N

[R EPON( TMAX - ( N*T )]G(1)Where, TG is guard band interval, N is number of ONU’s,
TMAX is EPON maximum cycle period and REPON is EPON data
rate. Bandwidth demand here is the aggregate of wired andwireless demand. During each cycle, the DBA module keeps
track of the unclaimed bandwidth from the set of lightly loaded
ONUs. It then redistributes this excess bandwidth to otherheavily loaded ONUs based on their requested bandwidth.
During each cycle, the DBA module keeps track of theunclaimed bandwidth from the set of lightly loaded ONUs. It
then redistributes this excess bandwidth to other heavily
loaded ONUs based on their requested bandwidth i.e. two
ONUs requesting bandwidths B1 and B2 more than B MAX will
be assigned excess bandwidths proportional to B1 and B2.
During each cycle, the lightly loaded ONUs with Ri < BMAX
will contribute a total remainder cycle bandwidth:
The heavily loaded ONUs with Ri > BMAX will require a
total over the limit cycle bandwidth as shown in equation (2):(2)‫ܤ‬஼௬௖௟௘̴ை௩௘௥௟௜௠௜௧ ൌ  σு௜ ሺܴ௜ െ  ‫ܤ‬ெ஺௑ ሻ
The total remainder cycle bandwidth can be fairly distributed
amongst the heavily loaded ONUs to expand their maximum
transmission window as follows in equation (3)ο‫ܤ‬௜௘௫௧௥௔ ൌ  ‫ܤ‬஼௬௖௟௘̴ோ௘௠௔௜௡ௗ௘௥ ൤ோ೔ ି஻ಾಲ೉

஻಴೤೎೗೐̴ೀೡ೐ೝಽ೔೘೔೟

൨

(3)

The granted bandwidth, BGH, for a heavily loaded ONUi is
given by equation (4):

BGH

'Biextra  BMAX

(4)

If Ri is the requested bandwidth of ONUi, BGranted is the
bandwidth granted using the proposed limited service-based
distributed DBA scheme, then BGranted can be expressed as
equation (5):
BGranted

­ Ri
°
® Ri
°
¯ BGH

If Ri d BMAX
If Ri ! BMAX & BCycle _ Re mainder t BCycle _ OverLimit
If Ri ! BMAX & BCycle _ Re mainder  BCycle _ OverLimit

(5)

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We call this process as Inter-ONU scheduling.
After each ONU is given fair share of its bandwidth then it
runs Intra-ONU scheduling module to distribute its total granted
bandwidth to wired and wireless users. Intra-ONU schedulingalgorithm is given as follows in (6) and (7);‫ܤ‬ோఱಸ೔೑ೃఱಸಱబǤరಳହீ̴௚௥௔௡௧௘ௗୀቐ

೒ೝೌ೙೟೐೏ ଴Ǥସ஻೒ೝೌ೙೟೐೏ ାቄ଴Ǥ଺஻೒ೝೌ೙೟೐೏ିோೢ೔ೝ೐೏ ሽ௜௙ோఱಸಭబǤరಳ೒ೝೌ೙೟೐೏ Ƭோೢ೔ೝ೐೏ழ଴Ǥ଺஻೒ೝೌ೙೟೐೏

(6)
‫ܤ‬

ோೢ೔ೝ೐೏೔೑ೃೢ೔ೝ೐೏ಱబǤలಳ೒ೝೌ೙೟೐೏ ௪௜௥௘ௗ̴௚௥௔௡௧௘ௗୀቐ
଴Ǥ଺஻೒ೝೌ೙೟೐೏ ାቄ଴Ǥସ஻೒ೝೌ೙೟೐೏ ିோఱಸሽ௜௙ோೢ೔ೝ೐೏ಭబǤలಳ೒ೝೌ೙೟೐೏ Ƭோఱಸழ଴Ǥସ஻೒ೝೌ೙೟೐೏

(7)

Where R5G is the bandwidth demand from 5G users and Rwired
is the bandwidth demand from the wired users. Moreover
B5G_granted and Bwired_granted is the bandwidth granted to 5G andwired users respectively. Since, typically the wired data rate ismore than wireless, it is given more share.
Typical 5G/4G MAC is centralized and connection-oriented.
A connection identifier (CID) identifies each 5G/4G connection.
The main mechanism for providing a connection-based QoS is
to classify and associate packets traversing the MAC interface
to IP Service Flows (SFs), where each existing SF is identified
by a 32-bit SF identifier (SFID) and is characterized by a set of
QoS parameters. A CID is then mapped into an SFID provided
that the SF has already been admitted (active SF). Once the UE’s
CIDs are terminated at the BS, they are mapped into theappropriate mobility tunnels based on their CIDs. The BS’s
packet classifier then maps their constituent IP SFs into theirappropriate priority queues based on CIDs attached to the IP
packets. To allow for traffic separation in the PON-basedtransport network (IP cloud connecting the BSs to the
OLT/SGW), the BS maps each CID into a corresponding
DiffServ Code Point (DSCP) in order to translate CID totransport-based QoS (DSCP). Using this mapping function,
packets on a given CID associated with specific QoS parameters
are marked with a specific DSCP for forwarding in the transport
network. The MPC performs the mapping for DL packets
On the other hand, EPON technology does not allow this typeof CID-based connection. Rather, it supports only enhanced QoS
through prioritization where packets are classified, stored indifferent priority queues and, then, scheduled for service
according to their priorities. In a typical centralized EPON, QoS
support is implemented via two independent schedulingmechanisms [10]: 1) inter-ONU scheduling: an aggregate
bandwidth is allocated to each ONU by the OLT. 2) intra-ONU
scheduling: each ONU makes a local decision to allocate thegranted bandwidth and schedules packets transmission for up to
eight different priority queues in the ONU. In the case of the
proposed architecture, however, instances of the same DBA
algorithm are executed simultaneously at each ONU. Thus, bothscheduling mechanisms (inter and intra-ONU scheduling) are
performed at each ONU-BS in a fully distributed approach,leading to the notion of integrating both scheduling mechanisms

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

at the ONU. This enables the proposed distributed architecture
to provide better QoS support and guarantees.
For simplicity, we assume that each ONU maintains three
separate priority queues that share the same buffering space.
We consider three priority classes P0, P1, and P2, with P0
having the highest priority and P2 having the lowest. These
classes are used for delivering voice (CBR), video stream(variable-bit-rate or VBR), and best-effort (BE) data,
respectively, as they allow easy mapping of DiffServ’s
Expedited Forwarding (EF), Assured Forwarding (AF), and BEclasses into 802.1D classes. Since both EPON and 5G/LTE
classify data traffic in a differentiated services mode, aneffective mapping mechanism is required between EPON
priority queues and CID-based 5G/LTE IP flows. Specifically,
the mapping has to identify which 5G/LTE IP flow should be
stored in which EPON priority queue for equivalent QoS.
EPON has up to eight different priority queues in each ONU,
while 5G/LTE supports Guaranteed Bit Rate (GBR) & NonGBR classes of service. QoS in 4G is based on bearers and in
5G is based on flow. In this work we assume that 5G/LTE GBR
queues are mapped into EPON P0 queue and Non-GBR queues
into P1 and P2 based on QoS parameters setting.
KEY SALIENT NETWORKING FEATURES ENABLED BY
THE DISTRIBUTED EPON-BASED RAN ARCHITECTURE

IV.

The distributed ring-based architecture along with thesupporting control plane enables the proposed EPON-based
RAN architecture to support several key salient networking
features that significantly enhance the performance of both the
RAN and MPC in terms of handoff capability, overall network
throughput and latency, and QoS support. These include:
A. Significance of Local Mobile LAN Traffic:
Local mobile LAN traffic is defined here as bidirectional
multimedia traffic exchange (including VOIP, video, and data
sessions) between two mobile users served by two different BSs
that are either collocated or attached with/to two different ONUs
on the same ring (same PON domain). In the proposed EPONbased RAN architecture, this traffic is directly routed on the ringfrom the source BS directly to the destination BS and vice-versa
as local LAN traffic, without the direct participation of either the
OLT or the MPC (e. g., SGW). This is significant as the volumeof VOIP calls and/or multimedia data exchange between all
local mobile users that are served by the many different BSs
attached to the same ring, is substantial. In a typical 5G/LTE
RAN, however, this traffic represents bidirectional US/DS dataexchange between the two mobile users, which must be routed
first from the source BS to the MPC (US traffic) and then from
the MPC to the destination BS (DS traffic), and vice-versa.
Thus, a substantial volume of local mobile traffic andassociated signaling overhead, as well as the lengthy andcomplex processing of this traffic (e. g., sessions (LTEbearers/mobility tunnels) switch/set-up, retain, and tear-down
and associated signaling commands from the BSs to the MPC
and vice-versa), have been offloaded from the overburdened

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MPC to the access nodes (ONUS/BSs) of the RAN. This has asignificant impact on the performance of the MPC. First, it frees
up a sizable fraction of the badly needed network resources aswell as processing on the centralized serving nodes (e. g. SGW)in the MPC to handle Internet-bound traffic more efficiently.
Second, it frees up capacity and sessions on the typicallycongested mobile backhaul from the BSs to the MPC and viceversa.
B. Enhanced Handoff Capabilities
In 5G and LTE standards, hard handoff (HHO) is mandatory.
The HHO is a break-before-make procedure, in which LTE user
equipment (UE) breaks its connections with the serving BS(SBS) before setting up new connections with the target BS(TBS) and this is when traffic interruption and packet loss takeplace. By exploiting both the distributed nature of the ring-based
RAN architecture and the supporting control plane, the proposedarchitecture enables the support of seamless and speedy interBS Hos in which, as the simulation results will show, packet loss
is almost totally avoided and VoIP and other real-time IP
applications can be adequately supported during HO. This isaccomplished as follows:
1) When a UE enters a domain served by the PON-RAN, it
needs to register itself to the domain OLT’s access router andupdates the new location in its home subscriber server (HSS).
As long as the UE is roaming within the same PON-RAN
domain, it does not need to reregister again.
2) The physical connectivity of both the SBS and TBS
attached to the ring allows direct data exchange andintercommunications among them during HO (compare thesimplicity and reduced latency and signaling overhead of thisdirect approach versus that of the typical 4G indirect
bidirectional lengthy intercommunications and logicalconnectivity among the SBS and TBS via the MPC). Thus, once
the TBS accepts the HO command, the SBS may immediately
start to forward the buffered data (which have not yet beensuccessfully sent to the UE), to the TBS directly on the ring as
local LAN traffic. This is significant for creating the typical 4G
logical connectivity among the SBS and TBS, which requires
the lengthy process of signaling to the EPC to coordinate themobility-tunnel set up/switch from the SBS to TBS (and viceversa) via the MPC, is totally avoided as well as the direct
participation of the SGW/OLT.
3) For the HO to complete, the TBS signals the OLT/SGW toinform it that the HO is complete and to update its records with
the new TBS, i.e., to add TBS (and corresponding target ONU(TONU) that is collocated or attached with/to the TBS) to theforwarding list for the UE. Then, under the typical 4G/5G RAN
scenario, to resume normal operation and forward DS traffic to
the TBS (or UE), the typical lengthy process of setting up amobility tunnel form the MPC to the TBS is essential. Under the
proposed PON-based RAN architecture, however, the scheduler
at the OLT just simply redirects the UE’s DS traffic from the DS
queue that was serving the SONU/SBS before the HO (the OLT
houses N dedicated DS queues, each serving one of the N

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

ONUs-BSs attached to the ring) to the new DS queue that is now
serving the TONU/TBS. The HO can happen from 4G to 5G andvice versa. This proposed setup supports and enhances
performance. To further reduce the signaling latency and packet
loss during the HO, the OLT may concurrently broadcast DS
traffic destined to the UE to both the SBS and TBS.
Overall, the proposed EPON-based RAN architecture
introduces several significant advantages over typical 5G/LTERAN and the advantages include: 1) a significant reduction in
the signaling overhead and handoff latency; 2) offloading asizable fraction of the local mobile sessions switch/set-up andtear-down and associated lengthy and complex signaling
processing from the overloaded MPC to the RAN’s accessnodes; 3) re-registration procedures to the HSS when the UE
moves from a BS to another is avoided as long as the UE roams
within the coverage area served by the BSs attached to the ring;4) during inter-BSs HOs, no path switch/setup command isneeded since the path (mobility tunnels) from MPC to the UE
remains unchanged.
V.

PERFORMANCE EVALUATIONIn this section, we first compare the performance of the
proposed EPON-based mobile 5G RAN with that of thetypically centralized 5G RAN. Two simulation programs weredeveloped using MATLAB, one for the typical 5G RAN and theother one for the EPON-based RAN. The performance metrics
used here are network utilization, average throughput, and Endto-End (ETE) delay. We consider the practical case of nonuniform traffic load in which, during a given period, some BSs
might be lightly loaded/idle, while other BSs might be heavily
loaded. At a given total network load, different BSs havedifferent average traffic loads. Under this non-uniform trafficload scenario, the significance of utilizing PON-based RAN
architecture is established.
The following are the system parameters used for simulating
the EPON-based RAN architecture: (1) a PON with 16 ONUs,
each serving a varying number of BSs (a minimum of one BS toa maximum of 10 BSs), depending on the varying traffic load.;(2) aggregate access link data rate from the UEs to a given ONU
is 100 Mb/s; (3) the RAN DS line rate (from the
OLT/SGW(ePC) to the ONUs/BSs) is assumed to be same as the
US line rate (from the ONUS/BSs to the OLT/SGW) and isequal to 1 Gb/s; (4) the average distance between the
OLT/SGW(ePC) and ONS/BSs is 20 km; (5) the buffer size ineach ONU/BS is 1 Mbyte; (6) the maximum EPON cycle timeis 2 ms for US transmission, while a standard fixed periodic
cycle of 10 ms is assumed for 5G US transmission (from the UEs
to the BS); (7) the IEEE 802.3ah MPCP REPORT/GATE
message is 64 bytes; (8) we assume that all network traffic is justmobile traffic initiated by 5G UEs, i.e., traditional EPON’s
fixed wired end-user’s traffic is assumed to be zero; (9) the total
mobile traffic is divided equally among US mobile traffic andlocal mobile LAN; (10) the mobile traffic model used here is as
described above in Section III B, we assume that 5G/LTE GBR

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queues are mapped into EPON P0 queue and Non-GBR queues
into P1 and P2 based on QoS parameters setting.; (12) the DBA
scheme reported in [12] is used here to provision EPON US
traffic, whereas the proportional fairness algorithm is used toprovision 5G US traffic.
To have a fair comparison, all EPON-based RAN parameterslisted above are also used for simulating the typical 5G except
for the following: each and every dedicated link data rate of thetypical 5G RAN in either US (16 dedicated point-to-point links
between the ONUs/BSs and the OLT/SGW) or DS (16 dedicatedpoint-to-point links between the OLT/SGW and the ONUs/BSs)
direction is set to 62.5Mbps. Thus, the aggregated link data ratein either direction is: 62.5Mbps * 16 = 1 Gbps, which is equal
to that of the EPON-based RAN.
Table I shows the possible scenarios of four unevenly loaded
Base Stations i.e., ligtly loaded, moderately loaded, heavy
loaded and super heavily loaded BS’s. Similarly Fig.s 4 and 5
show the uplink utilization versus time at a given single network
load of 0.83 and versus the total network load for unevenly andevenly BSs respectively. This is for both the typical 5G andEPON ring-based RAN architectures.
Although traditional 5G shows on average similar
performance in evenly loaded BSs as in Fig. 5. nonetheless Fig.
4 demonstrates that EPON ring-based RAN has a much higher
level of usefulness as well as stability with less variation withtime compared to typical 5G. This enhances the network’s
stability and predictability. Fig. 6 shows that the average uplinkthroughput EPON-based RAN architecture is much higher than
the typical 5G RAN architecture at a higher total network load.
Table I:UNEVENLY LOADED BASE STATIONS SCENARIOS
Heavily Loaded BSs

Lightly Loaded BSs

BSs

BS LoadBSs

BS Load

Total Network
Load1

0.848815

0.10510.24

30.8488130.10510.39

50.8488110.1051

0.54

Super Heavily Loaded BSs

Moderately Loaded BSs

BSs

BS LoadBSs

BS Load

Total Network
Load

1

1.85

15

0.3262

0.68

2

1.85

14

0.3262

0.83

3

1.85

13

0.3262

0.98

4

1.85

12

0.3262

1.13

5

1.85

11

0.3262

1.28

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1371

Typical 5G N-GBR
Typical 5G N-GBR

Ring-based 5G N-GBR

Typical 5G GBR
Ring-based 5G GBR

Fig. 4. Uplink Utilization Time Series at Network Load = 0.83

Fig. 7. Average network ETE delay for GBR and Non-GBR traffic

It can be observed from the Fig. 7. that at a lower load, thetypical 5G has a lower delay than the Ring-based RAN. Thisis expected as typical 5G traffic utilizes dedicated point-topoint links and the queuing delay are still almost zero.
However at higher traffic load, as expected, the Ring-based
5G RAN exhibits much lower delay.

Fig. 5. Uplink Utilization Time Series of Evenly loaded BSs at Network Load
= 0.83Fig. 6. Uplink Ave Throughput

Fig. 7. shows the ETE network delay for both Guaranteed
Bit Rate (GBR) and Non-Guaranteed Bit Rate (N-GBR) traffic
for both the typical 5G and Ring-based RAN architectures.

A. Handover scenario
One significant benefit of using EPON ring-based 5G
network would be less handover delay and packet drop. To
validate this, we use a scenario where UE node moves from its
Home Agent BS 4 to Foreign Agent BS 5. Uni-directional best
effort application traffic is conFig.d between UE and the serverat the rate of 64 Kbps. UE from BS 4 has trajectory that starts
moving around 110 seconds. Its movement converges to BS 5
between 115 to 120 seconds. Same scenario is set up for bothtraditional 5G backhaul and 5G BS’s connected to ring
network. Parameters collected for comparison are the traffic
received/dropped and handover delay vs. time. Handover delay
is computed from the time the User Equipment sends a Handoff
REQ message starting the handoff process until initial ranging
with the Fig. 8. shows the throughput versus time for a UE
during HO when moving away from the SBS attached to ONU1
and approaching the TBS attached to a neighboring ONU 2 forboth the typical 5G and EPON ring-based RAN architectures.
A unidirectional BE application traffic is conFig.d between
UE and the server at the rate of 64 Kbps. The UE has trajectorythat starts moving around 110 seconds and converges to theTBS between 115 to 120 seconds. Same scenario is set up forboth traditional 5G and EPON-based RAN. Parameterscollected for comparison are the traffic received/dropped andHO latency. HO latency is computed from the time the UE
sends a HO request message to initiate the HO process until
initial ranging with the TBS is successfully completed. As

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

expected, EPON-based RAN show lower HO latency (15 ms
versus 20 ms) as shown in Fig. 9. and almost no packets drop
as compared to typical 5G.new Serving BS successfullycompleted.

Fig. 8. Traffic Throughput during UE handoff

Fig. 9. UE Handover Delay

VI.

CONCLUSION AND FUTURE WORKThis study presents a simple and cost effective PON-based 5G
mobile backhaul RAN architecture supporting several key
salient networking features that collectively contribute towards
significant enhancement of the performance of both the RAN
and MPC in terms of handoff capability, overall network
throughput and latency, and QoS support. We quantify andcompare between the merits of utilizing a distributed EPONbased 5G/4G RAN architecture and that of traditional LTE
backhaul infrastructure. Some of the key technical requirements
associated with devising a truly unified fixed-mobile 5G/ LTE
access transport architecture that is built on top of a typically
centralized PON infrastructure are also outlined in the work. For
future work we will like to test the performance of the networkwith higher data rates, perform packet loss and power budget
analysis for increased number of ONUs connected in the ring.

