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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 GiRI (Global IT Research Institute)

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Anonymity:
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 1

5J

1 Load Adaptive and Fault Tolerant Distributed Stream Processing System for Explosive

745

Stream Data
Myungcheol Lee*, Miyoung Lee*, Sung Jin Hur*, Ikkyun Kim***Big Data SW Research Department, ETRI, Daejeon, Republic of Korea, **Cyber Security System Research
Department, ETRI, Daejeon, Republic of Korea

2 A Triple Hill Cipher Algorithm Proposed to Increase the Security of Encrypted Binary

752

Dataand its Implementation Using FPGA
Ashraf A.M. Khalaf, Mona S. Abd El-karim,Hesham F. A. Hamed
Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia,
Egypt

3 Fast Intra-Beam Switching Scheme using Common Contention Channels in Millimeter-

760

wave based Cellular Systems
Nak Woon Sung*, Yong Seouk Choi**Communications Internet Research Laboratory, ETRI, Daejeon, Korea

4 Improving Beam Distribution Evenness in 3-Dimensional Beamforming with Carrier

766

Aggregation
Jun-woo Kim, Gosan Noh, Jang-won Moon, Youn-ok Park, Ilgyu Kim*Communications Internet Research Laboratory, ETRI, Daejeon, Korea

5 Differentiated Assignment of Extrinsic Information in Iterated Decoding of Fixed Weight772

Codewords
Wonsun Bong*, Yong Cheol Kim*
*Dept. of Electrical and Computer Eng., University of Seoul, Korea

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

745

Load Adaptive and Fault Tolerant Distributed
Stream Processing System for Explosive Stream
Data
Myungcheol Lee*, Miyoung Lee*, Sung Jin Hur*, Ikkyun Kim***Big Data SW Research Department, ETRI(Electronics and Telecommunications Research Institute),
218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700, Republic of Korea** Cyber Security System Research Department, ETRI(Electronics and Telecommunications Research Institute),
218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700, Republic of Korea{mclee, mylee, sjheo, ikkim21}@etri.re.kr

Abstract—As smart devices such as sensors, smartphones,
and CCTVs are becoming extensively utilized recently, stream
data from those smart devices are consistently generated
explosively. There are also increasing cases that we notice
security attacks after already important assets are damaged bycyber-targeted attacks such as APT attacks due to the lack ofreal-time security log processing capability. Accordingly, thedemand to process and analyse the exploding stream data inreal-time and in advance is consistently increasing in many
application domains. However, existing distributed stream
processing systems like Storm and S4 are not well adaptive
when there are drastic increase of input stream data. In thispaper, we propose a distributed stream processing system
which supports several load adaptation techniques utilizable forvarious circumstances of explosive data stream, and also
supports fault tolerance mechanisms to fail over in several
failure situations.
Keyword—Big Data, Distributed Stream Processing, Load
Adaptation, Data Explosion, Load Shedding, Task Scheduling,
Fault Tolerance

I. INTRODUCTIONA

S smart devices such as sensors, smartphones, andCCTVs are becoming extensively utilized recently,
stream data from those smart devices are consistently
generated explosively and the need to process and analyse theexploding data stream in real-time is increasing [1]-[2].
There are also increasing cases such as eBayhacking(2008), Stuxnet(2010), Nonghyup(2011), and recent———————————————————————
Manuscript received April 29, 2015. This work was supported by Institutefor Information & communications Technology Promotion (IITP) grant
funded by the Korea government (MSIP). (B0101-15-1293, Cyber-targeted
attack recognition and trace-back technology based on the long-term historic
analysis of multi-source data)
Myungcheol Lee is with the Electronics and Telecommunications
Research Institute, Daejeon, 305-700, Korea (corresponding author toprovide phone: +82-42-860-1691; fax: +82-42-860-6699; e-mail:mclee@etri.re.kr).
Miyoung Lee is with the Electronics and Telecommunications ResearchInstitute, Daejeon, 305-700, Korea (e-mail: mylee@etri.re.kr).
Sung Jin Hur is with the Electronics and Telecommunications ResearchInstitute, Daejeon, 305-700, Korea (e-mail: sjheo@etri.re.kr).
Ikkyun Kim is with the Electronics and Telecommunications ResearchInstitute, Daejeon, 305-700, Korea (e-mail: ikkim21@etri.re.kr).

3.20 Cyber Incident (2013.03.20) that we notice security
attacks after already important assets are damaged by cybertargeted attacks like APT (Advanced Persistent Threat)attacks. These APT attacks are designed to steal industrial
secrets or military secrets from major government agencies
or enterprises and customer information, and paralyse theindustrial control system and consequently cause tremendous
physical damages, or wage an act of war [3].
According to Verizon data breach report [4] published in
2010, even though there were attack and breach logs left for
86% of breach cases, attacked organization’s attack detection
mechanism was not able to alert security warnings before theactual damage, due to the lack of real-time processing
capability.
For providing real-time data processing and analysis tohandle these explosive large-volume of stream data andpredict future, or detect attack and damages, researches on
real-time distributed stream processing systems such as
Apache Storm [5] and Yahoo S4 [6] are actively being
studied.
Existing distributed stream processing systems support
fundamental real-time stream processing functionalities [7],but are not dynamically scalable enough because they are notadaptive when there are drastic increase of input stream data.
In this paper, we propose a distributed stream processing
system which supports several load adaptation techniques
utilizable for various circumstances of explosive data stream,
and also supports fault tolerance mechanisms to fail over inseveral failure situations.
The remainder of this paper is organized as follows:
Section 2 describes the system architecture and programming
model of our proposed distributed stream processing system.
Section 3 describes several load adaptation techniques for thevarious circumstances of explosive stream data, which wereapplied to our proposed distributed stream processing
system. Section 4 describes several fault tolerance
mechanisms applied to the proposed system. Finally, Section
5 presents the conclusion and future works.
II. DISTRIBUTED STREAM PROCESSING SYSTEM
A. System Architecture
Our proposed techniques were implemented in a

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016746

distributed stream processing system depicted in Fig. 1,
which consists of a service manager, several service manager
candidates, several task execution managers, several task
executors, a cluster coordinator, and a metadata storage.

Fig. 2. Distributed Parallel Stream Processing Model
Fig. 1. Distributed Stream Processing System Architecture

Service manager manages entire cluster and schedules task
instances of user-defined distributed stream processing
service to distributed nodes for parallel execution. Task
execution managers manage task executors on each node, andthe task executors run each assigned task instances as
separate threads in the same process space within the task
executor. Task instances are cloned from user-defined task
and they run on distributed nodes in parallel by sharing andpartitioning the same input data stream.
Cluster coordinator is used for master election in case of
master node’s failure, and for shared storage andcoordination of communication between several clustercomponents. Metadata storage is for the management of all
the data related to the cluster, service, user, and QoS (Qualityof Service) preferences.
B. Programming Model
The distributed stream processing system supports
DAG(Directed Acyclic Graph) based distributed stream
processing programming model to users as in Fig. 2, and theprogramming model contains input sources, task processing
logics and output sources. LamaTask is a DAG node forrepresenting task processing logic, and LamaInput and
LamaOutput are DAG nodes for representing communication
with external input and output sources.
Users write their distributed stream processing service
according to the programming model, and the tasks
containing processing logic belonging to the service aredispatched to distributed nodes and run continuously inparallel as task instances. The data communicated by eachtask instances are represented in key/value-based record
stream.

Common stream data model are defined as LamaRecord sothat users can process various structured and unstructured
stream data generated from diverse application
environments. LamaRecord consists of key and value pair,where key is represented as String, and value is represented
as POJO (Plain Old Java Object) which can be any primitive
Java type or user-defined object type. Programming modelcomponents like input sources, tasks, output sources
exchange data each other as a stream of LamaRecords as inFig. 3.