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REFERENCES[1]

UMTS Evolution, from 3GPP Release 7 to Release 8, HSPA andSAE/LTE, June 2008 UPDATE, www.3gamericans.org.
[2] 3GPP TS 23.401 “GPRS Enhancements for E-UTRAN access”, October
2007.
[3] H. Ekstrom, “QoS control in the 3GPP Evolved Packet system,” IEEE
Communication Magazine, 47(2):76-83, 2009.
[4] IEEE 802.6e-2005, “Air interface for fixed and mobile broadband
wireless access systems,”[5] S. Sarkar, S. Dixit, and B. Mukherjee, “Hybrid Wireless-Optical
Broadband-Access Network (WOBAN): A Review of Relevant
Challenges,” IEEE/OSA Journal of Lightwave Technology, 25(11):33293340, 2007.
[6] Kun Yang, et al., “Convergence of Ethernet PON and IEEE 802.16
Broadband Access Networks and its QoS-aware Dynamic Bandwidth
Allocation Scheme,” IEEE J. Selected Areas in Comm., 27(2):101-116,
2009[7] G. Shen, R. S. Tucker, and C-J. Chae, “Fixed Mobile Convergence
Architectures for Broadband Access: Integration of EPON and WiMAX”,
IEEE Com. Mag., 45(8):44-50, 2007.
[8] N. Ghazisaidi, M. Maier, and C. M. Assi, “Fiber-Wireless (FiWi) AccessNetworks: A Survey,” IEEE Communications Magazine, 47(2):160-167,
Feb. 2009[9] 5G PPP Architecture Group, “View on 5G Architecture: https://5gppp.eu/wp-content/uploads/2019/07/5G-PPP-5G-Architecture-WhitePaper_v3.0_PublicConsultation.pdf, June 2019.
[10] 3GPP Study on scenarios and requirements for next generation accesstechnologies, Technical report, TR 38.913 (Release 14), October 2016.
[11] Gupta, A. et al., “A survey of 5G networks: Architecture and emerging
technologies” IEEE Access, vol 3, 1206– 1232, 2015[12] A. Delowar, et al., “Ring-Based Local Access PON Architecture for
Supporting Private Networking Capability,” OSA Journal of Optical
Networking, 5(1):26-39, 2006.
[13] IEEE 802.3 Ethernet in the First Mile Study Group,
http://www.ieee802.org/3/ efm/ public/ index.html
Syed Rashid Zaidi received the M.S., M.Phil.
and Ph.D. degrees in Electrical Engineering from
City University of New York, NY, USA. He iscurrently Assistant Professor & Program Directorof Cybersecurity & Networking Technology andElectronic Engineering Technology in The
Department of Engineering, Physics &Technology of the Bronx Community College of
The City University of New York. His research areas are Fiber Optics
Communications, LTE, WiMAX, 5G, and next generation wireless networks
and cybersecurity. He has numerous peer-reviewed publications, invited talks,conference presentations and received numerous awards, a recent one is aprestigious grant award from the U.S. Department of Education to update the
Cybersecurity program and build the latest industrial-standard lab.
Ajaz Sana received the M.S., M.Phil. and Ph.D.
degrees in Electrical Engineering from City
University of, New York, NY, USA. He iscurrently an Assistant Professor in The
Department of Engineering, Physics &Technology of Bronx Community College of The
City University of New York. His research areas
are Free Space Optics, WiMAX, and next
generation wireless networks. He has many peerreviewed research publications.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1373

Shahab Hussain received his M.S., M.Phil., andPh.D. degrees in Electrical Engineering from The
City University of New York, USA. He is working
as Senior Professional in the Mobile Networks
Division of Nokia. His research areas are LTE,
PON, and next generation (5G) wireless networks.
Dr. Hussain has over 20 years of experience in
Research & Development and Wireless Services
Delivery with a specialty in Testing, Architecture
Design, Integration, Performance of Wireless Networks, Business
Development, and Operations Support Competency. Dr. Hussain is also
serving as an Adjunct Professor of Engineering in the Department of STEM
of North Shore Community College in Massachusetts.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1374

A Method for Controlling Scan Rate
Based on Estimated Retransmission Rateof Background Trafﬁc
Kenta SUZUKI∗ , Takuya KURIHARA∗ , Kazuto YANO∗ , Yoshinori SUZUKI∗
Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan.
∗ Email: {kenta-suzuki, tkurihara, kzyano, yoshinori.suzuki}@atr.jp
Abstract—For efﬁcient network scan to narrow-band wirelessnetworks, this paper proposes a method for controlling a scanrate based on the estimated retransmission rate of background
trafﬁc from a scan response delay obtained at a scan rate. This
proposed method is aiming to select the highest scan rate that cankeep the estimated retransmission rate of the background trafﬁc
below a predetermined retransmission threshold. First, throughcomputer simulations considering a Wi-SUN sensor network withtwo different network situations and multiple scan rates, wederive a regression function between the mean of scan responsedelay and COR (Channel Occupation Rate) of the background
trafﬁc, and that between the COR and the retransmission rateof the background trafﬁc, for each the network situation and thescan rate using the least-squares method. Then, we propose amethod for estimating the retransmission rate of the background
trafﬁc at a different scan rate using three kinds of estimators;
network situation estimator, COR estimator, and retransmission
rate estimator. After that, we propose a control scheme ofscan rate based on the estimated retransmission rate of the
background trafﬁc. We evaluate the estimation accuracy of theretransmission rate of the background trafﬁc using the proposed
estimation method. We conﬁrm that the proposed estimation
method can estimate the retransmission rate of the target networkwith the average error lower than 0.058 regardless of the situationof the target network and the scan rate. Moreover, we evaluate
the performance of scan rate control using proposed method.
We conﬁrm that proposed method can scan faster by 0.18 pps
and decrease the retransmission rate of the background trafﬁc
by 0.015 compared with a ﬁxed scan rate.
Index Terms—Network scan, Wi-SUN, Scan rate control, QoS
estimation, Network simulation
Manuscript received on +BO. 5, 2021. This work is a result of the project
entitled “Research and Development of Technologies of Efﬁcient Network
Scanning for Wide-Area IoT Wireless Networks,” which is supported by theMinistry of Internal Affairs and Communications as part of the research
program “R&D for Expansion of Radio Wave Resources (JPJ000254).” This
paper is a follow-up of the invited journal to the presented paper entitled “A
StudyonEstimationofRetransmissionRateofBackgroundTrafﬁcforVarious
ScanRateswithScanResponseDelay”ofthe23thInternationalConferenceonAdvancedCommunicationTechnology(ICACT2021).
Kenta Suzuki is with the Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International, Japan. (correspondingauthor, email: kenta-suzuki@atr.jp)
Takuya Kurihara is with the Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International, Japan. (email: tkurihara@atr.jp)
Kazuto Yano is with the Wave Engineering Laboratories, Advanced Telecommunications Research Institute International, Japan. (email:
kzyano@atr.jp)
Yoshinori Suzuki is with the Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International, Japan. (email: yoshinori.suzuki@atr.jp)

I. I NTRODUCTIONR

ECENTLY, the number of IoT (Internet of Things)
wireless devices has been increasing dramatically, andvulnerable IoT devices for cyber-attacks are also increasing
rapidly. Attacked IoT devices can be used as a steppingstone by attackers and are used as perpetrators of DDoS
attacks. Such devices transmit a large number of packetsof unauthorized trafﬁc to disturb trafﬁc which is originallytransmitted in their network; hereafter, we call this original
trafﬁc as background trafﬁc. In order to ﬁnd such vulnerable
IoT devices, network scan toward a wide area of the Internet
is useful.
In existing applications of network scan for a wide areaof the Internet [1], a scanner makes the network scan at ahigh rate to ﬁnish the scan quickly. However, when making ascan toward wireless networks with a small network capacity
like LPWA (Low Power, Wide Area) networks such as WiSUN [2] and LoRa [3], these networks are easily congested.
Consequently, QoS (Quality-of-Service) such as transmission
delay will be degraded due to collide and retransmit packets[4].
In order to keep the QoS degradation of the background
trafﬁc at an allowable level, it is necessary to select the highest
scan rate that can keep a balance between the processing
time of the network scan and the QoS degradation of the
background trafﬁc. Examples of QoS are throughput and delayof background trafﬁc observed in application layer. In thispaper, we focus on the retransmission rate of the background
trafﬁc in MAC (Medium Access Control) layer which causes
these QoS degradations. For realizing this control, the scannerneeds to know the retransmission rate of the background trafﬁcin the target network.
However, the scanner cannot directly know the QoS in
the target network if it scans from the outside of the target
network. The retransmission rate of the background trafﬁc
depends on the situation of the target network. For example,
the retransmission rate of the background trafﬁc increases as
the mean of the scan response delay or scan rate increases [5].
This is because that collision is occurred by congestion of thetarget network and retransmission is triggered by the collision.
Therefore, the scanner needs to estimate the retransmission
rate of the background trafﬁc which is different for each target

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

network by using information which can be observed at thescanner such as scan response delay.
As related works for estimating the situation of network, amethod for estimating the number of users in IEEE 802.11
network based on collision probability obtained through ananalysis of state transition of CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) is proposed by [6].
Moreover, a method for estimating the number of users using a
PDF (Probability Density Function) of transmission deferring
time in the backoff algorithm of IEEE 802.11 and collision
probability obtained through simulations is proposed by [7].
These methods aim to grasp the network situation using
the collision information which is obtained from the target
network directly.
As other related work, estimating the available bandwidth
in IEEE 802.11-based multi-hop wireless networks using thenumber of retransmission is proposed by [8]. By this method,
the number of retransmissions is directly obtained from thetarget networks. Although there are various related works asmentioned above, it is difﬁcult to apply these methods toestimate the retransmission rate of background trafﬁc at thescanner. Therefore, a method for estimating the retransmission
rate of background trafﬁc at the scanner existing out of thetarget network is required.
In order to estimate the retransmission rate of the background trafﬁc via Internet, we have proposed a method for estimating the retransmission rate of the background trafﬁc fromthe scan response delay [9]. The proposed method consists of
two kinds of estimators. The ﬁrst one estimates the situation of
the target network from the distribution of the scan responsedelay. The second one estimates the retransmission rate of the
background trafﬁc in the target network from the mean ofscan response delay and the regression function between themean of scan response delay and the retransmission rate of
the background trafﬁc corresponding to the estimated network
situation. The regression function is empirically obtained forseveral network situations through simulations assuming a WiSUN PAN (Personal Area Network).
For realizing the control of the scan rate, we need todetermine the proper scan rate from the current scan rate
and obtained scan response delay. Therefore, we also proposed a method for estimating the retransmission rate of the
background trafﬁc at a different scan rate using three kindsof estimators; network situation estimator, COR (Channel
Occupation Rate) estimator, and retransmission rate estimator[10].
This paper shows a method for controlling the scan rate
based on the retransmission rate of the background trafﬁc
estimated from the scan response delay using the estimators
proposed in [10]. First, the followings are conﬁrmed throughempirical analysis using network simulation assuming various
situations with different number of terminals or the transmission interval of the background trafﬁc.
1) The relationship between the scan response delay andthe transmission interval of the background trafﬁc canbe expressed by a regression function.

13752) The relationship between the transmission interval of
the background trafﬁc and the retransmission rate of the
background trafﬁc can also be expressed by a regressionfunction.
3) The shape of the regression function is different depending on the situation of the target network and the scanrate.
Next, based on the above empirical analysis, we propose a method for estimating the retransmission rate of the
background trafﬁc for various scan rates different from thecurrent scan rate and the scan response delay. The proposed
estimation method consists of three kinds of estimators. The
ﬁrst one estimates the situation of the target network fromthe distribution of the scan response delay. The second one
estimates the COR of the background trafﬁc from the meanof the scan response delay, the scan rate at which the scan
response delay is obtained, and the estimated situation of thetarget network. Here, the COR of the background trafﬁc isused instead of the transmission interval of the background
trafﬁc. The third one estimates the retransmission rate of the
background trafﬁc at the target scan rate from the estimated
COR of the background trafﬁc and the estimated situation of
the target network.
Finally, we propose a control scheme of the scan rate
based on the estimated retransmission rate of the background
trafﬁc. The proposed method selects the highest scan rate thatcan keep the estimated retransmission rate of the background
trafﬁc below a predetermined retransmission threshold. The
performance of the proposed method is also evaluated throughcomputer simulations.

II. S ITUATION OF TARGET NETWORKIn general, a situation of a network varies by time of day
such as morning, work time, or midnight. In addition, how
the network situation varies depends on the application run in
the network or network topology. Therefore, it is necessary toanalyze the statistical characteristics of the retransmission of
the background trafﬁc in various situations of target networks.
In the empirical analysis, we consider a sensor networkthat consists of Wi-SUN devices. Examples of the factor ofdetermining the network situation are the number of terminals,
the transmission interval, frame length, and communication
direction. In this paper, we focus on the number of terminals
and the transmission interval because these factors affect thecongestion situation of the background trafﬁc signiﬁcantly.
As shown in Fig. 1, we vary the situation of the target network by changing the transmission interval of the background
trafﬁc (Case 1), or the number of terminals which transmit
the background trafﬁc (Case 2). The background trafﬁc istransmitted from terminals to their application server. We
assume that the interval of situation change is much longer
than the transmission interval of the background trafﬁc.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

6FDQQHUTABLE I
Baseline parameters for entire simulation.

$SSOLFDWLRQVHUYHUCommon6FDQWUDIILF

Overall
Wi-SUN system%DFNJURXQGWUDIILF,QWHUQHW&DVH 

&DVH

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7HUPLQDOHJVHQVRU

Scan trafﬁc&KDQJLQJWUDIILF
LQWHUYDO&KDQJLQJ
QXPEHU
RIWHUPLQDOVBackground trafﬁc«

Case 1

Background trafﬁc(Situation index 1)

Case 2

Background trafﬁc(Situation index 2)

Fig. 1: Example of situations of target network (without scan
trafﬁc).

III. E MPIRICAL ANALYSIS OF RELATIONSHIP BETWEENTHE SCAN RESPONSE DELAY AND RETRANSMISSION RATEnak · xk

100 sec
100
200 kHz50 kbps
50 kbps
0.16 us
1 ms
1.16 ms
3
5
1
1–10 pps
12.8 ms
UL
27.84 ms
1
60–180 ms
180 ms
150 ms
120 ms
90 ms
60 ms
1–5
1
2
3
4
5
180 ms

We conduct empirical analysis of the relationship between
the scan response delay and the retransmission rate of the
background trafﬁc through computer simulations, assuming
the system model described in the previous section. We try to
express the relationship by a polynomial regression function
given by Eq. (1).

Simulation time
Number of trials
Bandwidth
Phy communication rate
Symbol duration
CCA duration
Turnaround time
Unit of backoff period
Minimum backoff exponent
Maximum backoff exponent
Number of terminals
Scan rate
Frame length
Communication direction
Frame length
Number of terminals
Transmission interval
Conﬁguration index 1
Conﬁguration index 2
Conﬁguration index 3
Conﬁguration index 4
Conﬁguration index 5
Number of terminals
Conﬁguration index 1
Conﬁguration index 2
Conﬁguration index 3
Conﬁguration index 4
Conﬁguration index 5
Transmission interval

OF BACKGROUND TRAFFIC

p(x) =

1376

(1)

k=0

where n is the regression order of the least-squares method,
and ak is the coefﬁcient for the k-th regression order thatminimizes the squared error.
In order to determine the coefﬁcients in Eq. (1), we ﬁrst
evaluate the scan response delay, the transmission intervalof the background trafﬁc, and the retransmission rate of
the background trafﬁc for various scan rates and network
situations. Here, we assume that the packet length of the
background trafﬁc is ﬁxed, and thus we use the COR of
the background trafﬁc instead of its transmission interval. We
simulate two different network situations by ns-3 [11]. Table I
shows the baseline parameters for the entire simulation in thispaper. As a network topology, there are at least two terminals(for the background trafﬁc source(s) and for the scan trafﬁc)that are connected to the same Wi-SUN gateway. The Wi-SUN
gateway is connected to the scanner by wired LAN. The scanrate changes in a range of 1–10 pps (packet per second).
In the network situation corresponding to Case 1 in Fig. 1,
we assume that there is one terminal that transmits the
background trafﬁc, and its transmission interval is changed
from 60 to 180 ms with a 30 ms step. In the network situation
corresponding to Case 2 in Fig. 1, we assume that there areseveral terminals for transmitting the background trafﬁc with(a) Case 1(b) Case 2

Fig. 2: Regression function of scan response delay vs. COR
of background trafﬁc [10].

a ﬁxed transmission interval of 180 ms, and the number ofterminals is changed from one to ﬁve.
A. Relationship between scan response delay and COR ofbackground trafﬁc
In order to determine the regression order of the regressionfunction, we draw regression curves for different regression
orders from 5 points of the average scan response delay and theaverage COR of the background trafﬁc with different networks
situations, i.e. the values of the transmission interval in Case 1or the number of terminals in Case 2.
Figure 2 shows the regression functions derived for different
regression order n. When both the network situation and trafﬁc
conﬁguration are same, the COR of the background trafﬁc isalso same regardless of the scan rate. The mean of the scan
response delay of the higher scan rate (9 pps) is higher than

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

1377

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V

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021  

Fig. 5: Variance of scan response delay [9].

(a) Case 1>QXPEHURIUHWUDQVPLVVLRQVSDFNHW@>QXPEHURIUHWUDQVPLVVLRQVSDFNHW@

Fig. 3: Fitting accuracy of each regression order [10].

(b) Case 2(a) Case 1(b) Case 2

Fig. 4: Regression function of COR of background trafﬁc vs.
retransmission rate of background trafﬁc [10].

Fig. 6: Regression function of scan response delay vs. itsvariance [9].

that of the lower scan rate (3 pps). We can also ﬁnd that theshapes of curves are different between two network situations.
Figure 3 shows the ﬁtting accuracy of the regression function for different regression orders and network situations. The
ﬁtting accuracy is calculated as the absolute error between theﬁtting data and the actual simulation result. Since regression
order 3 shows the best performance, we use it for the subsequent evaluation.

Figure 5 shows the variance of the scan response delay
versus the situation of the target network at scan rates of 3 pps
and 9 pps. The variance of the scan response delay becomeslarger as the amount of the background trafﬁc or the scanrate increases. Therefore, the relationship between the scan
response delay and its variance can also be derived with aregression function.
Figure 6 shows the regression functions for the mean of scan
response and its variance. We draw a regression curve from 5(average scan response delay, variance of scan responsedelay) points for each network situation in the same way
as in Section III-A. Since the shapes of curves are different
between two network situations, the network situation can be
distinguished by the relationship between the mean of scan
response and its variance. Therefore, in order to estimate thesituation of the target network, we use the regression functions
for the mean of scan response and its variance.

B. Relationship in background trafﬁc between COR and retransmission rate
Figure 4 shows the regression functions for the COR of the
background trafﬁc and the retransmission rate of the background trafﬁc. We draw a regression curve from 5 (average
COR of background traf f ic, average retransmission
rate of background traf f ic) points in the same way as in
Section III-A for each network situation. Since the shapes ofcurves are different between two network situations, we use
these regression functions for the estimation of the situationof the target network.
C. Distribution of scan response delayThis section summarizes the previous research [9] for estimating the network situation in the target network conﬁrmed
the relationship between the scan response delay and its
variance through empirical analysis of the distribution of thescan response delay.

IV. E STIMATION OF RETRANSMISSION RATE OFBACKGROUND TRAFFIC

Based on the empirical analysis described in Section III,
we propose a method for estimating the retransmission rateof the background trafﬁc at the target scan rate from a scan
response delay obtained at a different scan rate. Figure 7
shows the block diagram of the proposed method that employs
three kinds of estimators . The ﬁrst estimator E S estimates

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021


Networksituation
estimator̅,   , 

1378

The COR ˆ of the background trafﬁc is estimated from thefollowing equation.



COR
estimator
̅


̅


C
ˆ = Eî,α(δ̄) =

̂

,

Retransmission
rate estimator, ̂



Fig. 7: Block diagram of proposed method.

the situation î of the target network; hereafter, we call thisestimator as network situation estimator. The second estimator
E C estimates the COR of the background trafﬁc in the target
network; hereafter, we call this estimator as COR estimator.
The third estimator E R estimates the retransmission rate of the
background trafﬁc in the target network; hereafter, we call thisestimator as retransmission rate estimator. Each estimator ofis trained for each cases i.e. Case 1 or Case 2 mentioned
in previous section. Therefore, the function E of the proposed
estimation and its output ρ̂ are described as following equation.
RCρ̂ = E(δ̄, σ 2 , α, β) = Eî,β(Eî,α(δ̄))(2)

î = E S (δ̄, σ 2 , α)(3)where δ̄ is the mean of the scan response delay observed at
the scanner, σ 2 is the variance of the scan response delay, α
is the scan rate when the scan response delay is observed, βis the target scan rate for estimating retransmission rate of the
background trafﬁc, and ρ̂ is the estimated retransmission rateof the background trafﬁc at β.
In network situation estimator E S , the network situationindex î is estimated by ﬁnding the situation that gives theminimum distance of the variance of the scan response delay
between the observed and estimated values. The observed
variance is calculated from the scan response delay observedat the scanner. For each situation of the target network, theestimated variance is calculated from the mean of the scan
response delay by using the regression functions for the meanof the scan response delay and its variance.
The index î of the situation of the target network is
estimated from the following equation.
î = E S (δ̄, σ 2 , α) = arg min |σ 2 − Vi,α (δ̄)|i(4)

where Vi,α is the regression function for the mean of the scan
response delay and its variance shown in Fig. 6.
COR estimator E C estimates the COR ˆ of the background
trafﬁc from the mean δ̄ of the scan response delay by using theregression function for the mean of the scan response delay
and the COR of the background trafﬁc, for the situation index
i and the scan rate α.

3
l=0

aC
· δ̄ ll,î,α(5)where aCis the coefﬁcient for the l-th regression order thatl,î,αminimizes the squared error for the estimated situation indexî
Retransmission rate estimator E R estimates the retransmission rate ρ̂ from the COR ˆ of the background trafﬁc by using
the regression function for the COR and the retransmission
rate of the background trafﬁc, for the situation index i and thescan rate β.
The retransmission rate ρ̂ is estimated from the following
equation.
Rρ̂ = Eî,β
(ˆ
) =

3

l=0

aR
· ˆl
l,î,β(6)where aRis the coefﬁcient for the l-th regression order thatl,î,βminimizes the squared error for the estimated situation indexî.
V. C ONTROL OF SCAN RATE BASED ON ESTIMATEDRETRANSMISSION RATE OF BACKGROUND TRAFFIC

We propose a method for controlling the scan rate based on
the estimated retransmission rate of the background trafﬁc byusing the proposed estimation method described in Section IV.
Figure 8 and Table II show a processing image and notations of
the proposed method, respectively. First, the proposed method
requires following information; the observed scan responsedelay ( ), the scan rate (α ∈ ) for observing the scan
response delay, the candidate scan rates for estimation target( ), and the retransmission rate threshold (θ). Second, theestimated retransmission rate (ρ̂k ) of the background trafﬁcfor each candidate scan rate (Bk ∈ ) is estimated by using
the proposed estimators ( ) which are trained with cases (theset of the indexes is deﬁned by ). Finally, the highest scanrate (α ∈ ) that satisﬁed the condition ρ̂k ≤ θ is calcurated.
TABLE II
Notations.
ijθkα (∈ )α (∈ )
ρkˆ·Case index of network situation.
Conﬁguration index of network situation.
Retransmission rate threshold.
Trained cases for proposed estimators.
Proposed estimators trained with .
Candidate scan rates for estimation target.
Scan rate index of .
Scan rate when observed scan response delay.
Scan rate controlled by proposed method.
Samples of scan response delay when using α.
Retransmission rate at Bk .
A value estimated by using proposed method.

Figure 9 shows the scanning ﬂow of the proposed method.
First, the scanner is initialized by setting θ, α, , and . After
that, the scanner makes a scan using α in phase 2n of the n-th
trial. Here, n is a natural number. During phase 2n, the scan

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1379

Scan responsedelay [ms]Input : 䞉 Observed scan response delay ( ). 䞉 Candidate scan rates for䞉 Retransmission rate
threshold ().
estimation target ( ).
䞉 Scan rate for observing
the scan response delay ( ∈ ).
Index () Scan rate (B ) [pps] = 0.1
Target samples
1
50

time

2

10

3

2

4

1

Estimation : 䞉 Estimate retransmission rate (௞ ) for each scan rate B௞
using estimators ( ) which are learned with one or more cases ( ).
Index ()Scan rate (B ) [pps]Retransmission rate (ߩො )

1

50

2.0

2

10

0.4

3

2

0.05

4

1

0.01

Output : 䞉 The highest scan rate ( ᇱ ∈ ) that satisfied the condition ௞.

Fig. 8: Processing image of proposed method.

(1) Set , , ,System…
initialization

2 Scan using 

5 Scan using  ᇱPhase 2nPhase (2n+1)

3 Observe
4 Determine ′…
timen-th trial

Fig. 9: Scanning ﬂow of proposed method.

response delay
are observed at the scanner. At the end ofphase 2n, the scanner determines α by using the proposed
algorithm shown in Fig. 10. Finally, the scanner makes a scanusing α during phase (2n + 1). In this paper, we assume thatthe situation of the target network is stable from phase 2n tophase (2n + 1). Moreover, we assume that the scanner can get
one or more samples of the scan response delay in each phase.
Figure 10 shows the proposed algorithm for controlling thescan rate based on the estimated retransmission rate of the
background trafﬁc. First, the average δ̄ and the variance σ 2 ofthe scan response delay are calculated from the observed scan
response delay . After that, the retransmission rate ρ̂k of the
background trafﬁc for each candidate scan rates is estimatedby using the proposed estimators in Fig. 7. While estimating
the retransmission rate of the background trafﬁc, β is added
into the set of the candidate scan rates  when satisﬁed
the condition ρ̂k ≤ θ. Finally, the controlled scan rate α iscalculated by getting the maximum scan rate in
not empty. Otherwise, the minimum scan rate inα .

if  isis set to

VI. P ERFORMANCE EVALUATION OF PROPOSED METHOD
In this section, we evaluate the proposed method by thefollowing steps. First, we evaluate the estimation accuracyof the retransmission rate of the proposed estimators. Next,
we evaluate the performance of the scan rate control ofthe proposed algorithm. Finally, through analysis of theseevaluation results, we conﬁrm that proposed method can scanfaster and improve the retransmission rate of the background
trafﬁc compared with a ﬁxed scan rate.
A. Estimation accuracy of the retransmission rate
We evaluate the estimation accuracy of the retransmission
rate of the proposed method. We calculate the estimation error
γ between an actual value γA and the estimated one γE by
the following equation.
γ = |γE − γA |.

(7)

Figure 11 shows the true positive rate (TPR) of the situation
estimation as the performance of network situation estimator,
which had evaluated in previous research [9]. The TPR of
Case 1 is 55% on average, and the TPR of Case 2 is 67%
on average. Therefore, network situation estimator sometimes
selects the wrong situation.
Figure 12 shows the average estimation error of the CORof the background trafﬁc for different training cases. When

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1380

Start

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VLWXDWLRQHVWLPDWLRQCalculate the average ̅ and its variance from the observed scan response delay .

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= B
  E ̅,

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Fig. 10: Proposed algorithm.
(b) Case 2

estimating the retransmission rate in Case 1, the estimator
trained both for Cases 1 and 2 can decrease the estimation error
by 0.003 from that of the estimator trained only for Case 2(i.e., the mismatched training case). On the other hand, whenestimating the retransmission rate in Case 2, the estimator
trained for both Cases 1 and 2 can decrease the estimation error
by 0.015 from that of the estimator trained only for Case 1 (i.e.,the mismatched training case).
Figure 13 shows the average estimation error of the retransmission rate of the background trafﬁc for different trainingcases. When estimating the retransmission rate in Case 1, theestimator trained both for Cases 1 and 2 can decrease theestimation error by 0.004 from that of the estimator trained
only for Case 2 (i.e., the mismatched training case). On theother hand, when estimating the retransmission rate in Case 2,
the estimator trained for both Cases 1 and 2 can decrease theestimation error by 0.009 from that of the estimator trained
only for Case 1 (i.e., the mismatched training case).
From Figs. 12 and 13, we can ﬁnd that COR estimatorand retransmission rate estimator trained both for Cases 1
and 2 can improve the estimation accuracy compared withthe mismatched training case. On the other hand, comparedwith the estimator trained for the actual situation of the target
network (i.e., the matched training case), the estimator trained

Fig. 11: True positive rate of situation estimation of target
network [9].
for both Cases 1 and 2 degrades the estimation accuracy
because of the imperfect estimation of the situation at network
situation estimator. However, the estimation error of the COR
and that of the retransmission rate are not signiﬁcant comparedwith the matched training case. Therefore, training for bothcases can be a fail-safe approach.
Figure 14 shows the average estimation error of the retransmission rate of the background trafﬁc for each the current
scan rate α and the candidate scan rate β when the estimators
are trained for both Cases 1 and 2. This result shows that the
average error is lower than 0.058 regardless of the scan rates
α and β.
B. Performance of controlling scan rate
We evaluate the performance of the scan rate control. Table III shows the parameter set for the performance evaluation.
In this simulation, the proposed method scans according to thescanning ﬂow explained in the previous section V. Here, thecandidate scan rates are set 1–10 pps. We also evaluate theperformance when a ﬁxed scan rate is used as in existing scan
applications for comparison. In the case, a ﬁxed scan rate α

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

0.06

Case 1Case 20.05

15 GHFUHDVHG

0.04
0.3 GHFUHDVed

0.03
0.02

0.01
0.00

Trained forCase 1

Trained for Trained forCase 2
Cases 1DQG2
COR estimator

Error of retransmission rateof background traffic>QXPEHURIUHWUDQVPLVVLRQVSDFNHW@

Fig. 12: Average estimation error of COR of background trafﬁc
for different estimators [10].

0.05
0.04

Case 1Case 20.03

0.9 GHFUHDVed
0.4 GHFUHDVed>QXPEHURIUHWUDQVPLVVLRQVSDFNHW@

Error of COR of background traffic

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1381

ȕ
ȕ
ȕ
ȕ
ȕ
ȕ
ȕ
ȕ
ȕ
ȕ

Į

Fig. 14: Average estimation error of retransmission rate of
background trafﬁc for different scan rates (α, β) [10].

0.02
0.01
0.00

Trained for Trained for Trained forCase 1Case 2 Cases 1 and 2
Retransmision rate estimator

Fig. 13: Average estimation error of retransmission rate of
background trafﬁc for different estimators [10].
is used from phase 2n to phase (2n + 1) of the scanning ﬂow.
During scanning, we assume that the situation of the target
network is stable in each phase. In addition, the samples ofthe scan response delay observed at the scanner is not empty.
We ran simulation with 10,000 different seeds.
Figure 15 shows the average retransmission rate of the
background trafﬁc for all situations when = {1, 2}. ThisTABLE III
Parameter set for performance evaluation.
Parameter
Case index of network situation (i)Conﬁguration index of network situation (j)Trained casefor proposed estimators ( )Retransmission rate threshold (θ)
Scan rate when observed
scan response delay (α)
Number of observed
scan response delay (N )

Value
[1-2]
[1-5]
[1, 2, {1, 2}][0.05, 0.1, 0.15][1-10][1-100]Fig. 15: Average retransmission rate of background trafﬁc forall situations when = {1, 2}.

result shows that proposed method can converge the averageretransmission rate of the background trafﬁc with the increaseof the number of the observed scan response delay (N ).
When θ = 0.1 and 0.15, the average retransmission rate of
the background trafﬁc can achieve less than θ if at most 10
samples of the observed scan response delay are obtained.
However, when θ = 0.05, the average retransmission rate of
the background trafﬁc dose not achieve θ regardless of N . This
result implies that there is the lower bound of the achievable
retransmission rate because the accidental retransmission of
the background trafﬁc can occur as long as scanning.
Figure 16 shows the average retransmission rate of the
background trafﬁc for defferent situation (i, j) of the target
network when θ = 0.1 and = {1, 2}. When i = 1 (case 1),
θ is satisﬁed regardless of j with the increase of N . On the

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

>QXPEHURIUHWUDQVPLVVLRQVSDFNHW@ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1382

3URSRVHGPHWKRG)L[HGVFDQUDWH([FHHG ̝
RQ IL[HG VFDQ UDWH GHFUHDVHG
ș \ 

6FDQUDWH  
ZKHQREVHUYHGVFDQUHVSRQVHGHOD\>SSV@(b) Case 2 (i = 2)

Fig. 16: Average retransmission rate of background trafﬁc fordifferent situation of target network when θ = 0.1 and ={1, 2}.

other hand, when i = 2 (case 2), θ is not satisﬁed in mostcase of j even if the N increases. Here, the higher value ofj represents the higher congesting level. And also, each j ofcase 2 is congested compared with the same j of case 1 .
Therefore, these results imply that the lower bound of theachievable retransmission rate of case 2 is higher than thatof case 1 because the collision of the background trafﬁc iseasily occurred in case 2 in which there are more terminals
than case 1.
Figures 17 and 18 show the average retransmission rateof the background trafﬁc and controlled scan rate α fordifferent current scan rates α when θ = 0.1, N = 10, and= {1, 2}. Here, each error bar in each graph shows therange of the average value for all situations of the target
network. This result shows that the proposed method cankeep the retransmission rate of the background trafﬁc below

Fig. 17: Average retransmission rate of background trafﬁc fordifferent current scan rates (α) when θ = 0.1, N = 10, and= {1, 2}.


FRQWUROOHGE\SURSRVHGPHWKRG(a) Case 1 (i = 1) SSV RQ DYHUDJH
 SSV LQFUHDVHG]LJ^HVWYD_XHRQIL[HGVFDQUDWH


ZKHQREVHUYHGVFDQUHVSRQVHGHOD\Fig. 18: Average scan rate (β) for different current scan rates(α) when θ = 0.1, N = 10, and = {1, 2}.

θ regardless of the current scan rate α. As seen in the resultof a ﬁxed scan rate, α = 3 pps is the highest scan rate which
satisﬁes θ on average. Compared with this result, the proposed
method can decrease the upper range of the retransmission rateof the background trafﬁc by 0.015. Moreover, the proposed
method increases the scan rate by 0.18 pps compared with thehighest scan rate of the ﬁxed scan rate. Therefore, the proposed
method can scan faster and improve the retransmission rate of
the background trafﬁc compared with the ﬁxed scan rate.
Figures 19 and 20 show the average of the retransmission
rate of the background trafﬁc and the average of the controlled
scan rate α for all situations of the target network. The
proposed estimators are trained for different cases , respectively. Figure 19 shows that the estimator trained for case 2
only and cases 1 and 2 can keep the average retransmission

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

>QXPEHURIUHWUDQVPLVVLRQVSDFNHW@ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021([FHHG ̝ș \ 

Fig. 19: Average retransmission rate of background trafﬁc fordifferent training cases ( ) when θ = 0.1 and N = 10.


FRQWUROOHGE\SURSRVHGPHWKRG([FHHG ̝ SSVLQFUHDVHG

Fig. 20: Average scan rate (α ) for different training cases ( )when θ = 0.1 and N = 10.

rate of the background trafﬁc below θ. Moreover, Fig. 20
shows the estimator trained for cases 1 and 2 can scan fasterby 0.58 pps compared with the estimator trained for case 2
only on average. Therefore, the proposed estimator trained for
multiple cases can more effectively control the scan rate thanthe estimator trained for a single case.

1383

Then, we proposed the method for estimating the retransmission rate of the background trafﬁc for various scan rates
different from the current scan rate and the scan responsedelay. First, network situation estimator estimates the situationof the target network from observed value and estimated value
regarding the variance of scan response delay. Second, COR
estimator estimates the COR of the background trafﬁc fromthe mean of scan response delay, the estimated situation of thetarget network, and the scan rate which is used for observing
the scan response delay. Third, retransmission rate estimator
estimates the retransmission rate of the background trafﬁc fromthe COR of the background trafﬁc at the estimated situationof the target network, and a scan rate of estimation target.
After that, we proposed the method for controlling thescan rate based on the estimated retransmission rate of the
background trafﬁc. First, the scanner is initialized by setting
retransmission rate threshold, scan rate for observing the scan
response delay, the proposed estimators trained for one or morecases of the situation of the target network, and candidate scanrates. Second, the scanner scans by using the current scanrate and observes the scan response delays during scanning.
Finally, the scanner selects the highest scan rate that can keep
the estimated retransmission rate of the background trafﬁc
below the retransmission threshold.
We evaluated the estimation accuracy of the retransmission
rate of the background trafﬁc by using the proposed estimation
method. We conﬁrmed that the proposed estimation method
can estimate the retransmission rate of the background trafﬁcwith the average error lower than 0.058 regardless of thesituation of the target network and the scan rate. Moreover,
we evaluated the performance of scan rate control by using
the proposed method. We conﬁrmed that the proposed method
can scan faster by 0.18 pps and decrease the retransmission
rate of the background trafﬁc by 0.015 compared with the caseusing a ﬁxed scan rate.
ACKNOWLEDGMENT
This work is a result of the project entitled “Research andDevelopment of Technologies of Efﬁcient Network Scanningfor Wide-Area IoT Wireless Networks,” which is supported by
the Ministry of Internal Affairs and Communications as partof the research program “R&D for Expansion of Radio Wave
Resources (JPJ000254).”

VII. C ONCLUSIONR EFERENCES

This paper proposed a method for controlling the scan rate
based on the retransmission rate of the background trafﬁc froma scan response delay obtained at a scan rate.
First, through computer simulations considering a Wi-SUN
sensor network with two different network situations andmultiple scan rates, we derived a regression function using
the least-squares method for the mean of the scan responsedelay and the COR of the background trafﬁc, and that for the
COR of the background trafﬁc and the retransmission rate of
the background trafﬁc.

[1] “The ZMap Project”, https://zmap.io/ (Retrieved on Aug. 30, 2021)[2] “Wi-SUN Alliance”, https://wi-sun.org/ (Retrieved on Aug. 30, 2021)[3] “LoRa Alliance”, https://lora-alliance.org/ (Retrieved on Aug. 30, 2021)[4] K. Suzuki, T. Kurihara, K. Yano, and Y. Suzuki, “Impact of Congestion
Level on Success Probability of Network Scan for IoT Wireless Equipment,” IEICE Society Conf. 2019, B-6-7, September 2019. (in Japanese)[5] K. Suzuki, T. Kurihara, K. Yano, and Y. Suzuki, “Impact of Network
Scanning on Communication Performance of Wi-SUN,” IEICE Society
Conf. 2020, B-6-21, September 2020. (in Japanese)[6] Giuseppe Bianchi and Ilenia Tinnirello, “Kalman Filter Estimationof the Number of Competing Terminals in an IEEE 802.11
network,” Proc. IEEE INFOCOM 2003, March-April 2003.
DOI:10.1109/INFCOM.2003.1208922

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021
[7] N. Matsumoto, I. Oka, and S. Ata, “Estimation of The Number of Users
by Backoff and Collision Information on WLAN,” IEICE technical report,
IT2018-12, pp. 1–4, July 2018. (in Japanese)[8] N. V. Nguyen, I. Guerin-Lassous, V. Moraru, and C. Sarr,“Retransmission-based available bandwidth estimation in IEEE 802.11based multihop wireless networks,” Proc. ACM MSWiM 2011, Oct.-Nov.
2011. DOI: 10.1145/2068897.2068961[9] K. Suzuki, T. Kurihara, K. Yano, and Y. Suzuki, “A study on Estimationof Retransmission Rate of Background Trafﬁc with Scan Response
Delay of Network Scan,” IEICE technical report, NS2020-79, pp. 20–
25, November 2020.
[10] K. Suzuki, T. Kurihara, K. Yano, and Y. Suzuki, “A Study on Estimationof Retransmission Rate of Background Trafﬁc for Various Scan Rates with
Scan Response Delay,” Proc. ICACT 2021, pp. 160–165, Feb. 2021, doi:
10.23919/ICACT51234.2021.9370480[11] “ns-3 Network Simulator”, https://www.nsnam.org/ (Retrieved on Aug.
30, 2021)

1384

Laboratories working on future mobile satellite communication systems. From
June 2018, he has been engaged in the research of innovative radio communication systems at ATR Wave Engineering Laboratories, Kyoto, Japan. He
received the Younger Engineer Award from the IEICE Japan in 2003. He isa senior member of IEICE.