Fig. 3. Stream Data Model

We provide an abstract class called ILamaTask for users to
implement their processing logic as task in Java language,and users define tasks by extending ILamaTask abstract classand implementing the processing logic inside the
ILamaTask’s execute() method just as defining theMergeTask class in Fig. 4.

Fig. 4. Implementation of User-defined Task

ILamaTask abstract class has other several methods
supporting the implementation of user-defined tasks.
• void prepare(ServiceConf config) : define what needs
to be prepared before initially running execute()
methodCopyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

747•

void execute(InputCollector inputs, OutputCollector
outputs) : user processing logic is implemented insideexecute() method• void declarePorts(PortDeclarer declarer) : define input
port number, output port number, whether tosynchronize all the input ports, etc.

// from any channel
List<LamaRecord> records = inputs.get();for (LamaRecord record : records) {String key = record.getKey();
Object value = record.getValue();// Process key/values here// …
// Generate new key/values here
String newKey = …;
Object newValue = …;// new LamaRecord is created inside emit()
outputs.emit(newKey, newValue);}Fig. 7. Defining Distributed Stream Processing Service

Basic procedure for defining distributed stream processing
service begins with creating LamaService object, and thencreate InputSource object and then register the created input
sourcetotheservice
objectbycalling
LamaService.addInput() as in Fig. 7. All the subsequent tasks
and output sources are registered to the service by callingprevious tasks’ LamaTask.setInput() method.
We support multiple input and output ports for tasks andmultiple channels between ports, and users also define thespecifics of input and output ports when they define tasks.

Fig. 5. Reading and Writing Key/Value Pairs

Fig. 5 depicts how users read and write the stream data
inside execute() method. Users access input data by calling
InputCollector.get(),
LamaRecord.getKey(),and
LamaRecord.getValue(), and send the processing results tonext tasks by calling OutputCollector.emit().
Port is used for data transmission between input sources,task, and output sources as in Fig. 6. There is a channel fordata communication established between preceding port
(output port of any input source or task) and following port
(input port of any output source or task), and the channels cancommunicate only one kind of data, or with the same schema(key, value).

Fig. 6. Port-based Inter-Task Communication

Fig. 8. Relationship between I/O Ports

Relationship between input and output ports are classified
as in Fig. 8. 1 to 1 is for exchanging data directly between 1
input port and 1 output port, and 1 to N is used forbroadcasting data from 1 input port to several output ports,
and M to 1 is used for collecting output data generated fromseveral tasks into a task and perform reduction operation.
Users can choose whether they would accesssynchronously or asynchronously data from multiple input
ports as depicted in Fig. 9. In the synchronous mode,
whenever data are ingested at all the input ports, user-defined
ILamaTask.execute() method is called. Inside the execute()
method, users can access data from each port by calling
InputCollector.get(port) method with port index argument. In
asynchronous mode, whenever data are ingested at any one of
the input ports, user-defined ILamaTask.execute() method iscalled, and users can access the ingested data by calling
InputCollector.get() method without port index argument.

Input source and output source are allowed to have exactly
one input port and one output port, and tasks can have several
input and output ports by user’s definition, but the default
number is one. For example, LamaTask 5 in Fig. 6 has 2 input
ports and 1 output port, and input port 0 has 1 input channel
from LamaTask 1’s second output port and input port 1 has 1
input channel from LamaTask 2’s first output port, andoutput port 0 has 1 output channel to LamaOutput 2’s input
port 0.

Fig. 9. Synchronous and Asynchronous Data Access from Multiple Input
Ports

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

The synchronized access mode between multiple input
ports
ispredefinedusing
PortDeclarer.syncInputPorts(boolean flag) method and the
PortDeclarer is set to the task by overriding
ILamaTask.declarePorts(PortDeclarer
declarer)
when
extending ILamaTask.

Fig. 10. Data Partitioning Scheme

Our proposed distributed stream processing system
supports 2 kinds of partitioning scheme, which are used forpartitioning data from several instances of previous task to
several instances of next task. ROUND_ROBIN scheme isused for load balanced partitioning and SAME_KEY is used
for sending the data with the same key to the same target
instance, and which is useful for receiving the same classes ofdata, as in counting words shown in Fig. 10.
C. Dynamically Optimized Communication Channel
The distributed stream processing system supports
optimized communication channels between task instances
based on their execution location. If the two task instances,which communicate data each other, run on the same node,
they run as separate threads in the same task executor
process, and they exchange data using a common queue as
the non-serialized data object itself. There are no serializationand deserialization overhead required for communicating thedata in this case. On the other hand, if the two task instances,which communicate data each other, run on the different
nodes, they run as separate threads in the different task
executor processes on different nodes, and they exchange
data using TCP-based sockets, and the data need to beserialized to array of bytes using Kryo [8] or Java’s
serialization mechanism [9] and after the transmission, thedata need to be deserialized back to Java objects to beaccessed by the consuming task instances.
In our experimental observation, in the extremely
optimized distributed stream processing system execution,
most of the overhead was generated by the object
serialization
anddeserializationforTCP-based
communication between nodes. Based on this observation,
our proposed system tries to schedule task instances, which
communicate data each other, on the same node as possible.
D. Stream Data Window
Our proposed system provides stream data windowing
function so that users can process unbounded structured andunstructured stream data by limiting them as a sequence ofbounded data batches. Time-based and count-based window
types are supported, and both of the windows are defined by
the window size and how much to slide the window as time
elapses or as data are ingested. In a time-based window,

748

window size and sliding size is defined in the millisecond
unit, and in a count-based window, window size and sliding
size is defined as the number of records.

Fig. 11. Stream Data Window

Window is defined at the input source by calling e.g.
LamaInput.setWindow(window_type,
window_size,
sliding_size) method and the input source slices the input
data as defined by the window definition. Examples of moredetailed window operations are depicted in Fig. 11.
III. LOAD ADAPTATION TECHNIQUESAll the load adaptation techniques applied to the proposed
distributed stream processing system are summarized inTable I, and the detailed explanation for each techniquefollows in the next subsections.
TABLE I
LOAD ADAPTATION TECHNIQUES FOR DISTRIBUTED STREAM PROCESSINGSYSTEMS

PurposeInter-node Load
Balance
Inter-task Load
BalanceTechnique- Load-aware Instance Dispatch- Load-aware CPU Scheduling- Task Split and Merge
Increasing Processing- Task Migration
Capability- Adding More Nodes
Load Shedding- Input Data Shedding

A. Load-aware Instance Dispatch
In a distributed stream processing system, user-definedtasks are split into several task instances for exploiting data
parallelism and dispatched by global scheduler to different
nodes for parallel execution. If we dispatch task instances on
the node with the least load, we can shorten the processing
time and the latency when users access the outputs.

Fig. 12. Load-aware Instance Dispatch

Our global scheduler gathers load status information from

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

each worker nodes and mark their status as “NORMAL” or“OVERLOADED”, and dispatch task instances to“NORMAL” nodes preferentially, for example node2 in Fig.
12 is the most loaded node, therefore the global scheduler
dispatches T2.3 and T2.4 to node1, and T2.5 to node3, and notask instances to node2.
B. Load-aware CPU Scheduling
Task instances dispatched to each nodes are executed bytask executors running on each nodes, and they run as
separate threads inside the task executor processes and havetheir own input queue for receiving data from previous tasks
or input sources.

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D. Task Migration
If a node is fully utilized and the task instances running on
the node cannot be split into more instances, then the global
scheduler migrates the less loaded task instances to anotheridle nodes as in Fig. 15 so that they are not affected by otherheavy task instances. The migrated task instances need tostore their current input queue data and memory status into
the persistent storage for the continuation of execution after
the migration.