Kenta Suzuki was born in Japan, 1988. He received
his B.S. degree in Computer Science from Hirosaki
university, Japan in 2011, and received his M.S.
degree in Computer Science from Tohoku university,
Sendai, in 2013.In 2013, he joined Mobile Techno
Corp., where he was engaged in development on
broadcast radio system for disaster prevention andsystem level simulator for network simulation. Since
April 2019, he has been assigned to Advanced
Telecommunications Research Institute International(ATR) as a researcher, and he is engaged in researchand development on network scan systems. He is a member of the IEICE.
Takuya Kurihara was born in Japan, 1988. Hereceived his B.E., M.E., and Ph. D. degrees inelectrical engineering from Nippon Institute of Technology, Saitama, Japan, in 2011, 2013, and 2017,
respectively. In 2018, he joined Advanced Telecommunications Research Institute International (ATR),
Kyoto, Japan, where he is currently a researcher ofWave Engineering Laboratories. His research interests include nonlinear systems, signal processing,and optimization. He received the 2013 Presentation
Award in Nonlinear Problems from IEICE TechnicalCommittee on Nonlinear Problems and the IEICE Young Researcher’s Awardin 2014. He is a member of IEICE.
Kazuto Yano was born in Japan, 1977. He received
the B.E. degree in electrical and electronic engineering, and the M.S. and Ph.D. degrees in communications and computer engineering from Kyoto
University in 2000, 2002, and 2005, respectively. He
was a research fellow at the Japan Society for the
Promotion of Science (JSPS) from 2004 to 2006. In
2006, he joined the Advanced Telecommunications
Research Institute International (ATR). Currently,
he is the Head of Dept. Wireless Communication
Systems at Wave Engineering Laboratories, ATR.
His research interests include spacetime signal processing for interference
suppression, MIMO transmission, and PHY/MAC cross-layer design of wireless communication systems for ISM bands. He is a member of IEEE and asenior member of IEICE.
Yoshinori Suzuki was born in Japan, 1970. He
received the B.E., M.E. and Ph.D. degrees fromTohoku University, Sendai, in 1993, 1995 and 2005
respectively. He joined NTT Wireless Systems Laboratories in 1995. Since then, he engaged in researching microwave signal processing techniques
for satellite onboard applications and onboard multiple beam antenna feed techniques. He worked as aparttime lecturer at Niigata University in 2012 and2014. From 2013 to 2014, he was in charge of sales
engineering of satellite communication services in
NTT Software Corporation (currently NTT Techno Cross Corporation). Since
then, he was a research engineer in NTT Access Network Service Systems

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1385

Ransomware Detection
Using Open-source Tools
Sun-Jin Lee, Hye-Yeon Shim, Yu-Rim Lee, Tae-Rim Park, Il-Gu Lee
Department of Future Convergence Technology Engineering, Sungshin :RPHQ¶VUniversity, South Korea{220214013, 220214012, 220214014, 220214011, iglee}@sungshin.ac.kr
Abstract²The recent development of new and variant
malicious codes, and the increase in cyberattacks in the form ofintelligent Advanced Persistent Threat (APT), has led to rapidly
increasing levels of damage. In particular, in the case ofransomware, the damage per attack is large, because
ransomware uses a network propagation method, by which
each attack can infect multiple victims. As ransomware as aservice (RaaS) has increased recently, even people without thecapacity to develop malicious code have become able to attack
via ransomware. In this study, we built and experimented witha framework that detects ransomware in network and system
environments using open-source tools. This study showed
through analysis and experiments that open-source tools canquickly identify and respond immediately to APT attacks.
Keywords²Open-source, Endpoint Detection and Response(EDR), Google Rapid Response, Open-source HIDS SECurity(OSSEC), osquery, Ransomware Detection

I. INTRODUCTION
Due to the prolonged COVID-19 pandemic, remote work
has become commonplace, and the number of industries
using the Internet of Things (IoT) has increased, expanding
the attackable area. Among the potential modes of attack,ransomware is widely used in crimes, because it can attack
multiple people simultaneously through networks.
Ransomware²a
portmanteau
ofRansomandSoftware²penetrates the system to be attacked, encrypts
data, and then demands money from the victims in return fordecryption. Attack types include penetrating the system toencrypt data, stealing confidential information, and blocking²²²²²²²²²²²²²²²²²²²²²²²
Manuscript received January 19, 2021. This work was partly supportedby the National Research Foundation of Korea (NRF) grant funded by the
Korea government (MSIT) (No. 2020R1F1A1061107) and Korea Institutefor Advancement of Technology (KIAT) grant funded by the Korea
Government (MOTIE) (P0008703, The Competency Development Programfor Industry Specialist), and a follow-up of the invited journal to the accepted& presented paper entitled "Study on Systematic Ransomware Detection
Techniques" of the 23th International Conference on Advanced
Communication Technology (ICACT2021).
Sun-Jin Lee is with the Department of Future Convergence Technology
Engineering, SuQJVKLQ :RPHQ¶V 8QLYHUVLW\ 6RXWK .RUHD H-mail:220214013@sungshin.ac.kr)
Hye-Yeon Shim is with the Department of Future Convergence
7HFKQRORJ\ (QJLQHHULQJ 6XQJVKLQ :RPHQ¶V 8QLYHUVLW\ 6RXWK .RUHD
(e-mail: 220214012@sungshin.ac.kr)
Yu-Rim Lee is with the Department of Future Convergence Technology(QJLQHHULQJ 6XQJVKLQ :RPHQ¶V 8QLYHUVLW\ 6RXWK .RUHD H-mail:220214014@sungshin.ac.kr)
Tae-Rim Park is with the Department of Future Convergence
7HFKQRORJ\ (QJLQHHULQJ 6XQJVKLQ :RPHQ¶V 8QLYHUVLW\ 6RXWK .RUHD
(e-mail: 220214011@sungshin.ac.kr)
Il-Gu Lee is with the Department of Future Convergence Technology
Engineering, 6XQJVKLQ :RPHQ¶V 8QLYHUVLW\ 6RXWK .RUHD &RUUHVSRQGLQJ
Author phone: +82-2-920-7145; e-mail: iglee@sungshin.ac.kr)G

critical systems [1]. Starting with the infection of > 230,000
computers across 150 countries through encryption by"WannaCry" in 2017, ransomware has increased its attack
success rate by utilizing new and modified malware andobfuscation techniques. Even attackers who cannot develop
their own malware have recently used ransomware as theirmain means of attack, by licensing malware from hackers andsharing the profits obtained [2, 3]. According to Statista, thenumber of ransomware outbreaks worldwide in 2020 wasaround 340 million, up 61.8% from 2019 [4]. According to
the Cyber Attack Trends 2021 Mid-year Report released by
Checkpoint, the number of ransomware attacks in the firsthalf of 2021 increased by about 93% over six months, andransomware damage is rising rapidly [5]. Therefore, research
into systems which can respond efficiently to cyberattacks isneeded to respond to and minimize the damage caused byransomware. The use of digital forensics technology isimportant to quickly detect and efficiently respond tointelligent and advanced cyberattacks. Among the digitalforensic technologies, research into Endpoint Detection andResponse (EDR), an approach which involves the analysis ofa client's system event log to identify attacker intentions andrespond immediately to attacks is underway [6]. An EDR toolconnects a server to a client, allowing them to rapidly obtainclient information from the server, and then analyze andrespond to this information. This study used open-source
EDR tools to analyze in real-time whether a client connected
to an EDR server was infected with ransomware, and tocheck whether an attack could be detected based on thisinformation. The experiments used File Finder [7], one of theflows of Google Rapid Response (GRR), an open-source
EDR tool, Query's file-related query statement [8], andOpen-source HIDS Security (OSSEC)'s integrity monitoring
function [9] to analyze whether clients were infected withRAASNet [10], an open-source ransomware.
The contributions of this study are as follows.
ˍ We propose a framework for detecting attacks using
open-source tools in network and system environments.
ˍ We confirmed that the proposed ransomware
countermeasure method was feasible in a real system
environment, by examining the characteristics of each
open-source tool, selecting the WRRO¶V core function, andexperimenting.
The rest of this paper is structured as follows: Section II
introduces related research. Section III describes our
experiments with ransomware detection in three open-source
tools, and analyzes the results. Section IV describes aransomware detection methodology for each open-source
tool, and Section V provides the conclusions.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

II.

RELATED WORKAs the number of APT attacks such as ransomware
increase exponentially, technologies to prevent and defend
against such attacks are being actively studied.
A. Cyber Attack Detection
One study detected attacks using the characteristics ofransomware communication by analyzing the HTTP message
sequence and content size of ransomware products [11]. In
that paper, network communication results such as HTTP
traffic attributes were observed using the representative
ransomware families ³&U\SWR:DOO´ and ³/RFN\,´ and a
detection approach based on Proof of Concept Software
Defined Network (SDN) was used. The system achieved a
97% detection rate, showing the feasibility of the approach.
However, this approach had a limitation, in that the HTTP
response size of the command and control server, which ismainly used for attacks, was not considered. This study
differed from ours, in that it detected attacks based on
network attribute information.
There have also been studies into systems that defend
against attacks by analyzing the characteristics of theransomware. Representative studies include the detection ofAndroid malware by benchmarking the detection engines of
Dr-Droid and Virustotal, open-source malware detection
tools [12]. In this study, for malware based on adware,ransomware, scareware, and Short Message Service (SMS),
Dr-Droid detected attacks with an accuracy of 79.06% andVirustotal's detection engine detected attacks with anaccuracy of 98.7%. The work reported in this paper was
similar to this study in that ransomware was detected, but it
did not cover artifact information such as files or the time
infected with each ransomware.
In some studies, attacks were detected by identifying
suspicious behavior in event logs. A representative study
related to a score-based anomaly detection technique atendpoints using the Local Outlier Factor (LOF) andAutoencoder [13]. This study used unsupervised learning todetect abnormal behavior as suspicious attacks after learningnormal behavior. Suspicious behavior was identified byapplying the allowed list operation policy, which resulted in
107 previously undetected suspected processes in LOF, 44 in
AutoEncoder-based system behavior, and 24 in network
behavior. However, it is difficult to filter all files using thisVWXG\¶V SURSRsed operational policy, and there was alimitation in that some false detection occurred because
unsupervised learning was used.
B. Attack Detection Using Open-source EDR Tools
Recently, studies have used open-source EDR tools to
detect malicious code. A representative study detected
exploits using GRR [14]. In this paper, we studied amonitoring system which aimed to track attack threats, anddemonstrated that two types of exposures could be detected.
In controlled experiments, attacks due to vulnerabilities in
FTP servers and Adobe Reader could be detected using GRR,
and abnormal behavior of memory and networks was
detected using Hunts among the GRR functions. This work
demonstrated that cyberattacks are detectable by GRR tools,
but there was a limitation, in that it could not determine how
to detect cyberattacks using EDR tools other than GRR.
Another study was tested using osquery, flet, and elastic
stack solutions, to demonstrate the effectiveness of1386

open-source EDR systems in real-world environments [15].
In this study, various types of trojan attacks such as³$SSOHMHXV: /D]DUXV´ and ³M5$7/$GZLQG´ were detectedusing open-source EDR, demonstrating their applicability inreal-world environments. However, there was a limitation, inthat it was not possible to confirm whether this approach was
effective in all environments, because experiments were notconducted with attacks other than trojan horses.
A previous study evaluated the detectability of attacks
using several types of open-source EDR tools [16]. In thisstudy, we analyzed the EDR tools osquery, GRR, andMozilla InvestiGator (MIG), which can detect attacks using
real-time forensic functions. The three real-time forensic
tools can collect information by processing vast amounts ofdata in real-time over a network, without turning off thepower to a computer suspected of damage. In this study, three
tools were evaluated for process creation, persistence, andnetwork connection. It was possible to monitor the internal
operations of the operating system using osquery, findattacked files using GRR and MIG, and detect attacks bysearching for strings expected to be malware samples in theactual file content. The authors confirmed that MIG is useful,in that it can handle embedded systems, but was not sufficient
to check the Windows registry. The use of osquery
demonstrated that a system can be automatically monitored
based on a variety of attributes and artifacts, along with thereal-time investigation capabilities provided by GRR and
MIG. By evaluating the possibility of detecting attacks at thelevel of process generation, persistence, and network
connection among open-source EDR tools, this study
attempted to demonstrate the value of the use of EDR tools
for ransomware detection in network and system
environments.
III. Open-source-based Ransomware Detection
A. Ransomware Detection Frameworks
In this study we used open-source EDR tools to detect
when a device was infected by RAASNet, an open-source
ransomware. At this time, the payload of ransomware
generated by RAASNet is an "override and name" method.
This is an attack that copies an existing file, changes the fileextension to the .DEMON format, and then removes theexisting file to prevent viewing or modifying the encrypted
file. The following is a ransomware detection method foreach of the representative open-source tools, GRR, osquery,and OSSEC.
1) Google Rapid Response
GRR is a framework for responding to infringement
accidents, focusing on remote live forensics developed by
Google.
A GRR server communicates with pre-registered clientsusing messages. A client communicates with a server using
the HTTP protocol, and sends a batch of responses via
periodic HTTP POST requests. The client generates amessage queue based on the client's name, and then processes
the responses in the queue. The client may only receive arequest corresponding to its own queue, but may transmit a
response to all worker queues. Communication between theserver and client is encrypted using an RSA public key,signed using the sender's private key [17].
Table I summarizes the core functions provided by GRR.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

TABLE პKEY FEATURES OF GRR
FeatureDescription

Cross-platform

Support for cross-platform Linux, OS X andWindows

Memory Analysis

Real-time remote memory analysis using
YARA library

Can schedule action
queries

Recurring tasks can be scheduled

Registry search function

Search and download capabilities for files
and Windows registry

Large-scale hostmonitoring

Large-scale monitoring using the Huntfunction

GRR can run on Linux, OS X (macOS), and Windows
environments and enables real-time remote memory analysis
using YARA, a tool designed to help malware researchers
identify and classify malware samples. It is possible toautomate the process using the reservation function for
repeatedly performed Flow or Hunt. The user can also searchfor or download files and the Windows registry. Hundreds ofdigital forensic artifacts can be collected, and OS-level andnative file systems are accessible using SleuthKit (TSK) [18].
In this study, queries about before and after the infection of
the victim device systems were performed using File Finder,
which can obtain information about files in specific memory
spaces during the flow of the GRR server. Using thisapproach, changes in the file system were checked before andafter their infection with ransomware.
2) osquery
osquery is a SQL-based operating system instrumentation,
monitoring, and analysis framework developed by Facebook.
It has a built-in SQL language and hundreds of tables, so it iseffective at responding to incidents. There are two types ofosquery: osqueryi and osqueryd. osqueryi is an interactive
query console/shell that allows users to explore the state of
the operating system through the shell without the need tocommunicate with a daemon or run as administrator.
osqueryd is a host monitoring daemon that can schedulequeries and record state changes in an operating system. The
daemon aggregates query results over time and analyzes
changes in the query's state. The OS event API can be used tolog file or directory changes and hardware and network
events [19].
Table II summarizes the core functions provided byosquery.
TABLE ჟKEY FEATURES OF OSQUERY
FeatureDescription

Cross
platform

Can be built and used on a variety of OSs

Interactive
Query
ConsoleAn interactive query console, osqueryi, provides a
SQL interface for testing new queries and navigating
the operating system.

Can schedule
action queries
Large-scale
hostmonitoring

1387

A high-performance host monitoring daemon
osqueryd allows users to schedule queries that run
across their infrastructure
osqueryd's logging is tech stack-agnostic thanks to its
powerful plugin architecture.
It can be integrated into the existing internal log
aggregation pipeline.

osquery can run in the Windows, OS X, Linux, and
FreeBSD environments, and manages the operating system
as an interactive query console and relational database.
Large-scale environmental monitoring is possible using
various plug-in functions.
In this study, all files in the victim's infected memory
space were queried using the file query statement of osqueryi.
As with GRR, queries were performed before and after
ransomware infection to investigate changes in the filesystem before and after infection.
3) Open-source HIDS SECurity (OSSEC)OSSEC is an open-source host-based intrusion detection
system that allows administrators to monitor information on
agents, syslogs, databases, and agentless devices. OSSEC'sagent uses UDP communication, collects information, andforwards information to administrators for analysis. At thistime, some information is collected in real time and some iscollected periodically [20]. In the absence of an agent, it ispossible to monitor firewalls, routers, and Unix systems foragentless devices.
Table III summarizes the core functions provided by
OSSEC.
TABLE რKEY FEATURES OF OSSEC
FeatureDescription

Cross
platform

Can be built and used on a variety of OSes

File
Integrity
Check

Detect system changes in real-time to files and Windows
registry settings, maintain and manage forensic copies of
data

Log
monitoring

Monitoring and analysis of multiple log data in real time

Rootkitdetection
Activeresponse

Process and file-level analysis to detect malicious
applications and rootkits
Real-time response to attacks and changes to systems
through firewall policies, integration with third parties
such as Content Delivery Networks (CDNs) and support
portals, and multiple mechanisms

OSSEC provides comprehensive host-based intrusion
across multiple platforms, including Linux, Solaris,
Advanced Interactive eXecutive (AIX), Hewlett Packard
Unix (HP-UX), Berkeley Software Distribution (BSD),
Windows, Mac, and VMware Elastic Sky X (ESX). OSSEC
enables real-time change history management of files andWindows registry settings, and specializes in analyzing logs
in real time. Various policies can be used to respond to
attacks or changes, and users can set alert rules according totheir security requirements, enabling the performance ofcustomized actions when an alert occurs.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

In these experiments, changes to the ransomware-infected
files were detected using the integrity check, which is afunction of OSSEC; it sets the space to be checked for
integrity in an OSSEC configuration, and detects encryption
and file extension changes that exist in the memory space.
B. Experimental Environment
The environment was configured by dividing the server
and the client to be attacked. An open-source EDR (GRR,
osquery, and OSSEC) system was built on the server, andeach client version of the open-source EDR was installed on
the client before the files were attacked.
Table IV summarizes the components and versions of theenvironment used in the experiment.
TABLE სCOMPONENTS AND VERSIONS USED TO BUILD
THE EXPERIMENTAL ENVIRONMENT
Component
Version,QWHO&RUHL3URFHVVRU
CPU10700K @ 3.80 GHz
RAM
32.0 GB
Server,Client (agent)
Ubuntu 18.04.4 LTS
OS
RAASNethttps://github.com/leonv024/RAASNet
GRR
3.4.0-1
osquery
3.1.0
OSSEC
4.3.0

The CPU was Intel's i7 10th generation, and Ubuntu 18.04.4
LTS version was used for the OS in each server and client.
The software used was GRR 3.4.0-1, osquery 3.1.0, andOSSEC version 4.3.0.
C. Experimental Process
1) Google Rapid Response
For the experiments, each victim was registered in the GRR
Server in advance so that it could be managed as a single
agent. Figure 1 shows the attack detection process using
GRR.

1388

GRR Server sent a query to the infected system to compare
the query results before and after the attack.
2) osquery
The experiments were carried out by installing osquery on
a victim system. Figure 2 shows the attack detection processusing osquery.

Fig. 2. Configuration of the osquery experiment environment.

In osquery, SQL queries can be written to check andcompare data from the operating system. Using a SQL table,
it is possible to detect running processes, changes in the hashvalue of a file, changes in network connections, and otherinformation. osqueryi, an interactive query console, enables
incident response, system operation problem diagnosis, andthe identification of the causes of performance problems.
In the experiments, we queried osqueryi for file properties
in the victim's target folder, and compared the SQL tablevalues before and after infection with RAASNet. We used
µSelect
*
from
file
WHERE
path
like
"/home/osquery1/target/%"¶, compared the results using aquery, and analyzed the results to check whether the files
were infected with ransomware.
3) Open-source HIDS SECurity (OSSSEC)
For the experiments, the victim was registered as an agent
in the OSSEC server and system changes were monitored
based on the registered ID. Figure 3 shows the attack
detection process using OSSEC.

Fig. 1. Configuration of the Google Rapid Response (GRR) experimentenvironment.

Typical tasks provided by GRR include Flow and Hunt.
Flow is a logical collection unit that achieves a given goal in
the server and client, and Hunt is a Flow experimental
mechanism optimized for a large number of systems. Hunt isused to search for and monitor specific data in a large-scale
system, and Flow is used for general experiments. In thisexperiment, attacks were detected using File Finder during
Flow. The attacker attacked a specific folder in the filesystem of the victim PC registered as a GRR client, and theFig. 3. Configuration of the Open-source HIDS SECurity (OSSEC)
experiment environment.

OSSEC allows users to customize alert rules to meet theirsecurity requirements by modifying configuration options. In
this experiment, OSSEC's integrity check function was used
to detect RAASNet ransomware. The rules for the integrity
check were set in the OSSEC configuration, and themonitoring results were checked and compared before andafter the attack to determine the ransomware infection status.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

D. Experimental Results
To confirm the detection of ransomware, detection was
carried out separately before and after RAASNet was
executed. For the experiments, 10 MS Office Presentation
files, 10 MS Office Word files, and 10 MS Office Excel files
were created in a target folder.
1) Google Rapid Response
Figures 4 and 5 show the flow results for one file in thetarget folder before and after executing RAASNet using GRR
File Finder. Checking the experimental result revealed thatthe atime, mtime, and ctime of the target files in target_folder
had changed.
Atime is an abbreviation of last access time, meaning theprevious time the file was accessed. The atime value changes
when a file is opened or read by a command such as cat or vi.
mtime is an abbreviation of last modification time, whichmeans the last time the file was modified. The mtime value isupdated when the file contents are added or the file sizechanges. Ctime is an abbreviation of inode change time, andindicates the last change time of a file. When changing thepermission of a file using chmod or changing the owner of afile via chown, the ctime also changes while the permission
information is updated in the inode, and ctime is affected
when creating or deleting hard links. Unlike atime and mtime,
ctime has the characteristic that it cannot be changed with thetouch command. When changing data contents, both mtime
and ctime are changed.
When the victim's file was encrypted using RAASNet,atime, mtime, and ctime all changed.

1389

SDWK OLNH ³KRPHRVTXHU\WDUJHW´ When the query was
executed, it was confirmed that the name and file size of the
ppt1.pptx file, one of the target files, and the atime, mtime,and ctime properties of the file, were changed. As theransomware RAASNet was executed by targeting thevictim's folder, the files in the osquery1 directory wereencrypted and detailed properties of the file system changed.

Fig. 6. Results of osqueryi before RAASNet execution.

Fig. 7. Results of osqueryi after RAASNet execution.

3) Open-source HIDS SECurity (OSSEC)OSSEC provides real-time monitoring capabilities. In theseexperiments, modifying the ossec.conf file changed thedetection time in real time, and the detection range waschanged to the target folder range. Using a dump of thedatabase and integrity checking on the administrator screen,
the status of files in the target folder could be checked, as
shown in Figure 8. After RAASNet was infected and themonitoring results were observed through database dumping,it was confirmed that a file was created, as shown in Figure 9,
and that the file name and file hash value were changed.
Fig. 4. Results of Google Rapid Response File Finder Flow
before running RAASNet.

Fig. 5. Results of Google Rapid Response File Finder Flowafter running RAASNet.

Fig. 8. Hash values of the files in the target_folder directory before checking
RAASNet execution using Open-source HIDS SECurity (OSSEC)

2) osquery
Figures 6 and 7 show the query results before and after
executing RAASNet using osqueryi, and an analysis ofwhether the files in the target folder are infected. After
executing RAASNet, we used select * from the file with a
Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1390

case of osquery, it was possible to recognize changes in atime,
mtime, and ctime in the file system by querying a databasethat is updated in real-time for information about the target
file. OSSEC uses its own real-time integrity monitoring
function to check changes in the attack target file by changing
the hash value, such as md5 or sha1, of each attack target file.
All three open-source tools detect attacks by changing theenvironment of the ransomware-infected system, and when afile's hash value and file attribute values change suddenly, it
is judged as abnormal behavior and can prompt a real-time
response.
Fig. 9. Hash values of the files in target_folder directory after checking
RAASNet execution using Open-source HIDS SECurity (OSSEC).

V. CONCLUSIONS

IV. ANALYSIS OF EXPERIMENTAL RESULTS
In this study, we confirmed the ability of open-source EDR
to detect when the target PC was infected with ransomware.
For GRR, Flow's File Finder was used. Attacks could be
detected by the changes to the target file name and the ILOH¶V
atime, mtime, and ctime properties. In the case of osquery,the file system of the target PC could be checked using thequery statement provided by itself, and at tacks could be
detected by changes to the target file name and the atime,
mtime, and ctime file properties. In the case of OSSEC, theintegrity check function was used among the constantmonitoring functions provided, and attacks could be detectedby changing the file name and hash value. Table ტ shows thefunctions, methods, and results of ransomware attack
detection by the open-source tools used in this study.
By utilizing the key features of each open-source EDR tool,
it was possible to quickly detect various cyberattacks,
including ransomware. As shown in Table ტ, GRR can checkcondition changes such as the name of the attack target fileand the atime, mtime, and ctime properties using the filefinder among the filesystems in the flow it provides. In theOpen-source software

As cyberattacks increase and spread, it is essential toquickly detect and take action when attacks occur, tominimize damage to the system. Therefore, it is necessary to
introduce tools and information collection systems that can
detect attacks in real-time.
In this study we analyzed the unique characteristics andfunctions of each EDR tool. The target 3&¶VILOHV\VWHPwasattacked using RAASNet, an open-source ransomware. The
possibility of attack detection in open-source EDR was
confirmed using GRR, osquery, and OSSEC.
The results of these experiments show that GRR detects
attacks through a file finder that can check for file changes
during flow, osquery detects attacks through a query
statement that detects system changes, and OSSEC detects
attacks through a constant monitoring system.
This paper demonstrates the potential of detecting attacks
and warning users in the form of logs or alerts. In a follow-up
study, we plan to check the detection efficiency of each
open-source EDR tool in a large-scale environment, anddetermine the scope of the response.

TABLE ტANALYSIS OF RANSOMWARE DETECTION METHODS USING OPEN-SOURCE TOOLS
Detection
Detection
Detection
Function
SectionMethodGRR

File Finderosquery
OSSEC

File System Changes

Detection
ResultSQL Query

Select * from file where
SDWKOLNH³WDUJHWGLUHFWRU\´

Filename,
Atime, Mtime, Ctime

Integrity Check

Database Dumping

Hash(md5, sha1)

Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

REFERENCES
1.
2.
3.
4.

5.

6.
7.

8.
9.
10.
11.

12.

13.
14.
15.

16.

17.

18.
19.
20.

6$XUDQJ]HE0$OHHP0,TEDODQG$,VODP³5DQVRPZDUH
$6XUYH\DQG7UHQGV´Journal of Information Assurance and Security,
Vol. 6, No. 12, pp. 48±58, 2017.
2$VODQDQG56DPHW³$&RPSUHKHQVLYH5HYLHZRQ0DOZDUH
Detection $SSURDFKHV´IEEE Access, vol. 8, pp. 6249±6271, 2020.
3 + 0HODQG < ) ) %D\RXP\ DQG * 6LQGUH ³7KH
Ransomware-as-a-6HUYLFHHFRQRP\ZLWKLQWKHGDUNQHW´Computers &Security, vol. 92, 2020.
³Annual number of ransomware attacks worldwide from 2016 to2020´Statista.
[Online].
Available:
https://www.statista.com/statistics/494947/ransomware-attacks-per-ye
ar-worldwide/. [Accessed: 12-Nov-2021].
³&\EHU $WWDFN 7UHQGV ±  0LG <HDU 5HSRUW´ &KHFN 3RLQW
Research.
[Online].
Available:
https://research.checkpoint.com/2021/check-point-softwares-mid-year-attack-trends-report-reveals-a-29-increase-in-cyberattacks-against-or
ganizations-globally/. [Accessed: 12-Nov-2021].
: 8 +DVVDQ $ %DWHV DQG ' 0DULQR ³7DFWLFDO 3URYHQDQFH
Analysis for Endpoint DetectioQDQG5HVSRQVH6\VWHPV´2020 IEEE
Symposium on Security and Privacy (SP), pp. 1172±1189, 2020.
³*55 )ORZV´ *55 )ORZV - GRR documentation. [Online].
Available:
https://grr-doc.readthedocs.io/en/v3.2.1/investigating-with-grr/flows/
what-are-flows.html. [Accessed: 10-Nov-2021].
³$QRPDO\GHWHFWLRQZLWKRVTXHU\´RVTXHU\>2QOLQH@$YDLODEOH
https://osquery.readthedocs.io/en/stable/deployment/anomaly-detection/. [Accessed: 10-Nov-2021].
³6\VFKHFN´266(&
>2QOLQH@$YDLODEOH
https://www.ossec.net/docs/docs/manual/syscheck/index.html.
[Accessed: 10-Nov-2021].
OHRQY ³OHRQY5$$61HW´ *LW+XE >2QOLQH@ $YDLODEOH
https://github.com/leonv024/RAASNet. [Accessed: 10-Nov-2021].
.&DEDM0*UHJRUF]\NDQG:0D]XUF]\N³6RIWZDUH-defined
networking-based crypto ransomware detection using HTTP traffic
FKDUDFWHULVWLFV´ Computers & Electrical Engineering, vol. 66, pp.
353±368, 2018.
Samara, M., & El-Alfy, E.-S. M.³Benchmarking Open-Source
Android Malware Detection Tools´ 2019 2nd IEEE Middle East andNorth Africa COMMunications Conference (MENACOMM). 2019.
doi:10.1109/menacomm46666.2019.8988
Kim, Sujeong & Hwang, Chanwoong & Lee, Taejin, ³Anomaly
Based Unknown Intrusion Detection in Endpoint Environments´Electronics. 9. 1022. 2020. 10.3390/electronics9061022.
+ 5DVKHHG $ +DGL DQG 0 .KDGHU ³7KUHDW +XQWLQJ 8VLQJ
*555DSLG5HVSRQVH´2017 International Conference on New Trends
in Computing Sciences (ICTCS), pp. 155±160, 2017.
&KULVWRSKHU +XUOHVV ³([SORULQJ 2VTXHU\ )OHHW DQG (ODVWLF
Stack as an Open-source solution to Endpoint Detection and
5HVSRQVH´ SANS Institute Reading Room site, 2019. Available:
https://www.sans.org/reading-room/whitepapers/detection/paper/3916
5. [Accessed: 11-Nov-2021].
Kieseberg, Peter & Neuner, Sebastian & Schrittwieser, Sebastian& Schmiedecker, Martin & Weippl, Edgar. (2018). Real-Time
Forensics
ThroughEndpoint
Visibility.
10.1007/978-3-319-73697-6_2.
M.I. Cohen, D. Bilby, G. Caronni, ³Distributed forensics andincident response in the enterprise," Digital Investigation, Volume 8,
Supplement, Pages S101-S110, ISSN 1742-2876, 2011.
https://doi.org/10.1016/j.diin.2011.05.01³:KDW
LV
*55"´
*55
[Online].
Available:
https://grr-doc.readthedocs.io/en/latest/what-is-grr.html[Accessed:
7-Dec-2021]³:HOFRPH WR RVTXHU\´ RVTXHU\ >2QOLne]. Available:
https://osquery.readthedocs.io/en/stable/. [Accessed: 11-Nov-2021].
³266(& $UFKLWHFWXUH´ 266(& >2QOLQH@ $YDLODEOH
https://www.ossec.net/docs/docs/manual/ossec-architecture.html.
[Accessed: 11-Nov-2021].

1391

Sun-Jin Lee was born in Korea in 2000. She is a studentof the Integrated B.S./M.S. course in the Department ofConvergence Security Engineering in Sungshin:RPHQ¶V University, Seoul, Korea. Her current researchinterests are in the area of deep learning, Internet ofThings, malware detection, voice security, image
security, and video security.
Hye-Yeon Shim was born in Korea in 2000. She is a
student of the Integrated B.S./M.S. course in the
Department of Convergence Security Engineering in
Sungshin :RPHQ¶V University, Seoul, Korea. Her
current research interests are in the area of artificial
intelligence, deep learning, malware detection, andprogramming.
Yu-Rim Lee was born in Korea in 1999. She is a studentof the Integrated B.S./M.S. course in the Department ofConvergence Security Engineering in Sungshin:RPHQ¶V University, Seoul, Korea. Her current researchinterests are in the area of artificial intelligence, threat
defense, malware detection, and Internet of Things.

Tae-Rim Park was born in Korea in 1999. She is a
student of the Integrated B.S./M.S. course in the
Department of Convergence Security Engineering in
Sungshin :RPHQ¶V University, Seoul, Korea. Her
current interests are in the areas of artificial intelligence,
network security, malware detection, cloud computing,and Internet of Things.

Il-Gu Lee was born in Korea in 1978. He received his
PhD degree in the Graduate School of Information
Security in Computer Science & Engineering
Department from KAIST at 2016. He is a professor at
the Department of Convergence Security Engineering,Sungshin :RPHQ¶V University, Seoul, Korea. His
current research interests are in the area of
wireless/mobile networks with an emphasis on
information security, networks, and wireless systems.

Copyright ⓒ 2021 GiRI (Global IT Research Institute)


ICACT
Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1392

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Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

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ICACT
Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1397

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Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

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Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1399

An Efficient hole Recovery Methodin Wireless Sensor Networks
Mary Wu
Dept. Of Computer Culture, Yongnam Theological University and Seminary, Korea
mary-wu@hanmail.net
Abstract² In a wireless sensor network, since sensor nodes
are separated from the sensor network area due toenvironmental obstacles or are randomly placed in the area ofinterest, there are cases where there are no sensor nodes in someareas. In addition, a hole may occur in the sensor coverage area
due to the sensor node running out of energy or physicaldestruction of the sensor node. A coverage hole in a sensor
network may adversely affect the performance of a sensor
network, such as reducing the reliability of data sensed by thesensor network and worsening the data transmission load due
to a change in the sensor network topology or disconnection of
the data link. The coverage hole can be recovered bydiscovering the coverage hole that has occurred andadditionally placing new sensor nodes in the detected coveragehole. This can be solved by finding the coverage hole that has
occurred, and recovering the coverage hole by additionallyplacing new sensor nodes in the detected coverage hole atappropriate locations. Existing studies on coverage hole
recovery suggest a very complex method for discovering acoverage hole by identifying a coverage hole boundary node andrecovering a coverage hole through the two-step process offinding a hole and recovering a coverage hole. This study does
not separate the process of discovering and recovering acoverage hole in a sensor network, but determines whether asensor node is a hole boundary node or a hole interior node bychecking the connection line structure of its one-hop neighbornode. The hole boundary nodes determine the location of themobile node to be added by a simple calculation, and perform
coverage hole recovery. The proposed method is expected tohave better efficiency in terms of complexity and message
transmission compared to previous methods.
Keyword² Sensor network, Coverage hole, Boundary node,Connection line, Isosceles triangle

A

I. INTRODUCTION

sensor network is used to monitor and control the area inthe military and civilian fields for various purposes byplacing sensor nodes in a specific area. Sensor nodes arearbitrarily placed for special purposes, detect theenvironment and collect data, and the collected data iswirelessly transmitted to the sink node and then provided to²²²²²²²²²²²²²²²²²²²²²²²
Manuscript received January 10, 2021. This work was adopted as afollow-up of the invited journal to the accepted & presented paper entitled"An Efficient hole Recovery Method in Wireless Sensor Networks" at the22nd International Conference on Advanced Communication Technology(ICACT2020).
Mary Wu is a professor in the Dept. Of Computer Culture, Yongnam
Theological University and Seminary, 26, Bonghoe 1-gil, Jillyang-eup,Gyeongsan-si, Gyeongsangbuk-do, Republic of Korea. (corresponding
author to provide phone: +82-10-5648-8876; fax: +82-53-852-9815; e-mail:mary-wu@hanmail.net).

the user by the application application[1-6].
When sensor nodes are arranged remotely or randomly inan area of interest, separation of the sensor network area
occurs due to environmental obstacles or there are cases inwhich sensors do not exist in some areas. In addition, sincesensor nodes are difficult to use by exchanging or recharging
batteries, when the energy of some nodes is exhausted, it
becomes impossible to collect environmental data in some
network areas, which lowers the reliability of sensing data inthat area. If the sensing data around the coverage hole has
certain characteristics, it is possible to compensate to someextent the problem of reducing the reliability of sensor data inthe corresponding area by estimating the value of the sensed
data in the coverage hole with the sensing data around thecoverage hole. However, the number and size of coverageholes in a sensor network gradually increases over time,
which reduces transmission efficiency due to changes in
network topology and disconnection of data links, andadversely affects the performance of the entire sensor
network[7-18].
The discovery and recovery of coverage holes is an
important factor for efficient sensor networking, such as
ensuring data reliability and minimal delay in sensor
networks. The sensor network coverage hole may be detectedby discovering boundary nodes constituting the coveragehole, and existing sensor nodes may move to the coveragehole, or a new mobile node may be additionally deployed torecover the coverage hole. Since a mobile sensor noderequires a higher cost than a fixed sensor node, when acoverage hole is restored by adding a mobile node, it isrequired to achieve the maximum coverage effect with aminimum number of mobile nodes for cost-effective
coverage recovery. That is, when determining the location ofa mobile node to be newly added, it is required to select an
optimal location that maximizes the sensing area of the entire
sensor network and minimizes overlap of the sensing area.
The level of coverage required by a particular application
depends on its characteristics. For example, a military
surveillance application requiring a high level of security
may require a high level of coverage, while other types ofapplications may not require a high level of coverage. A ratioof the number of mobile nodes to the number of fixed nodes
may be determined according to the coverage request level ofthe application.
There are many studies that find coverage holes. It isdetermined whether the sensor node is a boundary node byconnecting the sensor nodes in a Delaunay triangle orVoronoi polygon structure or by applying geometric features
between neighbors. When a sensor node is identified as a
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

boundary node, a coverage hole is found in the sensor
network by verifying the geometrical features of polygons
with neighboring boundary nodes[7-10]. In these methods,
sensor nodes perform complex calculations based on location
information between neighbors in order to construct a
polygon, and a coverage hole boundary node performs
complex calculations and procedures to determine whether acoverage hole is the same as a neighboring hole boundary
node. In these methods, after performing the coverage holediscovery procedure, a procedure for coverage recovery is
additionally performed.
This study does not separate the process of discovering andrecovering a coverage hole in a sensor network, but recovers
a coverage hole by determining whether the sensor node itself
is a node inside the sensor network or a node at the edge ofthe coverage hole. The hole boundary node uses the distancefrom the neighboring boundary node as the base, andcalculates the vertex of an isosceles triangle using the double
sensing radius as the hypotenuse, and provides it as thelocation of the mobile node to be added.
The sensor node examines whether the connection line ofits one-hop neighbor node has a closed or open line structure
to determine whether it is a hole boundary node. If theconnection line of the one-hop neighbor node has an open
line structure, the sensor node determines itself as a coveragehole boundary node. Compared to previous methods, theproposed method discovers a coverage boundary node andrecovers a coverage hole in a very simple and intuitive way.
The structure of this paper is as follows. Section 2 introduces
related research, and section 3 describes the proposedcoverage hole discovery and recovery technique. Section 4shows the superiority of the proposed technique throughperformance evaluation with existing techniques, and finally,
section 5 presents the research results of this paper.

1400(a) r is greater than R(b) r is less than R

Fig. 1. Relationship between the circumcircle radius R of the Delaunay
triangle and the sensing radius r

In order to discover the coverage hole to which thecoverage hole boundary node belongs, it checks whether it
has a special geometric characteristic with the neighboringhole boundary node and proceeds with the process ofidentifying the coverage hole.
Ma[8] proposed a method for sensor nodes to discover a
coverage hole based on information about their two-hop
nodes. A sensor node divides its two-hop neighbor nodes into
an upper node group and a lower node group based on the
y-axis, aligns the nodes in each group based on the x-axis
value, and forms a triangle for itself and the other twoneighboring nodes. When the radius of the circumscribedcircle of the triangle is greater than the sensing radius and thecenter of the circumscribed circle is not included in thesensing region of the neighboring node, the corresponding
node is determined as a coverage hole boundary node. In fig.
2, the upper node group of sensor node s1 includes sensor
nodes s2, s3, and s4, and the lower group includes sensor
nodes s5, s6, s7, and s8. Since the radius R of the circumscribed
FLUFOHRIǻV1s2s3 is greater than the sensing radius r, and thecenter of the circumscribed circle is not included in thesensing radius of any neighbor of the sensor node s1, the nodes1 is determined as a coverage hole boundary node.

II. RELATED RESEARCH
Li [7] proposed a method for discovering coverage holeboundary nodes by configuring sensor nodes in a Delaunay
triangle in a sensor network. A sensor node recognizes its
own position and the position of a one-hop neighbor node,and constructs a Delaunay triangle with neighboring nodes
based on the distance and angle between the neighbors. This
process is performed by all sensor nodes in the entire sensor
network. To construct the Delaunay triangle, the sensor node
constructs a triangle in which the largest interior angle among
the circular neighboring nodes is the smallest. When a sensor
node has a radius of the circumcircle of its own Delaunay
triangle that is greater than a sensing radius of the sensor
node, a sensor node constituting the Delaunay triangle isregarded as a Hall boundary node. In fig. 1, s1, s2, and s3 are
one-hop neighbor sensor nodes, and a, b, and c are thedistances between sensor nodes. S is the area of the Delaunay
triangle, R is the radius of the Delaunay triangle
circumscribed circle, and r is the sensing radius of the sensor
node. Since the area of the circumcircle of the Delaunay
triangle is expressed as abc/4R, the sensor node candetermine whether it is an inner node or a boundary node
constituting a coverage hole in the corresponding Delaunay
triangle.

Fig. 2. Relationship between the radius R of the triangular circumscribedcircle of the neighboring sensor nodes and the sensing radius r

In the study of Ghosh and Wand[9,10], the sensor nodes inthe entire sensor network were configured in a Voronoi
polygonal structure. When the center point of a Voronoi
polygon is connected to the center point of a neighboring
Voronoi polygon, it appears as a Delaunay triangle. In the
study of Ghosh[9], when the size of the region not included in
the sensing region in the region of the intersection of theregion constituting the Delaunay triangle and its own
Voronoi region is larger than a certain threshold (ƎȜr2, r is
the sensing radius), it is determined that a coverage hole has
occurred. In fig. 3(b), the gray area shows the coverage hole
generation area for the Delaunay triangle area formed by thesensor node s4 with the neighboring nodes s1 and s2. Whennode s4 determines that a coverage hole has occurred in theregion of intersection of Ȟs1s2s4 and Voronoi polygon, it isdetermined as the location of the mobile node to add the point
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

of min(2r, d(v2, s4) on the bisector of v1v2v3.
In the study of Wand[10], if the distance between thesensor node and the furthest Voronoi vertex is greater than
the sensing radius, it is considered that there is a coverager, d(s4,v4)) on thehole. In this case, the point of max(
straight line between the Voronoi vertex of the furthest
distance and the sensor node is determined as the location of
the mobile node to be added.