Fig. 15. Task Migration

Fig. 13. Load-aware CPU Scheduling

Our local scheduler monitors each input queue’s load, orwhether there are any waiting records. If there are any
waiting records in the queue, it means that the task instancebound to the input queue is not processing fast enough for the
input data rate. Local scheduler marks each instance as“NORMAL”, “YELLOW”, and “RED” according to thenumber of waiting records in their input queue, and try to
make more CPU times assigned to the more loaded task
instance by having the less loaded task instances to sleep for awhile as in Fig. 13.
C. Task Split and Merge
If all the task instances, which belong to the same task, areoverloaded in all the nodes, and there are still available
resource in the cluster, global scheduler increases the data
parallelism of the task by adding more task instances to thetask. The added task instances can share the input data load
and lower the burden of existing task instances as in Fig. 14.

E. Adding More Nodes
If load of all the nodes are close to their limit, systemmanager can add more nodes to the cluster so that entire
cluster can handle more explosive data stream and have more
distributed stream processing capability.
Global scheduler recognizes immediately the dynamically
added nodes by system manager. By system manager’s
option, the global scheduler performs immediate rebalancing
among entire nodes or let the newly added nodes be utilized
for later dispatch only.
When rebalancing is performed, service manager detects
the most loaded node and migrates the task instances runningon the node to the newly added node, and the inputs andoutputs of the migrated task instances are all re-establishedaccording to the new service topology.
If service load becomes lower as time elapses, some of thenodes will become idle, and the idle nodes might be excluded
from the cluster and returned back to free pool by systemmanager’s preference.
F. Load Shedding
In case there are no more resources available, global
scheduler decides to drop data at the input source layer as inFig. 16. Users can specify their satisfaction for service
execution as QoS (Quality of Service) in our system, wherefour QoS items such as acceptable shedding rate, acceptablelatency, whether to do recovery, and finally whether topreserve order between data are supported. Only the services
for which user has specified shedding rate as more than 0 arechosen for load shedding.

Fig. 14. Task Split and Merge

This task split technique can be applied to only the tasks
that their data partitioning is ROUND_ROBIN, and the task
instances before and after the newly added task instances arenotified of the new task instances and their communication
channels are re-established to exchange data with the newtask instances.

Fig. 16. Load Shedding

IV. FAULT TOLERANCE
Our proposed distributed stream processing system
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

supports 3 kinds of fault tolerance techniques (master
failover, node failover, and instance failover) so that users’
distributed stream processing service can seamlessly run
even in the various failure situations.
A. Master Failover
In the normal cluster startup, service manager writes its
address to ZooKeeper’s master address node in ephemeralmode to notify service manager candidates and task
execution managers that it is now the master. All the othercomponents hold watcher on the ZooKeeper’s master address
node. If service manager dies, the address in the master
address node disappears with the node itself because the node
was ephemerally written. And the deletion of the master
address node is notified to all the components which have left
watchers for the node.
All the service manager candidates tries to write theiraddress to the master address node, and one of them wins therace and becomes the new master, and the election of newservice manager is notified to all the other components, andthey try to communicate with the new service manager andreport their status to the new service manager.
Eventually the new service manager gets to know thecluster status and the execution status of all the distributed
stream processing service and task instances seamlessly.
B. Node Failover
If a node is in trouble, it is detected by the global scheduler,
and all the task instances running on the failed node become
the target of the new scheduling. The task instances aredispatched by the global scheduler to other normal nodes andthe communication channels between the task instances andpreceding/following task instances are reestablished using
the instance channel information stored in ZooKeeper.
As a first step, the new task instances try to connect to their
output targets using targets’ channel information acquired
from ZooKeeper, and they create new TCP ports for their
input ports and store the new channel information to
ZooKeeper, and in turn their preceding tasks need to update
their output channels to the new task instances by getting
channel information from ZooKeeper. Thus, only the
instance channel information about receivers are stored in the
ZooKeeper
node
in
the
name
of
“receiver_instance_id@input_port” with the value of“receiver_host_name:tcp_port”.
All the task instances acquire the receivers’ channel
information from ZooKeeper and compare if both of thesenders and receivers run on the same node by comparing the“receiver_host_name” with the host name of their own
execution node. If they run on the same node, the senderestablishes common queue based communication channel
with the receiver. Otherwise, the sender establishes TCP
socket channel to the receiver with the acquired“receiver_host_name:tcp_port” information.
Newly established instance channel information are again
stored in ZooKeeper also for later failover activities.
C. Instance Failover
If a task instance fails suddenly, it is detected by global
scheduler. And the global scheduler dispatches the failed task
instance to another normal node, and the communication
channels between new task instance and preceding/following

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tasks are reestablished as explained in the previous section. If
the task instance continues to fail several times and the failurecount reaches the predefined threshold, the task instance ismarked as failure and excluded from rescheduling since then.
V. CONCLUSION
In this paper, we proposed a new distributed stream
processing system which supports several load adaptation
techniques to process explosive stream data in distributed
stream processing environments and several fault tolerance
techniques for resilient distributed stream processing in thevarious failure situations.
Our proposed system provides stream data model, andstream data programming model, multiport-based
communication mechanism, several data partitioning
schemes, and dynamic optimized communication channels
between tasks. Our proposed load adaptation techniques aredesigned considering various conditions of nodes and tasks,and classified as inter-node load balancing, inter-task loadbalancing, increasing processing capability, and finally load
shedding for the last way to avoid entire system halt due tothe data explosion. Our proposed fault tolerance techniques
are designed considering various failure situations likemaster, node, and instance failure.
We just implemented the load adaptation techniques andthe fault tolerance techniques in the distributed stream
processing system and are going to experiment the usefulnessof them thoroughly in a real-world environment as future
works. We hope our proposed techniques are useful as a hint
for flexibly handling problems in many data processing
domains experiencing input data explosion or fluctuation,and seamlessly providing distributed stream processing
service in various failure situations.
ACKNOWLEDGMENT
This work was supported by Institute for Information &communications Technology Promotion (IITP) grant fundedby the Korea government(MSIP). (B0101-15-1293,
Cyber-targeted attack recognition and trace-back technology
based on the long-term historic analysis of multi-source data)
REFERENCES
[1]

[2]
[3]
[4]

[5]
[6]

[7]

[8]
[9]

Mark A. Beyer, Anne Lapkin, Nocholas Gall, Donald Feinberg,
Valentin T. Sribar, “Big Data Is Only the Beginning of Extreme
Information Management,” Gartner, 2011.
John F. Gantz, “The Diverse and Exploding Digital Universe,” IDC,
2008.
Colin Tankard, "Advanced Persistent threats and how to monitor anddeter them," Network Security, vol. 2011, no. 8, pp. 16-19, 2011.
Verizon Inc., “2010 Data Breach Investigation Report,” 2010,
http://www.verizonenterprise.com/resources/reports/rp_2010-data-br
each-report_en_xg.pdf
Storm Project, Available at https://github.com/nathanmarz/storm/wiki/(referenced at Sep. 1, 2014)
Leonard Neumeyer, Bruce Robbins, Anish Nair, Anans Kesari, “S4:Distributed Stream Computing Platform,” Proc. of ICDMW 2010,
pp.170-177, Sydney, 2010
Miyoung Lee, Wan Choi, “Trends of Big Data Processing Technology
for Big Data Analytics,” Korea Information Processing Society
Review, vol.19, no.2, pp.20-28, 2012 (in Korean)
Kryo Project, Available at http://code.google.com/p/kryo/ (referenced
at Sep. 1, 2014)
Java Object Serialization Spec, Available at
http://docs.oracle.com/javase/8/docs/platform/serialization/spec/serial-arch.html (reference at Sep. 1, 2014)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

751

Myungcheol Lee (M’2015) received his Bachelor’s
Degree in Computer Engineering and his Master’s
Degree in Computer Engineering from Chungnam
National University, Daejeon, Korea in 1999 and2001, respectively. He became a Member (M) of IEEEin 2015. He is now a senior researcher at ETRI since2001. His research interest includes Big Data
processing and analytics, database, cloud computing,and distributed computing.