(a) Voronoi polygonal structure (b) Location of the mobile node to add
Fig. 3. Constructing a sensor network with Voronoi polygons

1401

sensor node creates a one-hop neighbor node connection line,
and a process in which the connection line determines
whether the connection line has a closed shape structure.
£ One-hop neighbor connectivity
7KHVHQVRUQRGHEURDGFDVWVDμ+HOOR¶PHVVDJHLQFOXGLQJLWV
,'DQGWKHQRGHWKDWUHFHLYHVWKHμ+HOOR¶PHVVDJHUHFRUGVWKHQHLJKERUOLVWLQWKHμRQH-hop neighboUOLVW¶7KHVHQVRUQRGHEURDGFDVWV D μRQH KRS QHLJKERU¶ PHVVDJH LQFOXGLQJ D μRQH
KRSQHLJKERUOLVW¶(DFKVHQVRUQRGHKDVDμRQH-hop neighbor
OLVW¶IRULWVRQH-hop neighbor node. In fig. 5, after the sensor
QRGH VHQGV D μ+HOOR¶ PHVVDJH QRGH  KDV D μone-hop
QHLJKERUOLVW¶!DQGQRGHKDVDμRQH-hop
QHLJKERUOLVW¶!$IWHUWKHVHQVRUQRGHEURDGFDVWVWKHμRQH-KRSHQHLJKERU¶PHVVDJHQRGHKDVDμRQH-KRSQHLJKERUOLVW¶RIQRGHVDQGDQGQRGH
30 haVDμRQH-KRSQHLJKERUOLVW¶RIQRGHV
and 39. This is shown in table 1.

Existing studies require complex rules and calculations
based on geometrical locations for sensor nodes to discover
coverage holes. In order to implement this in an actual
situation, a complicated procedure is required, and after acoverage hole is discovered, a procedure for recovering thecoverage hole is additionally required. This study discovers acoverage hole in a sensor network in a very simple andintuitive way, and proposes a coverage hole recovery methodthat does not separate the coverage hole discovery process
and the recovery process.
III. DISCOVERY AND RECOVERY OF COVERAGE HOLES

Fig.5. Constructing a sensor network with Voronoi polygons

This study proposes a technique for discovering andrecovering coverage holes based on the connectivity structureof one-hop neighbor nodes. The proposed method assumes
that sensor nodes are arranged at arbitrary locations, and thateach sensor node recognizes its own location and location
information of a one-hop neighbor node that exists within atransmission radius. If rs is greater than or equal to, it isis based on the feature that there is no
assumed that rs >sensing gap between one-hop neighboring nodes(rs : sensing
radius, rc : transmission radius).

Fig. 4. Model of sensing radius and transmission radius

A. Coverage hole discovery
A sensor node determines whether or not it is a boundary
node of a coverage hole by determining the connectivity
structure of its one-hop neighbor node. If the connectivity ofthe one-hop neighbor node is determined to be a closed shapestructure, the node is not a boundary node of a coverage hole,and if it is determined as a closed shape structure, the node isdetermined as a hole boundary node. The coverage holediscovery process has two processes: a process in which a

TABLE IUNITS FOR MAGNETIC PROPERTIES
One-hope
neighbors of
node 29

List of one-hopeneighbors of node 29

One-hope
neighbors of
node 30

List of one-hopeneighbors of node 30

28{17, 18, 27, 29, 30}

27{19, 20, 28, 30, 31}30{27, 28, 29, 31, 38, 39}

28{17, 18, 27, 29, 30}

39{29, 30, 38, 40, 43}29{28, 30, 39, 40}40{29, 39, 41, 57}

31{25, 26, 27, 30, 32, 38}38{31, 32, 37, 39, 43}39{29, 30, 38, 40, 43}

The sensor node creates a neighbor node connection line
EDVHGRQWKHμRQH-KRSQHLJKERUOLVW¶RIWKHRQH-hop neighbornode. To determine the starting node of the neighbor nodeconnection line, one of its neighbor nodes with the smallest
number of one-hop neighbor nodes is selected. The node 29
designates the node 40 having the smallest number of
one-hop neighbor nodes among its neighbors as the starting
node of the neighbor node connection line. Node 30
designates node 29 with the smallest number of one-hop
neighbor nodes among its neighbors as the start node of theneighbor node connection line.
After checking whether a one-hop neighbor of the
UHIHUHQFH QRGH H[LVWV LQ WKH μRQH-KRS QHLJKERU OLVW¶ RI WKHstarting node of the connection line, if it exists, the node is
GHVLJQDWHG DV WKH QH[W FRQQHFWLRQ QRGH ,Q WKH μRQH-hop
QHLJKERUOLVW¶RIQRGHVLQFHQRGHLVDRQH-hop neighborof reference node 29, node 39 is designated as the next
connection node, and the connection line of node 29 isconnected to <40-!,QWKHμRQH-KRSQHLJKERUOLVW¶RIQRGH
29, since node 28 and node 39 are one-hop neighbors of the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

reference node 29, any node 28 among the two nodes isdesignated as the next connection node, and the connection
line of node 30 is connected to <29-28>.
If there is an additional one-hop neighbor of the reference
node in the one-hop neighbor list, the node is connected in the
opposite direction of the connection line. For the connection
OLQHRIQRGHLQWKHμRQH-KRSQHLJKERUOLVW¶RIQRGHWKH
node 39 that is a one-hop neighbor relationship with thereference node exists in addition to node 28, so theconnection line of node 30 appears as <39-29-28>.
'HVLJQDWHWKHILUVWQRGHRIWKHFRQQHFWLQJOLQHDVWKHμILUVWQRGH¶ DQG WKH ODVW QRGH DV WKH μHnd QRGH¶ ,Q WKH μRQH-hop
QHLJKERU OLVW¶ RI WKH μHQG QRGH¶ D QRGH ZLWK D RQH-hopneighbor relationship with the reference node is found andconnected to the connecting line repeatedly. Through thisprocess, the one-hop neighbor node connection line of node
29 is <40-39-30-28>, and the one-hop neighbor nodeconnection line of node 30 is shown as <39-29-28-27-31>.
When the connection line includes all the neighboring
nodes of the reference node, the connection line creation
process is terminated. If not, the connection process isrepeated for the μfirst node¶ of the connection line, and thisprocess is repeated until all one-hop neighboring nodes of thereference node are included.
Since the one-hop neighbor connection line of node 30
does not include all the neighbors of node 30, it is checked
whether a one-hop neighbor node of the reference node exists
in the one-hop neighbor list of node 39 and the μfirst node¶ ofthe connection line. Since node 38 exists, when connected tothe connecting line, the connecting line of node 30 appears as<38-39-29-28-27-31>.
¤ One-hop neighbor connectivity
The sensor node determines whether the structure of the
one-hop neighbor node connection line is a closed figure ornot. When the structure of the connection line is a closed
diagram, the sensor node is determined as an internal node.
When the structure of the connection line is not a closed
diagram, the sensor node is determined as a boundary node of
the coverage hole. When the μfirst node' and μend node¶ of a
one-hop neighbor node connection line have a one-hop
neighbor relationship, the neighbor node connection line has
a closed shape structure. The one-hop neighbor nodeconnection line of node 29 is <40-39-30-28>, and the μfirstnode¶ 40 and μend node¶ 28 are not one-hop neighboring
nodes, so the connection line does not have a closed shapestructure. As a result, node 29 is determined as a coveragehole boundary node. The connecting line of node 30 is<38-39-29-28-27-! DQG VLQFH μILUVW QRGH¶  DQG μHQG
QRGH¶  KDYH D RQH-hop neighbor relationship, theconnecting line has a closed shape structure. As a result, node
30 is determined to be an internal node, not a boundary node.
B. Coverage hole recovery
If the sensor node itself is determined to be a boundary
node, it proceeds with the process of recovering the coverage
holH$ERXQGDU\QRGHVHQGVDμERXQGDU\QRGHQRWLILFDWLRQ¶
message including its ID and location information. A
ERXQGDU\QRGH WKDWUHFHLYHVDμERXQGDU\QRGHQRWLILFDWLRQ¶
message from a one-hop neighbor calculates the location of amobile node to be added based on its own location and thelocation of the neighboring edge node. The position of the1402

mobile node to be added is calculated as the vertex of an
isosceles triangle with the two boundary nodes as the baseand the double sensing radius (rs) as the hypotenuse. The
vertices of an isosceles triangle are calculated by the two
equations (1) and (2).

(a) Sensor network model(b) Isosceles triangle
Fig. 6. Sensor network and mobile node locations to be added

(1)
(2)
The borGHU QRGH VHQGV D μFRYHUDJH UHFRYHU\ UHTXHVW¶
message including the location of the mobile node to be
added. Upon receiving the μcoverage recovery request¶
message, the mobile node moves to the nearest target location
DQG WUDQVPLWV D μFRYHUDJH UHFRYHU\ UHVSRQVH¶ PHVVDJH
including the moved location.
In order to avoid redundant deployment of mobile nodes,
WKH PRELOH QRGH WKDW UHFHLYHV WKH μFRYHUDJH UHFRYHU\UHVSRQVH¶ PHVVDJH IURP D GLVWDQFH FORVHU WKDQ WKH VHQVLQg
radius gives up its role in the sensor network and waits for the
next round of recovery process.
The coverage discovery and recovery process is repeatedover several rounds until no coverage boundary nodes arefound. Fig. 7 shows the coverage hole recovery process in a
150 sensor nodes topology. Through the second roundcoverage hole recovery process, the coverage hole recovery
process is completed. Fig. 7(a) shows that 23 mobile nodes
locations are determined in the first round coverage hole
recovery. Fig. 7(b) shows 10 mobile nodes excluding
overlapping locations. Fig. 7(c) shows that 11 mobile nodes
positions are determined in the second round coverage hole
recovery. Fig. 7(d) shows 5 mobile nodes elected by
excluding overlapping positions.

(a) Location of mobile nodes
in the first round hole recovery(b) Location of mobile nodes
excluding duplicate locations in the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

first round hole recovery

1403

to the coverage recovery rate. The proposed method shows aresult of about 62% of message transmission compared to thebidding protocol, and about 81% of the result compared withthe sensing intersection-based hole recovery technique.
It is analyzed that as the coverage recovery rate increases,
the amount of messages transmitted increases as the numberof rounds increases to reach the coverage recovery rate.
(c) Location of mobile nodes
in the second round hole recovery(d) Location of mobile nodes
excluding duplicate locations in thesecond round hole recoveryFig. 9. Transmission amount of coverage hole recovery message

(e) Completion of coverage hole
recoveryFig. 7. Coverage hole recovery process

IV. EXPERIMENTS
An experiment was conducted to verify the effectivenessof the proposed coverage hole recovery technique. The
experimental comparison object used the bidding protocol
and the sensing intersection-based hole recovery technique.
Using C language, a transmission range of 20m, a sensing), and 150 nodes were placed in an
range of 15m (>arbitrary location in an area of 100m x 100m, and a control
message of 512kbit and a data message of 2048kbit wereused.
Fig. 8 shows the results of comparing the bidding protocol,
the sensing intersection-based method, and the proposed
method as an experiment on the amount of computationaccording to rounds. Compared to the bidding protocol
applying the geometric rule of Bornois polygons, theproposed method shows about 33% of the results, and about
58% of the results compared to the sensing intersection-based
hole recovery technique. As the round progresses, the amountof computation tends to decrease, and this is seen as a resultof the decrease in the coverage area to be restored as theround progresses, and the reduction in the number ofcoverage hole boundary nodes.

Fig. 8. Computational amount of coverage hole recovery

Fig. 9 shows the results of the bidding protocol, the sensing
intersection-based method, and the proposed method as anexperiment on the amount of message transmission according

Figure 10 shows the experimental results of the coverage
recovery rate for the number of mobile nodes in the proposed
method. The larger the number of mobile nodes, the greater
the coverage recovery rate. 8 (about 5%), 15 (10%), and 23(about 15%) mobile nodes were applied. A lot of recovery ismade in the first round, and as the rounds increase, a small
increase in coverage recovery is shown. In the third round, allcases show a coverage recovery rate of 90% or more. For the
15% with the largest number of mobile nodes, 100%coverage is achieved in the third round. Since a mobile nodeis more expensive than a general sensor node, it is considered
as an important factor for effective coverage recovery to
determine the appropriate number of mobile nodes inconsideration of the coverage recovery rate required by thesensor application.

Fig. 10. Coverage hole recovery rate according to the number of mobile
nodes

V. CONCLUSIONSThis study is a method of recovering a coverage hole in asensor network. A sensor node determines whether it is acoverage hole boundary node or not based on the connection
structure of one-hop neighboring sensors, and if it isdetermined as a boundary node, a method of recovering acoverage hole suggested. In order to recover the coveragehole, the boundary node determines the location of the vertex
of an isosceles triangle with the distance from theneighboring boundary node as the base and the double
sensing radius as the hypotenuse as the location of the mobile
node to be added. The proposed coverage hole recovery
technique recovers the coverage hole using a very simple
procedure and formula compared to the existing method. In arandomized sensor network, coverage hole recovery is a very
important factor for sensor application data reliability and
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1404

efficient networking, and the proposed method shows better
performance in terms of complexity and message
transmission compared to previous studies in experiments.
REFERENCES[1]

Xiaoqing Yu,Pute Wu, Wenting Han, Zenglin Zhang, A survey on
wireless sensor network infrastructure for agriculture, ComputerStandards & Interfaces, Vol. 35, Issue 1, pp. 59±64, Jan. 2013.
[2] Mary Wu, Hyunjin Park, ChongGun Kim, Multihop Routing based on
the Topology Matrix in Cluster Sensor Networks, Journal of The
Institute of Signal Processing and Systems, Vol. 14, No. 1, pp. 45-50,
Jan. 2013.
[3] Zhao Han, Jie Wu, Jie Zhang, Liefeng Liu, Kaiyun Tian, A generalself-organized tree-based energy-balance routing protocol for wirelesssensor network, IEEE Transactions on Nuclear Science, Vol. 61, Issue2, pp. 732-740, Apr. 2014.
[4] Mary Wu, Balanced Cluster-based Multi-hop Routing in Sensor
Networks, Journal of Korea Multimedia Society, Vol. 19, No. 5, pp.
910-917, May. 2016.
[5] Mary Wu, An Efficient Routing Protocol for Mobile Sinks in Sensor
Networks, Journal of Korea Multimedia Society, Vol. 20, No. 4, pp.
640-648, Apr. 2017.
[6] Mary Wu, Strong Connection Clustering Scheme for Shortest DistanceMulti-hop Transmission in Mobile Sensor Networks, Journal of Korea
Multimedia Society, Vol. 21, No. 6, pp. 667-677, Jun. 2018.
[7] Wei Li, Wei Zhang, Coverage hole and boundary nodes detection in
wireless sensor networks, Journal of Network and Computer
Applications, Vol. 48, pp. 35-43, Feb. 2015.
[8] Hwa-Chun Ma, Prasan Kumar Sahoo, Wen-Wen Chen, Computational
Geometry based distributed coverage hole detection protocol for thewireless sensor networks, Journal of Network and Computer
Applications, Vol. 34, Issue 5, pp. 1743-1756, Sep. 2011.
[9] Amitabha Ghosh, Estimating Coverage Holes and Enhancing
Coverage in Mixed Sensor Networks, Proceedings of the 29th AnnualIEEE International Conference on Local Computer Network, Nov.
2004.
[10] Guiling Wang, Guohong Cao, T. LaPorta, A bidding protocol for
deploying mobile sensors, Proceedings of the 11th IEEE International
Conference on Network Protocols, Nov. 2003.
[11] Wei Li, Yuwei Wu, Tree-based coverage hole detection and healing
method in wireless sensor networks, Computer Networks, Vol. 103, pp.
33-43, Jul. 2016.
[12] Rachid Beghdad, Amar Lamraoui, Boundary and holes recognition in
wireless sensor networks, Journal of Innovation in Digital Ecosystems,
Vol. 3, Issue 1, pp. 1-14, Jun. 2016.
[13] Anju Sangwan, Rishi Pal Singh, Coverage Hole Detection and Healing
to Enhance Coverage and Connectivity in 3D Spaces for WSNs: A
Mathematical Analysis, Wireless Personal Communications, Vol. 96,
Issue 2, pp. 2863-2876, Sep. 2017.
[14] Shuangjiao Zhai, Zhanyong Tang, Dajin Wang, Zhanglei Li, Xiaojiang
Chen, Dingyi Fang, Feng Chen, Coverage Hole Detection andRecovery in Wireless Sensor Networks Based on RSSI-Based
Localization, IEEE International Conference on Computational
Science and Engineering (CSE) and IEEE International Conference on
Embedded and Ubiquitous Computing (EUC), 21-24 Jul. 2017.
[15] Tarachand AmgothEmail author, Prasanta K. Jana, Coverage hole
detection and restoration algorithm for wireless sensor networks,
Peer-to-Peer Networking and Applications, Vol. 10, Issue 1, pp. 66-78,
Jan. 2017.
[16] Ahmed M. Khedr, Walid Osamy, Ahmed Salim, Distributed coveragehole detection and recovery scheme for heterogeneous wireless sensor
networks, Computer Communications, Vol. 124, pp. 61-75, Jun. 2018.
[17] Mary Wu, Adjacent Matrix-based Hole Coverage Discovery
Technique for Sensor Networks, Journal of The Korea Society ofComputer and Information, Vol. 24, No. 4, pp. 169-176, Apr. 2019.
Mary Wu was born in Korea in 1973 and received a
BS in Mathematics from Yeungnam University in
1996, a MS in Computer Science in 2001, and a Ph.D
in Computer Science in 2005. She has been acomputer professor at Youngnam Theological
University and Seminary since 2013. Her researchinterests include sensor network, social network
analysis, bigdata, IoT, metaverse education and soon, .

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1405

End-to-End Routing Algorithm Based on
Max-Flow Min-Cut in SDN Controllers
Nada Alzaben*, Daniel W. Engels*** Dept. of Computer Science, College of Computer Science and Information, Princess Nourah Bint Abdulrahman
University, Riyadh, Saudi Arabia**AT&T Center for Virtualization, Southern Methodist University, Dallas, USA
nialzaben@pnu.edu.sa, Daniel.w.engels@gmail.com

Abstract— In this paper, we present seven novel Max-Flow
Min-Cut (MFMC) based algorithms that solve the flow routing
problem in the Software Defined Network Controller. Ouralgorithms identify potential traffic bottlenecks using the
MFMC cut, and they avoid the bottlenecks during flow route
construction through their choice of the cut edge added to theroute. Our algorithms utilize either a random edge selection, ashortest path edge selection, or an edge capacity-based selection
from the set of cut edges. Our simulation results show
improvement in the network performance when using MFMC
and shortest path edge selection compared to simple shortest
path first algorithms, such that the mean wait time is reducedby 15.1%, the mean slowdown is reduced by 5.1%, reducing the
maximum completion time by 9.6%, and increasing the mean
throughput by 18.3%. Therefore, by explicitly consideringcongestion in routing decisions, better network performance isachieved.
Keyword— Heuristic algorithm, Peer-to-peer computing,Routing, Software defined networking.