Miyoung Lee received her Bachelor’s Degree in Foodand Nutrition, and Master’s Degree in Computer
Science and Statistics from Seoul National University,
Seoul, Korea in 1981 and 1983, respectively. She
received her Doctor’s Degree in Computer
Engineering from Chungnam National University,
Daejeon, Korea in 2005. She is now a principal
researcher at ETRI since 1988. Her research interest
includes big data management and processing,database, and distributed computing.
Sung Jin Hur received his Bachelor’s Degree inElectronics, Master’s Degree and Doctor’s Degree in
Computer Engineering from Kyungpook National
University, Daegu, Korea in 1990, 1992 and 1999,
respectively. He was a professor at Changsin
University from 1999 to 2001, and is now a principal
researcher at ETRI since 2001, and is leading DataManagement Research Section. His research interest
includes database, stream data processing, cloud computing

Ikkyun Kim received his Bachelor’s Degree,
Master’s Degree and Doctor’s Degree in Computer
Engineering from Kyungpook National University,
Daegu, Korea in 1994, 1996, and 2009, respectively.
He was a visiting researcher at Purdue University
from 2004 to 2005. He is now a principal researcher
at ETRI since 1996, and is leading Network Security
Research Section. His research interest includes
network security, computer network, cloud security, big data analytics.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016752

A Triple Hill Cipher Algorithm Proposed to
Increase the Security of Encrypted Binary
Dataand its Implementation Using FPGA
Ashraf A.M. Khalaf, Mona S. Abd El-karim,Hesham F. A. Hamed
Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia,
Egypt
ashkhalaf@yahoo.com,engmona1889@gmail.com,hfah66@yahoo.com

Abstract—Encrypted binary data security is an important task
in the field of data communication systems since many decades.
In this paper,we study the security problem and present a
proposed triple hill cipher algorithm and its implementation on
FPGA to encrypt any binary data such as images , audio , video… etc. The proposed algorithm uses three stages of a modified
hill cipher to make the algorithm more robust and gives highlevel security of the data,each stage is considered a block cipher
with a block length of 128 bits and key length of 256 bits. The
message to be encrypted is processed by this block cipher inthree stages. The keys are taken from random number
generator. The proposed algorithm is promising to give better
security.

Keyword—hill cipher , 256 bit key ,cryptography, VHDL,FPGA.

I. INTRODUCTION

Nowadayssecurity becomes an important feature with thegrowth of electronic communication systems.
Cryptography is one of the methods used to protect datafrom unauthorized access and being stolen [1]. Cryptography
is the science and study of secret writing. A cipher is a secretmethod of writing , whereby plaintext is transformed into
cipher text .The process of transforming plaintext into cipher
text is called encryption. The reverse process of transforming
cipher text into plaintext is called decryption. Bothencryption and decryption are controlled by a cryptographic
key or keys [2][3].
There are two types of cryptosystem, which are symmetric
cryptosystem and asymmetric cryptosystem. In Symmetric
cryptosystem, the sender and recipient share the same key. Itmeans the same key is used for encryption and decryption. In
Asymmetric cryptosystem, different keys are used. A public———————————————————————
Manuscript received July 9 , 2015. This work was self-supported, and afollow-up of the invited journal to the accepted conference paper of the
17th International Conference on Advanced Communication
Technology, and without Grants (Self -support).
Ashraf M. Khalaf is with the Faculty of Engineering, Department ofElectrical Engineering. (Corresponding author, Phone: +20 86 2355261;
fax: +20 86 2346674; e-mail: ashkhalaf@yahoo.com).
Mona S. Abd El-karim, is with the Faculty of Engineering, Departementof Electrical Engineering. (Phone: +201116123919; e-mail:engmona1889@gmail.com).
Hesham F. A. Hamedis with the Faculty of Engineering,
Department of Electrical Engineering. (E-mail:hfah66@yahoo.com).

key is used by sender to encrypt the message while therecipient used a private key to decrypt it [1][2].
In this paper we focus on hill cipher which is a type ofsymmetric cryptosystem.
The hill cipher was first described in 1929 by its inventor, themathematician Lester S. Hill, in the journal of the americanmathematical monthly (Eisenberg, 1998).[1][3][4]The hill cipher is a classical symmetric cipher based on
matrix transformation. It has several advantages including its
resistance to frequency analysis and simplicity due to thefact that it uses matrix multiplication and inversion forencryption and decryption. However, it succumbs to the
known plaintext attack [5] and as such there have been
efforts to strengthen the cipher through the use ofvarious techniques which have improved the security of
the cipher quite significantly [6],[7],[8].
In this paper, we present a proposed triple hill cipher
algorithm which consists of three stages of hill cipher ,eachstage is considered a block cipher with a block length of 128
bits and key length of 256 bits. The message to be encrypted
is processed by this block cipher in three stages to increase
the security. The keys are taken from random number
generator. Each stage consists of eight rounds with different
eight keys , in each round three operations are implemented ;key and plaintext matrix multiplication , stir operation and
XOR operation . this will be discussed in details in section III.we expect to achieve an algorithm more robust to
cryptanalysis as we will use three layers of security.
The reason for using three stages is that, we used the conceptof the common triple DES algorithm which is standardized
by ANS X9.52 and is used to enhance the DES algorithm[12].

II. THE BASIC CONCEPT OF THE CLASSICAL HILLCIPHER
Hill Cipher was the first polygraphic cipher in which thekey (K), plain text (P), and cipher text (C) are represented
in the form of matrices. The basic method of encryption
and decryption is represented by the following
equations[9][10].

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

C = (K P ) mod( 26)
P = (K −1 C )mod (26)

(1)
(2)

−1

whereK is the inverse of key matrix
Here modulo 26 arithmetic is used as the study was
performed on the English alphabet, where each letter(Alphabet) is allotted a number generally starting from 0 in acontinuous sequence one after the other as shown in Fig. 1[3].

Fig. 1Alphabet Numbering

As shown in figure 1, Alphabets are numbered as A equals 0,
B equals 1,... , and Z equals 25, but this is not a fixed
requirement of the cipher. The encryption of plain text
takes n successive plain text letters and substitutes them forn cipher text letters. In case n = 3, the encryption can be
expressed in terms of the matrix multiplication as follows[3]
c1
k11
c
k
[ 2 ] = [ 21
c3
k 31

k12
k 21
k 32

k13 p1
k 23 ] [p2 ] mod(26)k 33 p3(3)

III. THE PROPOSED TRIPLE HILL CIPHERALGORITHM
As shown in Fig. 2 the proposed Triple Hill Cipher algorithm
consists of three stages ,where plaintext is encrypted threetimes using three different 256 bits keys.
Keying options : Option 1: k1 ,k2 and k3 are different (not equal).
 Option 2: k1 ,k2 are different and k3 equal to k1, thisoption is considered double encryption ,but it isstronger than simply hill cipher encrypting twice,e.g with k1 and k2 because it protects against meet in
the middle attack[13], Option 3: k1 ,k2 and k3 are the same, this option isconsidered the least secure one.
In our proposed algorithm we used the first option 1, thekey of the first stage is taken from random number
generator ,then 1st key is rotated one time to get the 2nd key
and two times to get the 3rd key or we can take the threekeys from different generators.

Fig. 2 The Proposed Triple Hill Cipher Algorithm

753

Random number generators (RNGs)
Is a computational or physical devices designed to generate
a random sequence of numbers or symbols. There are two
types of these generators:
 True Random Number Generators (TRNGs) which
divided into two categories physical and nonphysical
TRNGs[14]. Physical TRNGs use nondeterministic
effects of electronic circuits such as shot noise fromzener diode, inherent semiconductors thermal noise,and free running oscillators. They produce a truly
random numbers. Their outputs depend only on
physical or nonphysical process not on any initial value.
 Pseudo Random Number Generators (PRNGs) is
considered an algorithm to generate a sequence of
numbers that appear random. The sequence is not truly
random in which it is completely determined by aninitial value called a seed. There are several techniques
used to perform PRNGs such as Linear Feedback Shiftregister, Linear Congruential Generator and Blum
BlumShub[15].
In this paper we will use PRNG with the Linear
Feedback Shift register (LFSR).
Linear Feedback Shift Register (LFSR)
A LFSR is made of sequential shift-register withcombinational feedback logic connected to it which cangenerate a sequence of binary values in a pseudo-randommanner.
Feedbacks around an LFSR’s shift register are connectedto the certain points (taps) of LFSR construction andconstitute either XORing or XNORing these taps toprovide taps back into the register.
The selection of taps determines how many values canbe generated in a given sequence before the sequence isrepeated. A certain tap arrangements lead to amaximal
length sequences of (2n - 1). These settings are calculated for
different lengths of LFSRs and are represented in[16].
In our algorithm, we need a 256 bit LFSR to get pseudo
random keys to enhance the security of the proposed
algorithm. Our cipher is symmetric (sender and receivershare the same key) so when we use random keys attransmitter we need to synchronize these keys with thereceiver’s keys, we can do that by using the same seed attransmitter and receiver.Here we will explain four-bit LFSR
as example:

Fig. 3Four-bit LFSR

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

As shown in Fig. 3, four-bit LFSR consists of four D-flip
flops and xor gate in its shift path. The feedback taps are
selected from taps 3 and 4. When LFSR seed is loaded to thefour flip flops ,LFSR generates random numbers
startingfrom an initial value selected by the user.
A. Steps of the proposed algorithm
Each stage in this algorithm includes eight rounds and sub
keys generation module as shown in Fig. 4.
For the first stage ; plaintext is divided into blocks of lengths
128 bits ,each block is represented in 4 by 4 byte matrix .

754

k 7 = key(31 downto 0)&𝑘𝑒𝑦(255 downto 160) (10)The 8th sub key k8 is obtained by taking the bits from 159 to32.
k 8 = key(159 downto 32)(11)
Each of k1 ,k2 , k3 ,k4 ,k5 ,k6 ,k7 and k8is then represented in 4
by 4 byte matrices .
B. Operations in each round
Each round includes three operations as shown in Fig. 5.

Fig. 5 Single Round Diagram

Fig. 4Single Stage Diagram

Sub keys generation:
128 bits sub keys are generated from the 256 bits key as
follow :The 1st sub key k2 is obtained by taking the bits from 255 to
224 , from 191 to 160 ,from 127 to 96 and from 63 to 31.
k1 = key(255 downto 224)&𝑘𝑒𝑦(191 downto 160)&
key(127 downto 96)&𝑘𝑒𝑦(63 downto 32)(4)

Operation 1 represents matrix multiplication of plaintext (pt)and sub keys (ki), where ki represents k1 ,k2 , k3 ,k4 ,k5 ,k6 ,k7
and k8.Here we encrypt any binary data ,so we use modulo 2
field , when values in the matrices are multiplied, bitwise
AND is used and when values are added bitwise XOR isused[10][11].
At decryption process the same operation is performed but
instead we use the inverse matrices of the sub keys.
Operation 2 represents the Stir operation

The 2nd sub key k2 is obtained by taking the bits from 223 to
192 , from 159 to 128 ,from 95 to 64 and from 31 to 0 .
k 2 = key(223 downto 192)&𝑘𝑒𝑦(159 downto 128)&
key(95 downto 64)&𝑘𝑒𝑦(31 downto 0)(5)
The 3rd sub key k3 is obtained by taking the bits from 255 to
160 and from 31 to 0.
k 3 = key(255 downto 160)&𝑘𝑒𝑦(31 downto 0) (6)The 4th sub key k4 is obtained by taking the bits from 127 to
32 and from 159 to 128.
k 4 = key(127 downto 32)&𝑘𝑒𝑦(159 downto 128) (7)The 5th sub key k5 is obtained by taking the bits from 223 to96.
k 5 = key(223 downto 96)(8)
The 6th sub key k6 is obtained by taking the bits from 95 to 0
and from 255 to 224.
k 6 = key(95 downto 0)&𝑘𝑒𝑦(255 downto 224) (9)The 7th sub key k7 is obtained by taking the bits from 31 to 0
and from 255 to 160.

Fig. 6 Stir operation

As shown in Fig. 6, the stir operation is defined by thefollowing steps:
 The 1st and 2nd bits from each byte in a row of A are
combined to form the first byte of B in that row.
 The 3rd and 4th bits from each byte in a row of A are
combined to form next byte of B in that row.
 The 5th and 6th bits from each byte in a row of A are
combined to form next byte of B in that row.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016




The 7th and 8th bits from each byte in a row of A are
combined to form the last byte of B in that row.
This
stir
operationisreversible,i.e.
Stir(Stir(A))=A[9].

Operation 3 represents XOR operation
We perform XOR between sub keys ki and the output of stir
operation .it is performed as bit by bit XOR ,as example if wehave
M = 11110000
and
L = 00110011
then
XOR(M, L) = 11000011
XOR operation is reversible if N = XOR(M, L)
thenL = XOR(N, M) and M = XOR(N, L).
The three operations of the round are repeated eight times
with different sub keys for ki=1:8 to perform one stage of the
Triple Hill Cipher , so to perform the triple hill cipher werepeat the stage three times ,so we can achieve an algorithm
more robust to cryptanalysis.
Summary of The Proposed Triple Hill Cipher Algorithmi.
Encryption process
1. Read the message ( plaintext ) as a binary and divide
it into blocks of lengths 128 bits ,then is represented
in 4 by 4 byte matrices
2. 128 bits sub keys matrices (ki =1:8) are generated
from 256 bits key
3. Find inverse of the sub keys matrices
4. If the matrices are noninvertiblechange the key andgo to step 2
5.
𝑓𝑜𝑟 𝑚 = 1: 3{
𝑓𝑜𝑟 𝑖 = 1: 8{
𝑝𝑚1 = (𝑘𝑖 . 𝑝𝑡) 𝑚𝑜𝑑(2)𝑝𝑚2 = 𝑠𝑡𝑖𝑟(𝑝𝑚1 )𝑝𝑚3 = 𝑋𝑂𝑅(𝑘𝑖 , 𝑝𝑚2 )𝑝𝑡 = 𝑝𝑚3}}
𝑐 = 𝑝𝑡

Where pt is the plain text ,c is the cipher text ,m is the numberof stages and i is the number of rounds.
i.
1.
2.

755

IV. SIMULATION AND IMPLEMENTATION RESULTS
A. Simulation results
The proposed algorithm is coded using VHDL language andsimulation results taken from Modelsim6.0C as sown in thefollowing figures:

Fig. 7Timing Simulation of The 256 bit LFSR

Fig. 7 shows timing simulation of the 256 bit LFSR whichworks as a pseudo random number generator ,as shown infigure in each clock cycle different bits are generated.

Fig. 8 Timing Simulation of the sub keys generation

Fig. 8 shows Timing simulation of sub keys generation
process from the 256 bit key , as shown in figure in eachclock cycle k1 ,k2 , k3 ,k4 ,k5 ,k6 ,k7 and k8 are generated withdifferent values depending on the value of the 256 bit key
used.

Fig.9Timing simulation of one roundFig. 9 shows timing simulation of one round ,wherept is theplain text , kiis the 128 bit sub key and ct is the cipher text
from one round ,as shown we have ct with different values
and this process is repeated eight times in each stage.

Decryption process
Prepare the inverse of the sub keys matrices ki-1.