R

I. INTRODUCTIONOUTING in computer networks is a fundamental process
for finding a communication path that connects two end
points in the network for data transfer. The routing process isrecognized as an essential factor in solving the network
performance problem. In traditional networks the two mainfunctionality of routing is coupled in the router, however, inthe new network layer paradigm, the Software Defined
Networking (SDN), the network functionality is decoupled in
two separate planes of operation: the control plane and data
plane [1]-[4].
Benefiting from the SDN global view, the control plane———————————————————————A manuscript received Jan. 26, 2021. A follow-up on the invited journal
to the accepted and presented paper entitled “ End-to-End Routing in SDN
Controllers Using Max-Flow Min-Cut Route Selection Algorithm“ of the
23rd International Conference on Advanced Communication Technology(ICACT2021).
Nada Alzaben is a PhD student in Southern Methodist University and alecturer in the Department of Computer Science at Princess Nourah bint
Abdulrahman University, Riyadh, Saudi Arabia. (corresponding author,
phone:001-214-768-3050, e-mail: nialzaben@pnu.edu.sa).
Daniel W. Engels, is with AT&T Center for Virtualization, Southern
Methodist University, Dallas, TX 75205 USA (e-mail:Daniel.w.engels@gmail.com ).

runs the routing algorithms to find path for each flow in the
network. Once the path is defined for the flow, theforwarding plane uses that path to deliver the data from one
end to another through network switches [5].
With the increase use of Virtual Private Networks (VPNs),
secured communications with Transport Layer Security(TLS), along with the high demand of sending video files,
which consume the network bandwidth and dominate the
network traffic, have highlighted the importance of flowrouting problem. In this type of traffic, they demand that all
packets follow the same path from source to destination.
Using Software Defined Networks (SDNs) enables
end-to-end routing of flows through its global view which
result in optimizing a given objective function such as
maximizing the mean throughput [6]-[9].
The end-to-end flow routing problem is defined as follows.
Given a network graph ሺǡሻǡ such that each edge    iswith a capacity ǡ where c  Z+, meaning only c flows may besent at a time on that edge. Having a set of flows where
each flow fi has a given number of packets and a releasetime , non-preemptively route all flows inonto
optimize the objective function.
The Shortest Path First algorithms (SPF) are mostcommonly used in end-to-end flow routing [10][11],however, due to their lack of explicitly consideringcongestion when routing causes network performancedegradation in a high load traffic. Therefore, we present our
Max-Flow Min-Cut based algorithm which is capable of
explicitly considering congestion in routing decisions. The
SPF algorithms are based on either statically or dynamicallycalculate the path with shortest distance or minimum numberof hops. The static shortest path is a topological shortest paththat ignores path capacity availability. Moreover, thedynamic shortest path is more logical in considering the edge
capacity availability at the time of flow released before
assigning a flow on it [12].
In a network with high traffic flow demand which placethese flows over edges with limited capacity, such that theflow demand exceeds the edge capacity, consequently causes
congestion in the network, i.e., bottleneck arise. In the Static
Shortest Path (SSP) algorithm, the flows between any two
ends will always take the short path regardless of available
capacity, while for the Dynamic Shortest Path (DSP)
algorithm, the algorithm finds the shortest available path
Copyright ⓒ 2021 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

which considers congestion implicitly during the flow releasetime. However, flows placed on these kinds of paths in a hightraffic flow causes bottleneck problem which means thatthese flows are most likely going to be sharing same
segments of the path in the network. This indicates the needfor a routing algorithm that considers congestion explicitly tominimize this problem.
The Max-Flow Min-Cut (MFMC) algorithm, with apolynomial time complexity, is capable of identifying theminimum set of edges in the network; such that by theirremoval the source node is disconnected from the destination[13]-[15]. We present a suite of algorithms (MC) that utilize
the MFMC algorithm in partitioning the network into two
sub-networks, where the first one has the source whereas theother has the destination, by explicitly identifying thebottleneck edges that is represented by the minimum numberof edges which hold the maximum possible flow across thecut. The MC algorithms runs the MFMC algorithm
recursively and chooses one edge from the cut set to be partof the path in each run which consequently construct a paththat connects the source node to the destination. By repeating
the process of slicing the network using MFMC algorithm,
we are explicitly identifying the bottleneck edges in the
network through path construction process.
We evaluate all of our variant MC algorithms in our
simulation by comparing the result of our measured objectivefunctions to the results obtained by variant shortest path
algorithms. These simulations evaluate our algorithms under
different flow size distributions and under high load
networks. The Shortest Path algorithms we evaluated are:shortest path with minimum hops, shortest path withminimum distance, and the shortest path based on the least
congested links. In our evaluations, we adopted two types ofeach SP algorithm, either a static algorithm or dynamic which
consider edge availability when routing. The SP variant
algorithms are: Dynamic Shortest Path with Minimum Hop(DSP-mh) and Static Shortest Path with Minimum Hop(SSP-mh), Dynamic Shortest Path with Minimum Distance
DSP-md and Static Shortest Path with Minimum Distance
SSP-md, and the invert version of that which is the Shortest
Path based on the least congested links, DSP-inv and SSP-inv.
The end-to-end routing problem is simulated as a
non-preemptive routing with no clairvoyant, where the flow
size is only known once released. Moreover, different
variations of the problem were as simulated based on network
topology (Barabasi Albert, Mesh), release time(identical/arbitrary), flow size distribution (Pareto/Gaussian).
Our results showed improvement in minimizing the mean
wait time by 15.1%, the mean slowdown by 5.1%, themaximum completion time (makespan) by 9.6%, andmaximizing the mean throughput by 18.3%. These results
highlight the need to consider other algorithms other than SP.
Our Max-Flow Min-Cut based algorithm (MFMC) proved
to improve our evaluated objective functions in the evaluatednetworks. Therefore, we conclude that by explicitly
considering network congestion in routing decisions yield
better performance.
The remainder of this paper is organized as follows. We
cover the relevant related work in Section ϩ. The Max-Flow
Min-Cut routing algorithm is presented in Section Ϫ. In
Section ϫ  we introduce the simulation environment. In

1406

Section Ϭ we present the simulation results and analysis of
our algorithm. We draw a summary and conclusions in
Section ϭ.
II. RELATED WORK
End-to-end routing algorithms in the traditional networkrouters base picking the next hop on their routing tables.
Different algorithms for end-to-end routing were designed
based on various criteria such as: bandwidth capacity anddelay propagation [16][17], minimize end-to-end delay[18][19], minimize energy consumption [20]-[22], andminimize congestion [23]. All of which work on improving
the network performance through end-to-end routing
algorithms.
One of the oldest routing algorithms is the Shortest Pathalgorithm SP which was designed by Richard Bellman andLester Ford as the Bellman-Ford algorithm in 1958 [24] andEdsger W. Dijkstra as the Dijkstra algorithm in 1956 [25].
Many network routing algorithms have been developed basedon finding the shortest path as part of their algorithm, such as
the Widest Shortest Path (WSP) [26] which finds all shortpaths and picks the path with the widest bandwidth.
Moreover, the Shortest-Widest Path [27] is the opposite ofthe (WSP), where all edges less than the required bandwidth
capacity are removed then the shortest path is selected using
Dijkstra’s algorithm.
There are two variations of any shortest path algorithm
based on considering the network link state or not which isreferred to as the static shortest path or the dynamic shortest
path algorithms. The Static Shortest Path (SSP) calculates thepath using the topological state of the network which ignores
the links availability; however, the Dynamic Shortest Pathalgorithms consider the link state in their calculations.
According to Chang H. S. et al. [28], the static algorithm
minimizes the overhead of requesting updated link states,
however, the dynamic approaches are far superior than static
approaches.
In an arbitrary network, routing a set of arbitrary flows
over it demands applying routing algorithms which arecapable of achieving the goal of utilizing the network
bandwidth. Routing algorithms usually are either based onusing static link cost or dynamic link cost. For the static linkstate algorithms, for instance the Minimum Hop Routing
Algorithm (MHR) [29][30] and Static Shortest Path
Algorithm (SSP) [31], are finding short paths as on theminimum number of hops to reach the destination or as theshortest distance, however, they do not consider bottleneck
congestion in their calculations.
In Software Defined Networks, these types of algorithms
are preferable in case minimizing the update messages
overhead of the link states between the controller and the
network switches is under concern. However, thesealgorithms congest the network by neglecting the fact of link
availability while routing through sending flows with same
source and destination over the same path. Akin andKorkmaz [32] listed some of the dynamic shortest path
algorithms which are based on considering link bandwidthavailability, dynamic link cost algorithms, such as Dynamic
Shortest Path (DSP). Even though the dynamic link costalgorithms would cause overhead communications by

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

sending link state update messages with the network switches
in the SDN network, with known flows these algorithms
utilize network bandwidth and balance load. In the online
flow setting where the future flows are not known, theirperformance is not guaranteed. Our work is an extension of
the line of research in the end-to-end routing problem withknown flows in SDN controlled networks where we compare
our proposed algorithm with the DSP and MHR algorithms.
In a huge network such as the World Wide Web, flows
tend to be heavier tailed with an approximately (D=1) [33].
Self-similarity traffic tends to be heavy tailed distribution filesize, proved by Park K. et al. [34]. Consequently, that
self-similarity effects the network performance. The Pareto
flow size distribution is a one common heavy tailed
distribution and for that we adopt it when we generate ourflows in the simulation to evaluate our novel MFMC
algorithm.
The network layer fragments large data files before
transmitting them to fit the packet frame which does notexceed the Maximum Transfer Unit (MTU). The larger data
size the more packet frames created to transfer that data.
Previous studies shown that the packet size effect the network
performance, where the size of a packet can degrade thethroughput once it exceeds its dedicated packet size [35].
Since the space allocation for that packet to be transferred
may be doubled in size which waste an unused space
allocation. For instance, for a packet frame size of 10 and aflow size of 12 the MTU will cause the flow to be fragmented
in two frames where the second one has 8 unused spaces,
which doesn't utilize network resources. According to Lin etal. [36] sending large files over the network while
maintaining high quality of service, one must select anintelligent routing and scheduling algorithms for fast transfer
and better network utilization.
The shortest path algorithm has been widely adopted withboth traditional networks and SDN controlled networks.
Since it is widely applied in many routing algorithms, wechoose to compare our algorithm performance to it.

III. MAX-FLOW MIN-CUT ROUTING ALGORITHM
In the 1950s, Ford Fulkerson and Elias-Feinstein-Shannon[37] developed the Max Flow Min Cut (MFMC) theory.
Researchers since then have worked on a lot of algebraic
topology versions made of MFMC [38][39]. The MFMC
algorithm runs in polynomial time with a complexity
measured by the number of edges E multiplied by the
maximum number of flows f*, represented by (O(E f*)). To
find the min cut edges in a graph of 50 nodes, assume eachnode sends out 10 flows, it is expected to consume 122 ms on
a Microchip PIC at 5MIPS, while in a graph of 1000 nodes it
is expected to consume 49.95 ms on Intel Core i7 with
100,000 MIPS [40].
Routing flows in a network is performed in two folds:
choosing which flow to route from the flow set and then finda path to route that flow over it. Picking a flow from the flow
set in our simulation is based on picking the shortest flow
first to process and in case more than one flow have the same
flow size then we pick one of them randomly. Considering
consistency in our algorithm’s evaluation, we use the same1407

flow pick order across the evaluated algorithms. After
picking a flow, the next process step is defining the path forthat flow where we use our novel Max-Flow Min-Cut(MFMC) algorithm [41][42].
A general description of our MFMC algorithm is definedin Algorithm 1. Our algorithm is based on a recursive processof slicing the network graph by removing the edges in the cut
set. The edges in the cut set represent the minimum numberof edges which with their removal the graph gets
disconnected. By picking one edge from the cut set in eachloop we construct the path. We base our edge selection on
one of these categories: random selection, shortest pathselection, and capacity-based selection.
In an arbitrary network, to find a path that connects
between two nodes (s, d), we use our Max-Flow Min-Cut
routing algorithm as defined in Algorithm 1. Given {G} as an
undirected graph and a flow fi to be routed over G,
furthermore, knowing that the set Cut holds the minimum
number of edges with the maximum flow obtained byfollowing the min cut algorithm, the list path holds theselected edges from each cut set which defines the flow pathfrom the source node s to the destination node d for the flowfi.
Algorithm 1 General Min-Cut Routing Algorithm
1: find a path in G from source s to destination d.
2: Input: undirected graph G and a flow fi=(s,d)
3: Output: a path in G for fi that connects s to d.
4: function: FIND PATH IN GRAPH (G,f,path)5:
if {Connectivity(fs,fd)} =False then6:
return path = Null
7:
else
8:
Cut= get minimum edge cut between (s,d)
9:Partition G into G` and G`` by removing edges in
cut from G.
10:pick edge e=(e1,e2) from cut.
11:
append e to path list
12:
if (fs,fd) ==e then13:
return path
14:
else
15:
set new fs = e2 and new fd =e1
16:
set f`=( fs ,new fd) and f``=( new fs ,fd)17:
if G` >1 then18:
FIND PATH IN GRAPH (G`,f`,path)19:
if G`` >1 then20:
FIND PATH IN GRAPH (G``,f``,path)
In our recursive process of cutting the network edges,
during each step we result in having a set of edges each one
could be a possible edge within the path. Our suit ofalgorithms MFMC have variant variations based on the edge
selection criteria used for each algorithm. In this paper we
present seven different variations as stated in Table Ϩ.
In our variant variations of MFMC, we are constructing
different paths based on the edge selection criteria adopted by
the algorithm. In random based selection algorithms, MC1
and MC2, the edge selection criteria from the cut set are
based on randomness. However, algorithm MC1 does notconsider cycles within the process and remove cycles after
constructing the path, while MC2 removes any edge that may
cause cycles from the candidate edges in the cut set.
In shortest path selection algorithm, the MC3, the edge that
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

is selected from the cut set is the edge that leads to anavailable shortest path. The difference between a path
constructed by this algorithm and a DSP-md is that MC3
explicitly consider capacity availability when routing which
reduce network congestion caused by high traffic demand.

Table I
Edge selection criteria
Algorithm
MC1
MC2
MC3
MC4
MC5
MC6
MC7

Edge selection criteria                     Ǥ             Ǥ          
Ǥ                                    

The third category in our edge selection-based criteria is
capacity-based selection. We have four variant algorithms
based on this category: MC4, MC5, MC6, and MC7. Whenconsidering edge capacity, we have another twoclassification which consider static edge capacity or dynamicedge capacity. In the static edge capacity, we select the edge
with minimum capacity with MC6 algorithm and we selectthe edge with maximum capacity with MC7. However, in the
dynamic edge capacity, we consider the available remainingedge capacity of an edge in the cut set. Furthermore, choosing
the edge with minimum remaining capacity is MC4algorithm and choosing the edge with maximum remaining
capacity is MC5. The MC6 and MC7 algorithms consider theedge capacity, regardless of available amount of capacity at
the time of the cut, where MC6 select the edge with theminimum capacity in the cut set while MC7 selects the one
with maximum capacity. When an algorithm considers theedge capacity, it is actually considering the most congested or
the least congested. When routing using MC4 the flows tend
to use the remaining capacity available of an edge to send theflow over it while in MC5 the flow tends to load balance bysending the flows over the least congested paths.

IV. SIMULATION ENVIRONMENT
In our simulation, the network is modeled as an undirected
graph ሺǡሻ, where the set of nodes  are connected byedges from the edge list . Each edge ሺǦͳǡ ሻ ischaracterized by two nodes Ǧͳ   and hold a capacityሺሺሻ!Ͳሻ. The flows are in our model are defined by the set
flowsሺ ሻ, where each fi F is characterized by a source node
indicated as ሺሻ and the destination node by ሺሻ. The flow
path which connects ሺሻ to ሺሻ is defined by the set ǡ whereሺൌሼሺǡͳሻǡሺͳǡʹሻǡǥǡሺǡሻሽሻǤ The path  bandwidth isindicated by ሺሺሻሻ which defines the minimum edge
capacity in the path .
Two network topologies are simulated to evaluate theproposed algorithms, the Max-Flow Min-Cut suit ofalgorithms MFMC, the Mesh network and the Barabasi
Albert network. The Mesh network, modeled as graph , is1408

the network where in general each node  is connected to atleast one node in . The full connected mesh network is themesh network where each node  is connected with every
other node in  and it has an edge degree of Ǧͳ, otherwise
we call it partial connected mesh network. We simulate apartial connected mesh network which gives a distinctive
result in calculating an objective function such as
maximizing the mean throughput compared to the fullconnected mesh, therefore, we choose the partial connectedmesh network with an edge degree of Ȁʹ.
The second network topology we simulate is the Barabasi
Albert (BA) network which is based on generating networks
with power law degree distribution. The power law degree
distribution starts with a minimum two nodes n1 and n2 then it
starts to expand by adding one node at a time with a degree of
, where ȁȁǤ Every new node has the choice to beconnected with another with higher degree in the network
which highlight node popularity or most visited [40].
The MFMC algorithms are evaluated over these
two-network topology, the partial connected Mesh and theBarabasi Albert network. Our simulator is developed in
Python language and to generate our network graphs, wehave used the NetworkX library1 to develop graph nodes andedges and used its built-in functions to maintain the graphs.
The network size ranges from 10 to 50 nodes and the edges
capacity are generated randomly, where each edge capacity is bound by ሺͳdሺሻdͳͲሻǤ The flows are generated randomly
by randomly selecting a source node  and a destination node in a uniform distribution.
The problem, as defined earlier, with having a network
graph and a set of flows we need to non-preemptively
route those flows oversuch that we optimize a set ofdefined objective functions. These objective functions aredefined in Equations (1) to (5). The source node isrepresented as  and the destination as . The time is , whichis measured in unit time, therefore, flow release time isdenoted by ǡ flow completed or finished time by and the
maximum completion time by ሺ ሻǤ The minimum travel
time between  and is denoted by ǡ as defined in Equation(1), and the flow size and path length are denoted by ሺሻ andሺሻ, respectively. Our simulated objective functions
represent an optimization factor which calculates network
performance such as: the mean wait time representing
average delay rate WT, the mean throughput TH, the mean
slowdown SD, the mean makespan MS, and the maximum
completion time (max(MS)).
Tti = l(fi) + l(pi) - 1
WTi = Fti – Rti - Tti
MSi = Fti - Rti
THi = l(fi)/(Fti – Rti)
SDi = (Fti – Rti)/Tti

(1)
(2)
(3)
(4)
(5)

In our simulation each node in the graph could be either asource node, a destination node or a router. Flows are
generated randomly by randomly picking a source node and a
destination node and define its release time either randomlyor all flows release times are set to zero. The size of each flow
is defined by either a Gaussian distribution or Pareto1

https://networkx.github.io

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

distribution. The parameters for the Gaussian distribution are
set asሺPൌͷሻ and ሺwൌͳሻ and for the Pareto distribution, torepresent very long flows as large files in the network, is set
as (D=1).
The algorithms, the MFMC suit of algorithms and the
Shortest Path SP algorithms, are evaluated under different setof problem scenarios. Under different network topology, theflows could be generated with all release times set to zeroሺൌͲሻ to evaluate the algorithms under a huge burst offlows in the network, otherwise, flows are released inarbitrary times set by a uniform distribution where ሺtͲሻǤInboth scenarios the flow size is set by either a Gaussian flow
size distribution or a Pareto flow size distribution.
In simulating a non-preemptive scheduling problem with a
non-clairvoyant constrain, we simulate an uninterruptible
flow packets sequence where the flows are not known before
they are released. In the non-clairvoyant we hold noknowledge of the future network flows in advance, therefore,
the flows are visible to the scheduler once they are released.

1409

and maximizing the mean Throughput by 5.93%. The MC6
and MC7 algorithms in a sense are the static version of MC4
and MC5 since they consider the minimum/maximum edge
capacity instead of the minimum/maximum available edge
capacity, therefore, for the rest of the analysis section weomit it from our figures for better visibility. Detailed analysisof these algorithms is stated in Table ϩ, where denote themean wait time by (MWT), the mean slowdown by (MSD),
the mean throughput by (MTH), and the maximum
completion time by (Makespan).