𝑓𝑜𝑟 𝑚 = 1: 3{
𝑓𝑜𝑟 𝑖 = 1: 8{
𝑐𝑚1 = 𝑋𝑂𝑅(𝑘𝑖 , 𝑐)
𝑐𝑚2 = 𝑠𝑡𝑖𝑟(𝑐𝑚1 )
𝑐𝑚3 = (𝑘𝑖−1 . 𝑐𝑚2 ) 𝑚𝑜𝑑(2)𝑐 = 𝑐𝑚3}}
𝑝𝑡 = 𝑐Fig.10 Timing Simulation of one stage

Fig. 10 shows the timing simulation of one stage from theencryption process, where ptstage is the plain text and ctstage
is the cipher text from one stage, also we note the contents ofthe cipher text changes in each clock cycle and this process isrepeated three times.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016756

as synthesis tool.The top level RTL schematic of the
proposed algorithm as shown in figures is given to establish
the fact that the HDL codes are synthesizable.

Fig. 11 Timing Simulation of the overall Proposed Triple Hill Cipher

Fig. 11 shows the timing simulation of the overall proposed
algorithm, where plaintext is the input message needed to be
encrypted, seed is the initial value of the pseudo random
number generator to get the random 256 bit keys, cipher1 isthe cipher text from the first stage,cipher2 is the cipher text
from the second stage and ciphertext is the final cipher text
which is the output of the third stage
Because of using random number generator to get keys we
get cipher text with different values in each clock cycle which
enhance encryption process and make the attacker’s task
more difficult.

Fig. 12. Top Level RTL schematic of the proposed algorithm

Fig. 12 represents the complete hardware implementation of
the proposed algorithm, as shown the data (plaintxt) is 128 bit
which is ciphered using a 256 bit key. The rst bit is used toreset the module and clk bit is used to clock the design. The
output is the 128 bit cipher text.

We used this data during the simulation process,here data isarranged as vectors of bits.
256 bits first key:
111110101110101111101011111010101010101010101010
101001100000111111111010101010101010101010101110
101010101010101010101010111010101011111111110000
011000101010101010101010101010101010101010101010
101010101111111111000000110010101110101010111010
1010101010110010
Plaintext:
111111111101010101010101010101010101010101010101
010000000000111111111101010101011010101010001100
11001101010101010101010101010101
The outputs of the three stages is
Cipher text1:
111011101010100010001000110000100101010010100010
011000101110100010101010101010001000100010000010
10101111101011011111001010110000
Cipher text 2:010101001111001001000100011000010000000111011111
001100010101010001010100010100000100010001000001
01110100111110100011000101010000
Cipher text 3 which also is the Final Cipher text of the
proposed algorithm :001010100111101100101000001100101000001101000111
100110101110101000101010001010100010100000100010
10010000011111110011001000110000
As shown from the simulation results,plain text is encrypted
three times ,which proves that our proposed algorithm
increases the difficulty in cryptanalysis .

B. Implementation results
FPGA implementation of the proposed algorithm wasaccomplishedonSpace-Grade
Virtex-4QVXQR4VSX55-10CF1140 using Xilinx ISE Design Suite 13.2

Fig. 13Top Level RTL schematic of the 256 bit LFSR

Fig. 13 shows the components (actually it is a part of thecomponents because we can’t attach all components in the
figure) of the 256 bit LFSR which acts as random number
generator to give us the random 256 bit keys.

Fig. 14Top Level RTL schematic of one RoundFig. 14 shows the components of one round which consists ofthree operations; matrix multiplication which is the first partin the figure from the left, stir operation the second part andthe xor operation the third part (also it is a part of thecomponents because we can’t attach all components in the
figure).

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757

TABLE I
Design summary of the proposed algorithm
Logic Utilization

Fig. 15Top Level RTL schematic of one Stage

Fig. 15 shows the components of one stage which consists ofeight rounds and sub keys generation module which is thesecond part in the figure from the right.

Used

Utilization

Number of Slice Flip Flop

3,072

Available
49,152

Number of 4input LUTs

9,222

49,15218%

Number of occupied slices

4,636

24,57618%

Number of bonded IOBs

514

640

80%

Number of
BUFG/BUFGCTRLs

1

32

3%

Average Fanout of Non-clock
Nets

3,77

6%

Design summary in Table Igives the number of slice flip
flops, number of 4 input LUT (Look Up Tables), number of
BUFG/BUFGCTRLS, number of bonded IOBs and average
fanout of non-clock network that used from the FPGA kit to
implement the hardware of our design.
Power analysis of the proposed algorithm design

Fig. 16 Top Level RTL schematic of The overall triple Hill cipher

Fig. 16 shows the complete hardware of the proposed
algorithm.

The total power dissipation is the sum of two types of power ; Quiescent (static) power is defined as the product of thepower supply voltage and static current, which itself has
two dual components: leakage currentand throughcurrent. Leakage currents are parasitic effects and aresmall in magnitude Through currents occur in normal
operation and are due to transistors being continuously
operated in their saturation region.
 Dynamic power which is frequency dependent and it has
two components: the “capacitive” load power and thecell power. The latter is consumed internally bythe cellprimitives. This component accounts for the power thatis required to mainly charge and discharge the internalcell capacitance. “Capacitive” load power represents thecurrents required to charge the external loads driven byeach cell. The overall dynamic power for an entire chipis given by [17]Fig. 17Top Level RTL schematic of The overall triple Hill cipher cont.

Fig. 17 also shows the complete hardware of the proposed
algorithm but with more details.
Design summary for the proposed algorithm is specified
in Table I to set up details .

Fig. 18 Power analysis summary of the design with Clock frequency 25
MHZ

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

758

Fig.19Power analysis summary of the design with Clock frequency 50 MHZ
Fig. 21Power analysis summary of the design with Clock frequency 528.067
MHZ

V. CONCLUSION

Fig. 20Power analysis summary of the design with Clock frequency
100MHZ

Figs 18, 19 and 20 specifies the power analysis summary ofthe design at frequencies 25, 50 and 100 MHZ. As shown infigure the quiescent power is approximately constant at
different frequencies, but the dynamic power changes withthe frequency it increases as the frequency increases.
It is clear from these figures that the dynamic power increases
as the frequency increases, so we need to know the maximum
allowed frequency of this design. Here is the timing summaryof that design, that from it we can know the maximum
frequency:
 Minimum input arrival time before clock: 4.263ns
 Maximum output required time after clock: 4.677ns
 Minimum period: 1.894ns (Maximum Frequency:
528.067MHz)Power analysis summary of the design at the maximum
allowed clock frequency will be shown in Fig. 21

In the cryptanalysis of the classical hill cipher ,the known
plaintext attack is performed using the method k = p−1 cwhere p is an invertible matrix , so if an attacker has mdistinct plaintext and cipher text ,can retrieve the key using
the previous method .In the proposed triple Hill Cipher
algorithm the known plain text attack becomes more difficult
as the plain text is encrypted in eight rounds with eight
different keys three times ,we can say that , we used 24
different keys which makes the task of the known plain text
attack more difficult, there is no doubt that increasing thenumber of stages for example four stages instead of threeincreases the level of security but the overall performance of
the algorithm become more slower. Also using LFSR as arandom number generator to get random keys enhances the
security of the proposed algorithm as the keys change moretimes.
REFERENCES[1]A.F.A. Abidin, O.Y. Chuan and M.R.K. Ariffin‘‘A Novel Enhancement
Technique of the Hill Cipher for Effective Cryptographic Purposes” Journalof Computer Science 7 (5): 785-789, 2011.
[2] William Stallings ,‘‘Cryptography and Network Security Principles
and Practices”, Fourth Edition, Prentice Hall, November 16, 2005.
[3] Jasdeep Singh Bhalla,‘‘ A Database Encryption Technique to Enhance
Security Using Hill Cipher Algorithm”, International Journal ofEngineering and Advanced Technology (IJEAT), Vol. 2, No. 4, April 2013.
[4] M. Nordin A. Rahman, A. F. A. Abidin, MohdKamirYusof, N. S. M.
Usop,‘‘ Cryptography: A New Approach of Classical Hill Cipher”,
International Journal of Security and Its Applications,Vol. 7, No. 2, March,
2013.
[5]D.R. Stinson, “Cryptography Theory and Practice”,Third Edition,
Chapman and Hall/CRC, Pp.13-37, 2006.
[6] V. U. K. Sastry, D. S. R. Murthy, S. DurgaBhavani, “A Block
Cipher Involving a Key Applied on Both the Sides of the Plain Text,”
International Journal of Computer and Network Security (IJCNS), Vol. 1,
No. 1, Pp. 27 -30, Oct. 2009.
[7] V. U. K. Sastry, V. Janaki, “A Modified Hill Cipher with Multiple
Keys”, International Journal of Computational Science, Vol. 2, No. 6,
815-826, Dec. 2008.
[8] Bhibhudendra Acharya, GirijaSankarRath, and Sarat Kumar Patra,“Novel Modified Hill Cipher Algorithm,”,Proceedings of ICTAETS, Pp.
126-130, 2008.
[9]GandharbaSwain,andSaroj Kumar Lenka,‘‘A Technique for Secret
Communication Using a New Block Cipher with Dynamic Steganography’’,
International Journal of Security and Its Applications,Vol. 6, No. 2, April,
2012.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016759

[10] Ahmed Desoky, AnjuPanickerMadhusoodhanan,‘‘Bitwise Hill Crypto
System’’,
DOI: 10.1109/ISSPIT.2011.6151539[11] Ali Muhammad Ali Rushdi and Fares Ahmad Muhammad Ghaleb,‘‘On
Self-Inverse Binary Matrices Over the Binary Galois Field’’,Journal of
Mathematics and Statistics 9 (3): 238-248, 2013.
[12] D. Coppersmith, D. B. Johnson and S. M. Matyas, ‘‘A proposed mode
for triple-DES encryption”,IBM J. RES, DEVELOP. VOL. 40 NO, 2
MARCH 1996.
[13] Ralph C. MerkleElxsi, ‘‘On the Security of Multiple
Encryption”,Technical Note Programming Techniques and Data Structures
D. Mcllroy Editor, Int. Martin E. Hellman Stanford University, Pp. 465-467,
Volume 24 the ACM Number 7, July,1981.
[14]C¸ .K. Koc¸ (ed.) “ Cryptographic Engineering”, DOI
10.1007/978-0-387-71817-0 3, c Springer Science+Business Media, LLC
2009[15] Jay Kumar, Sudhanshu Shukla, Dhiraj Prakash, Pratyush Mishra andSudhir Kumar,‘‘ Random Number Generator Using Various Techniquesthrough VHDL’’,International Journal of Computer Applications inEngineering Sciences ,VOL I, ISSUE II, JUNE 2011 , ISSN: 2231-4946.
[16] Douglas, J.S., 1997, “HDL Chip Design” 3rd, Doone Publications,
Madison, AL, USA, ISBN 0-9651934-3-8, , pp. 179-187.
[17] HichemBelhadj, BehroozZahiri, Albert Tai and Actel
Corporation,‘‘Power-sensitive designtechniques on FPGA devices’’,International
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Taipei
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1JUL03_PL_POW_TAC.PDF.

applications incommunications.

Ashraf A. M. Khalaf (M’98) received his B.Sc. andM.sc. degrees in electrical engineering from Minia
university, Egypt, in 1989 and 1994 respectively. He
received his Ph.D in electrical engineering from
Graduate School of Natural Science and Technology,
Kanazawa university, Japan, in Marsh, 2000. He iscurrently works as an associate professor atelectronics and communications engineering
Department, Minia University, Egypt.. His researchinterest includes digital signal processing and its
communications, neural networks, and opticalMona S. Abd El-karimwas Born :1-8-1989, she worka as a teaching assistant and iscurrently a master course student for M.Sc. degree inElectrical Engineering (Communication andElectronics), Faculty of Engineering, Minia
University, El-Minia, Egypt.

Hesham F.A. Hamed received the B.Sc.
degree in Electrical Engineering, the M.Sc. and
Ph.D. degrees in Electronics and Communications
Engineering from EL-Minia University, ELMinia,
Egypt, in 1989, 1993, and 1997 respectively. He
currently is Professor and a Dean of the faculty ofEngineering EL-Minia University. From 1989 to
1993 he worked as a Teacher Assistant in theElectrical Engineering Department, ELMinia University. From 1993 to
1995, he was a visiting scholar at Cairo University, Cairo, Egypt. From
1995to 1997, he was a visiting scholar at Texas A&M University, College
Station, Texas (with the group of VLSI). From 1997 to 2003, he was
anAssistant Professor in the Electrical Engineering Department, EL-Minia
University. From 2003 to 2005, he was Associate Professor in thesameUniversity. From 2005 to 2007, he was a Visiting Researcher at Ohio
University, Athens, Ohio. He has published more than 65 papers andonebook chapter. His research interests include analog and mixed-mode
circuit design, low voltage low power analog circuits, current mode
circuits,nano- scale analog and digital integrated circuits

design, and FPGA.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

760

Fast Intra-Beam Switching Scheme using
Common Contention Channels in
Millimeter-wave based Cellular Systems
Nak Woon Sung*, Yong Seouk Choi**Communications Internet Research Laboratory, ETRI,
218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700, Korea
nwsung@etri.re.kr, choiys@etri.re.kr
Abstract—Millimeter wave (mmWave) cellular system hasrecently been introduced as an attractive approach for 5G
mobile broadband communications. In the mmWave
beamforming cellular systems, the user equipment (UE) can
experience frequent service disruptions due to frequentswitching among a plurality of beams if the UE follows the
network controlled LTE handover procedures. In this paper, weshows how the mmWave beamforming cellular system canoperate and which kind of new handovers UEs can experience.
And we propose UE controlled beam switching mechanism
based on contention based uplink channels. In this mechanism,
UEs switch the serving beam to the target beam without random
access delay using the pre-acquired contention based channels.
Keyword—Beam switching, millimeter wave communication,
millimeter wave cellular systems

I. INTRODUCTION

R

ecently, as the growing number of smart phones andtablet PCs drives the increase of applications requiringmuch more traffic, the mobile data usage and traffic is more
accelerated. According to [1], the high-definition video,
sophisticated augmented reality and new types of games will
be becoming major applications in the next decade which
require 1000x bandwidth. However it is very difficult tosatisfy the demand for bandwidth hunger applications with
spectral efficiency enhancement because it has reached nearpractical Shannon limits. In addition, acquiring more
spectrum below 6GHz cannot provide the capacities required
because there isn‘t enough spectrum there. To meet 1000x
requirements, approaches different from the existing methods
have to be considered [2].
An attractive approach is to use millimeter wave———————————————————————
Manuscript received July 6, 2015. This research was funded by ‘The
Cross-Ministry Giga KOREA Project ’ [GK15N0100, 5G mobile
communication system development based on mmWave] of the Ministry of
Science, ICT and Future Planning, Korea.
Nak Woon Sung is with the Electronics and Telecommunications
Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700, Korea(corresponding author phone: +82-42-860-4890; fax: +82-42-861-1966;
e-mail: nwsung@ etri.re.kr).
Yong Seouk Choi is with the Electronics and Telecommunications
Research Institute, Daejeon, 305-700, Korea (e-mail: choiys@ etri.re.kr).