V. RESULTS AND ANALYSISOur Max-Flow Min-Cut based routing algorithm MFMC isevaluated within different problem variations based on the
network and flow characteristics. MFMC is evaluated against
different Shortest Path algorithms which consider short innumber of hops or short in distance. These shortest path
algorithms are noted as: (SSP-md/DSP-md), which calculates
the Static/Dynamic Shortest Path based on minimum
distance, (SSP-mh/DSP-mh), which calculates theStatic/Dynamic Shortest Path based on minimum hops, and(SSP-inv/DSP-inv) which chooses the most congested path in
the network by applying Dijkstra's algorithm over the
inverted edge weights. With this kind of evaluation in variant
problem formulation, it gives clear evaluation of ouralgorithm performance.
The performance of the algorithms was measured by the
results of the underlying objective functions. We haveevaluated four objective functions: We evaluated four
objective functions: minimizing the Mean Wait Time,minimizing the Mean Slowdown, minimizing the maximum
completion time (makespan) and maximizing the Mean
Throughput.
The performance of the dynamic routing algorithms
performs better than the static algorithms because they takethe network link states in consideration when finding thepath. We have evaluated the performance of the three shortest
path algorithms for both static state and dynamic. For
example, in a Barabasi Albert network, as in Fig 1, we show
the results of running the dynamic shortest path algorithms
next to the static once in minimizing the mean wait time.
In the results and analysis of our MFMC algorithms, we omit
the static version of the shortest path algorithms for
simplicity. Furthermore, we compared our MFMC suit ofalgorithms to each other where we have found that MC3
performed better than the rest of MFMC algorithms. As an
illustration we show in Fig 2, the mean wait time is
minimized by MC3 on average more than the rest of MFMC
algorithms by 28.2%, minimize the mean slowdown by
7.24%, minimizing the maximum completion time by 18.5%

Fig. 1. Comparing the performance of Static Shortest Path (SSP)
algorithms with the Dynamic Shortest Path (DSP) algorithms inminimizing the mean wait time

Fig. 2. Comparing the performance of our MFMC suit of algorithms
in minimizing the mean wait time

maximizing the mean Throughput by 5.93%. The MC6 andMC7 algorithms in a sense are the static version of MC4 andMC5 since they consider the minimum/maximum edge
capacity instead of the minimum/maximum available edge
capacity, therefore, for the rest of the analysis section weomit it from our figures for better visibility. Detailed analysisof these algorithms is stated in Table ϩ, where denote themean wait time by (MWT), the mean slowdown by (MSD),
the mean throughput by (MTH), and the maximum
completion time by (Makespan).
In minimizing the mean wait time regardless of network
topology we have found that our MC3 algorithm minimized
the mean wait time more than the DSP-md, DSP-mh, DSP-inv
on average by 18.4%, 11.7% and 15.3%, respectively.
However, the remaining of the MFMC algorithms on
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1410

Table II
Comparing Max-Flow Min-Cut Algorithm to the Path Finding Algorithms
Objective
Function

NetworkType

Minimize
MWT

BA

Maximize
MTH

MC3

MC4

MC5

MC6

MC7

12.6%

-14.5%

25.2%

8.9%

27.7%

24.8%

Pareto-5.6%-23.6%

7.5%

-3.1%

2.6%

Gaussian

37.5%-16.1%

36.9%

20.5%

Pareto-10.2%-25.1%

4.9%

BA

Gaussian

1.8%

-4.8%

Pareto

-4.1%

Mesh

Gaussian

4.7%

Pareto-2.9%

Gaussian
ParetoBA

MeshMinimizemakespan

Percent difference with DSP-mh

MC1
&2

Mesh

Minimize
MSD

Percent difference with DSP-md

Flow Size
Distribution
Gaussian

BA

Mesh

MC1&2

MC3

MC4

MC5

MC6

MC7

22.6%

-7.0%

10.6%

10.7%

-10.4%

28.5%

20.7%

26.0%

-8.4%

-4.7%

3.8%

-4.2%

7.0%

-10.7%

6.6%

0.7%

6.7%

5.6%

4.5%

-2.3%

36.3
%
26.1
%
49.5
%
25.1
%
9.4%

18.6
%
13.7
%
31.4
%
13.6
%
3.4%

38.9
%
20.4
%
40.2
%
23.7
%
9.6%

35.8
%
29.7
%
31.7
%
14.2
%
8.4%

-7.3%

-3.8%

-4.9%

-3.3%

-2.5%

2.5%

-0.8%

2.9%

1.8%

3.5%

4.3%

-5.9%

8.3%

3.1%

5.9%

2.9%

9.1%

-2.0%

12.9%

7.5%

7.2%

-7.4%

1.1%

-3.2%

2.4%

-2.8%

3.8%

-0.9%

8.1%

3.5%

10.4
%
9.5%

3.9%

-5.2%

6.8%

-3.7%

7.3%

-1.9%

-1.8%

-8.9%

2.6%

-7.5%

3.1%

-5.8%

-5.6%

1.3%

15.6%

4.8%

9.4%

7.8%

8.5%

-3.4%

4.5%

-5.3%

-1.1%

-2.6%

-2.0%

Gaussian

-4.9%

7.8%

-1.1%

7.1%

2.5%

3.3%

-5.0%

7.7%

-1.3%

6.7%

2.3%

3.2%

Pareto

-4.2%

10.4%

-1.3%

11.6%

1.8%

3.6%

-9.6%

4.2%

-7.0%

5.3%

-4.0%

-2.3%

Gaussian

7.1%

-7.7%

11.1%

6.6%

14.4%

12.9%

11.8%

-3.7%

16.0%

Pareto

3.1%

-5.3%

16.2%

3.11%

27.5%

11.1%

11.7%

2.5%

27.4%

Gaussian

29.3%-10.9%

11.8%

-0.7%

16.4%

7.3%

36.7%

-1.0%

24.2%

11.3
%
11.7
%
10.4
%

Pareto21.8%-10.6%

8.2%

-9.9%

10.3%

-2.7%

28.8%

-5.5%

14.3%

19.1
%
31.1
%
29.2
%
16.5
%

17.8
%
20.3
%
19.2
%
2.9%

average have increased the mean wait time compared with all
DSP algorithms by 16.4%. In addition, comparing theperformance of the algorithms with different flow size
distributions we have found that the mean wait time is
minimized with flows with Pareto size distribution more thanwith flows of Gaussian size distribution by 20.6% and 16.1%,
respectively.
We illustration our results in the objective function
minimizing the mean wait time with the example: having a
Barabasi Albert network with arbitrary release time flows
with Pareto flow size as in Fig 3. The Red line represent ourMC3 algorithm and the gray lines represent the othervariation of MFMC and we compare them to the dynamic
algorithms, DSP-md, DSP-mh, DSP-inv.
The analysis result in minimizing the mean slowdown wealso found that MC3 algorithm minimized this objectivefunction more than the DSP-md, DSP-mh, DSP-inv on
average by 6.4%, 1.5% and 7.2%, respectively. Moreover,the mean slowdown is again minimized by Pareto flow size
distribution more than Gaussian flow size distribution by
8.4%, respectively. However, the remaining MFMC
algorithms have increased the mean slowdown over all DSP
on average by 5% as illustrated in Fig 4.
With the aim to maximize the mean throughput, regardlessof network topology, our analysis shows that our MC3algorithm has maximized the mean throughput more than all
DSP on average by 18.3%. Moreover, comparing the resultsof this objective function between flows with Pareto size
distribution and flows with Gaussian size distribution theyboth performed the same by 18.6% and 17.2%, respectively.
However, MC1, MC2 and MC4 have not increased the mean

-4.8%

throughput over the DSP, it has minimized it by 2.7%. As for
MC5, MC6 and MC7, they maximized the mean throughput
more than all DSP on average by 2.4%.

Fig. 3. Mean wait time in Barabasi Albert network with arbitrary
release time flows and Pareto size distribution

We illustration our results in the objective function
maximizing the mean throughput with the example: having a
Barabasi Albert network with arbitrary release time flows
with Pareto flow size as in Fig 5. The Red line represent ourMC3 algorithm and the gray lines represent the othervariation of MFMC and we compare them to the dynamic
algorithms, DSP-md, DSP-mh, and DSP-inv.
In minimizing the maximum completion time (makespan),regardless of network topology, we also found that our MC3algorithm minimizes the maximum completion time morethan all DSP on average by 9.6%. Moreover, this objective
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

function is minimized with flows of Pareto size distribution
more than with flows of Gaussian size distribution by 18.2%
and 7.5%, respectively. However, the remaining MFMC
algorithms have increased the makespan over the DSP on
average by 5.7%.

1411

MFMC algorithms and we compare them to the dynamic
shortest path algorithms as well.
In our various simulations we have found that constraint
on flows release time, wither arbitrary or identical, had no
impact on the evaluation on our objective functions.
Moreover, selecting the edge from the cut set in our MFMC
algorithms based on being part of a minimum hop path
optimized the network performance in minimizing the mean
wait time, minimizing the mean slowdown, minimizing the
maximum completion time and maximizing the mean
throughput.
VI. CONCLUSION

Fig. 4. Mean Slowdown in Barabasi Albert network with arbitrary
release time flows and Pareto size distribution

Fig. 5. Mean Throughput in Barabasi Albert network with arbitrary
release time flows and Pareto size distribution

Fig. 6. Mean Maximum Completion Time in Barabasi Albert
network with arbitrary release time flows and Pareto size
distribution

In Fig 6, we illustrate the results of minimizing the
maximum completion time in a Barabasi Albert network withflows released in arbitrary times with Pareto flow size
distribution. Again, the red line represents our MC3algorithm while the gray lines represent the remaining

In summary, we present seven Max-Flow Min-Cut(MFMC) based algorithms that solve the flow routing
problem in the Software Defined Network Controller. The
performance of end-to-end routing in the Software Define
Network controllers can be maximized with the use of arouting algorithm that is fast and near optimal. Thesealgorithms identify potential traffic congestion using several
MFMC cut through their choice of edges in constructing theroute to send flows over them. The route construction
mechanism is based on one of three categories: random
selection, capacity-based selection, and shortest pathselection. In random selection, the path is constructed byrandomly picking edges from the cut set which our results
didn’t show improvements in the network objectivefunctions. However, with capacity-based selection the flows
tend either to route through the most congested paths withminimum capacity or they tend to balance network load byrouting through maximum edge capacity. In the third
category, we considered shortest path-based algorithm where
we have found that MC3 algorithm optimized the network
performance, such that the mean wait time is reduced by
15.1%, the mean slowdown is reduced by 5.1%, reducing the
maximum completion time by 9.6%, and increasing the mean
throughput by 18.3%. By comparing shortest path based
selection to the capacity based selection, we found that MC3
performed better which indicates that topology based
decision is more important than capacity based in choosing
an edge from the cut set.
The evaluation results of our simulation showed that ournovel Max-Flow Min-Cut (MFMC) algorithm with an edge
selection based on shortest path which picks the edge in the
cut set with minimum hops provides superior performance
contrast to the Shortest Path SP algorithm variants. Using SP
algorithms for network routing often degrade the network
performance due to the bottlenecks it causes by sending mostflows over the most congested links which minimizes thethroughput and increase the slowdown. These simulations
show that by explicitly identifying bottlenecks in a routing
algorithm through the least available flows in the cut edges
along with picking the edge that is part of potential shortest
path yield better network performance. Our MFMC iscapable to finding the path which result in significant
improvement over the commonly used shortest path
algorithms. We have demonstrated with our MFMC
algorithms that choosing the edges that leads to shortest path,such as in MC3, yields better performance across a broad
range network types and flow size distributions compared toa balanced or longer path approach.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

REFERENCES[1]

Sugam Agarwal, Murali Kodialam, and TV Lakshman. Trafficengineering in software defined networks. In 2013 Proceedings IEEE
INFOCOM, pages 2211–2219. IEEE, 2013[2] C. J. Bernardos, A. de la Oliva, P. Serrano, A. Banchs, L. M. Contreras,
H. Jin, and J. C. Zúniga. An architecture for software defined wirelessnetworking. IEEE Wireless Communications, 21(3):52–61, June 2014[3] Nick Feamster, Jennifer Rexford, and Ellen Zegura. The Road to SDN:
An Intellectual History of Programmable Networks.
SIGCOMMComput. Commun. Rev., 44(2):87–98, Apr 2014.
[4] N. McKeown. How SDN will shape networking, Oct 2011.
[5] Matthew Caesar, Donald Caldwell, Nick Feamster, Jennifer Rexford,
Aman Shaikh, and Jacobus van der Merwe. Design andimplementation of a routing control platform. In Proceedings of the
2nd conference on Symposium on Networked Systems Design &Implementation-Volume 2, pages 15–28. USENIX Association, 2005
[6] H. Kim and N. Feamster. Improving network management with
software defined networking. IEEE Communications Magazine,
51(2):114–119, February 2013[7] D. Kreutz, F. M. V. Ramos, P. E. Veríssimo, C. E. Rothenberg, S.
Azodolmolky, and S. Uhlig. Software Defined Networking: A
Comprehensive Survey. Proceedings of the IEEE, 103(1):14–76, Jan
2015.
[8] Nick McKeown, Tom Anderson, Hari Balakrishnan, Guru Parulkar,
Larry Peterson, Jennifer Rexford, Scott Shenker, and Jonathan Turner.
Openflow: Enabling innovation in campus networks.
SIGCOMMComput. Commun. Rev., 38(2):69–74, March 2008.
[9] A. Detti, C. Pisa, S. Salsano, and N. Blefari Melazzi. Wireless Mesh
Software Defined Networks (wmSDN). In 2013 IEEE 9thInternational Conference on Wireless and Mobile Computing,
Networking and Communications (WiMob), pages 89–95, Oct 2013.
[10] Q. Chen, K. Zheng, F. Ouyang, X. Gan, Y. Xu, and X. Tian. A shortest
path routing algorithm based on virtual coordinate in NELs. In 2016
8th International Conference on Wireless Communications Signal
Processing (WCSP), pages 1–5, Oct 2016.
[11] A. Nanda and A. K. Rath. Cost effective mod leach-a x2217; searchalgorithm for shortest path routing in wireless sensor networks. In 2016
Sixth International Symposium on Embedded Computing and System
Design (ISED), pages 147–151, Dec 2016.
[12] Dexiang Xie, Haibo Zhu, Lin Yan, Si Yuan, and Junqiao Zhang. An
improved Dijkstra algorithm in GIS application. In World Automation
Congress 2012, pages 167–169, June 2012.
[13] L. R. Ford and D. R. Fulkerson. Network flow and systems ofrepresentatives. Canadian Journal of Mathematics, 10:78–84, 1958
[14] Lester Randolph Ford and Delbert R Fulkerson. Maximal flow througha network. Canadian journal of Mathematics, 8:399–404, 1956.
[15] Lester R Ford Jr. Network flow theory. Technical report, Rand
CorpSanta Monica Ca, 1956.
[16] P. Huang, C. Wang, and L. Xiao. Improving end-to-end routing
perfor-mance of greedy forwarding in sensor networks. IEEE
Transactions on Parallel and Distributed Systems, 23(3):556–563,
March 2012.
[17] Nageswara S. V. Rao, Sridhar Radhakrishnan, and Bang-Young Choel.
Netlets: Measurement-based routing for end-to-end performance overthe internet. In Pascal Lorenz, editor, Networking ICN 2001,
pages184–193, Berlin, Heidelberg, 2001. Springer[18] L. Wang and H. Liang. Research and Improvement of the WirelessSensor Network Routing Algorithm GPSR. In 2012 International
Conference on Computing, Measurement, Control and Sensor
Network, pages 83–86, July 2012.
[19] Y.-F. Wen and Y.-S. Lin. Fair bandwidth allocation and end-to-end
delay routing algorithms for wireless mesh networks. IEICE
Transactions on Communications, E90-B(5):1042–1051, 2007[20] T. N. Nagabhushan, S. P. S. Prakash, and K. Krinkin. Power-saving
routing algorithms in wireless mesh networks: A survey. In 2012 11 th
Conference of Open Innovations Association (FRUCT), pages
107–115, April 2012[21] Rajan Sharma. ANN Based Framework for Energy Efficient Routing
In Multi-Hop WSNs. International Journal of Advanced Research in
Computer Science, 8(5), 05 2017[22] Hua R. Wu and Li Zhu. A wireless sensor network routing algorithm
based on Zigbee. Applied Mechanics and Materials,
513-517:1845–1849, 2014.
[23] Abdeljalil Rachadi, Mohamed Jedra, and Noureddine Zahid.
Self-avoiding paths routing algorithm in scale-free networks. Chaos:
An Interdisciplinary Journal of Nonlinear Science, 23(1), 2013.
[24] Richard Bellman. On a routing problem. Quarterly of Applied
Mathematics, 16(1):87–90, 1958

1412

[25] Donald B Johnson. A note on Dijkstra’s shortest path algorithm.
Journal of the ACM (JACM), 20(3):385–388, 1973.
[26] Jang-Ping Sheu, Quan-Xiang Zeng, R. Jagadeesha, and Yeh-Cheng
Chang. Efficient unicast routing algorithms in software-defined
net-working. In 2016 European Conference on Networks andCommunications (EuCNC), pages 377–381, June 2016.
[27] Man-Ching Yuen, Weijia Jia, and Chi-Chung Cheung. Simple
math-ematical modeling of efficient path selection for qos routing inload balancing. In 2004 IEEE International Conference on Multimedia
and Expo (ICME) (IEEE Cat. No. 04TH8763), volume 1, pages
217–220. IEEE, 2004.
[28] Hong Seong Chang, Byoung Wan Kim, Chang Gun Lee, Sang Lyul
Min, Yanghee Choi, Hyun Suk Yang, Doug Nyun Kim, and Chong
Sang Kim. Performance comparison of static routing and dynamic
routing in low-earth orbit satellite networks. In Proceedings ofVehicular Technology Conference - VTC, volume 2, pages 1240–1243
vol.2, 1996.
[29] Robert W Floyd. Algorithm 97: shortest path. Communications of theACM, 5(6):345, 1962.
[30] J. J Garcia-Luna-Aceves. A minimum-hop routing algorithm based on
distributed information. Computer Networks and ISDN Systems,16(5):367 – 382, 1989..
[31] R. A. Guerin, A. Orda, and D. Williams. Qos routing mechanisms andospf extensions. In GLOBECOM 97. IEEE Global
Telecommunications Conference. Conference Record, volume 3, pages
1903–1908 vol.3, Nov1997[32] E. Akin and T. Korkmaz. Comparison of Routing Algorithms with
Static and Dynamic Link Cost in Software Defined Networking(SDN). IEEE Access, 7:148629–148644, 2019.
[33] Martin F. Arlitt and Carey L. Williamson. Web server work load
characterization: The search for invariants. In Proceedings of the 1996
ACM SIGMETRICS International Conference on Measurement andModeling of Computer Systems, SIGMETRICS ’96, page 126–137,
New York, NY, USA, 1996. Association for Computing Machinery.
[34] Kihong Park, Gitae Kim, and M. Crovella. On the relationship between
file sizes, transport protocols, and self-similar network traffic. In
Proceedings of 1996 International Conference on Network Protocols(ICNP-96), pages 171–180, 1996.
[35] A. U. Shah, D. H Bhatt, P. R. Agarwal, and P. R. Agarwal. Effect ofpacket-size over network performance. International Journal of
Electronics and Computer Science Engineering, 1:762–766, 2012.
[36] Chuan Lin, Yuanguo Bi, Hai Zhao, Zeshen Wang, and Jinfa Wang.
Scheduling algorithms for time-constrained big-file transfers in theinternet of vehicles. Journal of Communications and Information
Networks, 2(2):126–135, Jun 2017.
[37] R. Ghrist and S. Krishnan. A topological max-flow-min-cut theorem.
In2013 IEEE Global Conference on Signal and Information
Processing, pages 815–818, Dec 2013.
[38] Ravindra K Ahuja, Thomas L Magnanti, and James B Orlin.
Networkflows: Theory, applications and algorithms. Prentice-Hall,Englewood Cliffs, New Jersey, USA Arrow, KJ (1963). Social Choiceand Individual Values, Wiley, New York. Gibbard, A. (1973).
“Manipulation of Voting Schemes: A general result”, Econometrica,
41:587–602, 1993.
[39] Honghua Hannah Yang and D. F. Wong. Efficient Network Flow Based
Min-Cut Balanced Partitioning, pages 521–534. Springer US, Boston,MA, 2003.
[40] S. Tie-li, D. Jing-wei, and D. Kai-ying. Scale-free network modelwith evolving local-world. In 2008 Fourth International Conference
on Natural Computation, volume 1, pages 237–240, Oct 2008[41] Nada Alzaben and Daniel W. Engels. End-to-end routing in SDN
controllers using max-flow min-cut route selection algorithm. In 2021
23rd International Conference on Advanced Communication
Technology (ICACT), pages 461–467. IEEE, 2021
[42] Nada Alzaben and Daniel W. Engels. End-to-end routing approach for
SDN based wireless ad hoc network. In 2021 IEEE Symposium on
wireless technology and applications (ISWTA), 2021, pp. 16-21.
Nada Alzaben. This author became a Member (M’18)
of IEEE in 2018. Saudi citizen born in Riyadh, Saudi
Arabia. Received her B.S. (2001) and M.S. (2012) incomputer science and information from King Saud
Univ. in Riyadh, Saudi Arabia.
She is currently working on her Ph.D. in computerscience at Southern Methodist Univ., Dallas, TX, USA.
Her research interests include network communications,routing and scheduling, network performance analysis.
She is a Lecturer in computer science and informationcollege at Princess Nourah Bint Abdulrahman Univ.,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 10, Issue 1, January 2021

1413

Riyadh, Saudi Arabia. Previously she worked as a senior programmer at
King Saud Univ. She has 5 research publications in the field of computer
networks and AI.
Dr. Daniel W. Engels received his Ph.D. from the
Massachusetts Institute of Technology. Born in
the US.
Dr. Engels is currently Head of AI, Cyber security
Science and Analytics, at HSBC. Previously, Dr.
Engels was a professor at Southern Methodist
University (SMU) where he was one of thecreators of, and the first Director of, the Master of
Science in Data Science (MSDS) program where
he grew the program from 0 students to more than
300 students in its first two years of operation In
addition to his data science expertise, Dr. Engels is
an expert in security and in RFID and IoT technologies, systems, andapplications. He was one of the founders of the IEEE International
Conference on RFID, and he was the Chair of the IEEE TechnicalCommittee on RFID in 2011 and in 2012. Dr. Engels is the former Directorof Research of the Auto-ID Labs at MIT, where he led the development ofseveral RFID protocols including the original “Gen2” protocol, and he is an
original member of the research team that founded the Auto-ID Center at
MIT and created the EPC System.
He has over 100 peer reviewed publications and 8 issued U.S. patents indata science, RFID, RFID applications, Internet of Things, security,
embedded computing, and computer-aided design. Dr. Engels received the2014 AIM Ted Williams Award in recognition of his contributions to the
AIDC industry. Dr. Engels is a member of AIDC 100 and is a Senior
Member of IEEE.

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