(mmWave) spectrum for 5G mobile communication systems[3]. mmWave has experienced higher propagation losses suchas path losses or return losses than bands below 6GHz. This
characteristic reduces both of cell coverage and interference.
However minimized interference enables dense deploymentsof small cells. Therefore, the total capacity can be scaled with
the number of base stations (BSs).
Path losses at mmWave bands, meanwhile, can beovercome in use of massive MIMO (Multiple-input andmultiple-output) technology in which antenna arrays consistof many active antenna elements. Nevertheless, thepropagation losses have a negative effect on service coverageof the mmWave wireless communication systems. In thiscase, beamforming technology can be utilized to increase
service coverage, expand cell capacity and minimize
interference [4], [5]. Beamforming is an advanced technology
in which a transmitting side transmits a plurality of beams in
different directions and a receiving side receives a plurality ofbeams in different directions.
In the mmWave cellular systems using beamforming
technology, a BS can forms a plurality of beams towards thedesired users (UE). In this beamforming architecture, a new
type of handover - beam switching within the same BS oramong different BSs - can occur.
While multiple narrow beams increase network capacity,
they also cause frequent beam switching even if the UE moves
around within the same BS. If the UE follows theconventional network controlled/UE assisted handoverprocedures in Long Term Evolution (LTE) defined by theThird Generation Partnership Program (3GPP) [6], the UE
experiences frequent service disruptions. In this paper, we
propose simple UE controlled beam switching mechanism
using contention based uplink channels.
The rest of this paper is organized as follows. Section II
describes the architecture of the proposed mmWave
beamforming cellular systems. Section III discusses thecontention based intra beam switching mechanisms. Finally,
we conclude our work in Section IV.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

Fig. 1. mmWave based beamforming cellular systems.

II. MMWAVE BEAMFORMIG CELLULAR SYSTEMS
A. mmWave beamforming cellular system architecture
To meet the 1000-fold bandwidth requirements, themmWave cellular system is one of the promising solutions [2].
First, the wide bandwidths for carrying bandwidth hunger
applications are available at mmWave spectrum above 6GHz.
Second, a number of picocell BSs with a plurality of
directional beams can be densely deployed in the small areas
due to the minimized mutual interference at the highfrequency spectrum. Therefore, total capacity increases
proportionally with the number of the BSs. Consequently,
more available bandwidth at mmWave and capacity
scalability deliver per-user data rate for supporting high speed
data and high definition video transmission. The Giga Korea
Project is a representative example to develop the mmWave
based mobile communications systems to deliver Gbps
broadband connection by 2020.
Fig. 1 depicts our proposed mmWave beamforming
cellular systems which provide a kind of outdoor picocells
and can be installed on existing street poles like lamp posts. In
this architecture mmWave cells are overlayed in macro
cellular networks or run as a stand-alone. We consider both ofdual connectivity and stand-alone operation possible for
future 5G networks. Thus, the UE supports different
operation modes like 4G and 5G radio access and different
coverage layers such as macro cells or mmWave picocells. In
addition, a single common 5G core network is designed tosupport 4G and WiFi access in use of the network
virtualization based on the distributed architecture
approaches. These proposed unified systems enables avarious types of connectivity, services, and use cases
integrated.
In addition to achieving high per-user data rates, lowlatency is another key target for the proposed systems sincelatency minimization provides new business opportunities tothe industry and performance improvement which theadvanced networks require for fast coordination andtransmission among different network entities [7]. Latency isdetermined by one round trip transmission to deliver thecontrol signaling or data transmission. And low latency is761

Fig. 2. The protocol architecture of mmWave based beamforming BS system.

achieved by the shorter transmission time interval (TTI)
length. With the short TTI length data blocks has to betransmitted for a short time but in wide frequency range.
The TTI length of 200us is set for the proposed system. In
case of LTE the total user plane (U-plane) delay is 4.8ms
under the assumption of 10% Hybrid Automatic
Repeat-reQuest (HARQ) Block Error Rate (BLER) [8]. Using
the same LTE latency calculation model, the U-plane
one-way access latency for the proposed system is given as
2.46ms. This one-way access latency is calculated as

T USER PLANE  2 .1P *3 .6

where p is the error probability of the

first HARQ retransmission and UE Processing Time of 1ms
and eNB Processing Time of 1.1ms are considered. The
minimum U-plane latency is expected to be 2.1ms when pequals zero. However, the U-plane latency of 2.46ms is more
realistic when considering radio frame alignment.
B. Beamforming base station architecture
An mmWave BS is designed to consist of three sectors
which form forty eight narrow beams. Each beam uses thesame 1 GHz bandwidth in 28GHz band and support
theoretical speeds up to about 3.3Gbps. In our systems, theuplink transmission timing of all the beams within a BS is also
assumed to be synchronized as the cell coverage is relatively
small due to properties of the ultra-high frequency. And eachbeam works like a kind of the small cell BS which has its own
physical channels, transport channels, and logical channels.
While each beam within the same BS shares the same cell
identity (Cell ID) which is unique within the network, it has
the different beam identity (Beam ID) which is unique within
only the BS. There is only the single radio resource control(RRC) [9] entity in a BS and therefore the same RRC
signaling messages can be delivered to UEs through broadcast
channels or shared channels per beam within the same BS. For
example, although UEs associates with different beams, each
UE receives the same Master Information Block (MIB)
broadcast on Physical Broadcast CHannel (PBCH) per beamand the same System Information Blocks (SIBs) sent on the
Physical Downlink Shared Channel (PDSCH) through thesame RRC messages.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

We show the proposed beamforming mmWave cellular
system architecture in Fig. 2. The logical channels from radio
link control (RLC) entity are mapped to the most proper beamincluding beam-specific medium access control (MAC)
entities through the beam mapper which is newly introducedin the proposed mmWave beamformig cellular systems. Beam
mapping has considerable effect on network throughput anddepends on beam switching policy; network controlled UE
assisted beam switching or UE-controlled beam switching.
 In case of network controlled UE assisted beamswitching, beam switching is decided and initiated by
the BS based on the measurement feedback
information which the network asks the UE tomeasure the signal of the surrounding beams andreport.


In case of UE-controlled beam switching, the UE
estimates the signal quality from each beam from theserving BS and the neighboring BSs and performs anappropriate beam switching procedures for selecting
its target beam.

In our paper, we are just concerned about theUE-controlled beam switching which promises greatly
increased scalability for the mmWave cellular systems.
Within each single beam, the unicast scheduler in MAC
entity is responsible for scheduling the beam‘s radio resources
utilized in the uplink and downlink whilst satisfying therequired quality of service (QoS) for all active radio bearers
within the beam.

III. CONTENTION BASED BEAM SWITCHING MECHANISM
A. Introduction of beam switching
In our proposed mmWave beamforming cellular systems,
the initial camp on procedure is similar with the existing LTE
camp on procedure except that the UE searches for both ofsuitable beams and cells and camps on the selected beaminstead of the cell. The reason is that each beam acts like thesingle small cell BS in which its own physical channels aredefined as shown in Fig. 2. The UE then performs the attach
procedure in order to register in the tracking area of theselected cell. However, the location registration process takes
place on cell level, not on beam level. The UE now
continuously monitors and searches for the best beam/cell on
which to camp.
In LTE, there are three kinds of handovers relying onwhether the evolved packet core (EPC) entities that a UE isconnected to are switched after the handover or not:intra-LTE, inter-LTE, and inter-RAT.
 Intra-LTE: An intra-LTE handover will occur withincurrent LTE nodes either via the X2 or via the S1(intra-MME and intra-SGW)Inter-LTE: Inter-LTE handover will take place
toward the other LTE nodes that belong to thedifferent pooling area (inter-MME and inter-SGW)Inter-RAT: Handover between different radio
technology networks, for example E-UTRAN andGERAN/UIRAN

As mentioned in Section II, new types of handovers are762

Fig. 3. The types of beam switching in beamforming cellular systems

required in mmWave beamforming cellular systems because a
UE has the connection with a BS through the channels in the
beams, not the channels in the cell. In other words, each cellconsists of the multiple beams and there are separate physicalchannels per beam which transport control messages and user
beam (serving beam) to the other beam (target beam) within
the same serving BS or the BS different from the serving BS
shown in Fig. 3. Two types of beam switching can occur: Intra beam switching: When a beam switching
happens, the target beam is selected among the beams
transmitted by the same BS which the serving beam
belongs to.
Inter beam switching: When a beam switching
happens, the target beam is selected among the beams
transmitted by the target BS different from theserving BS which the serving beam belongs to.

For instance, in Fig. 3, UE#1 and UE#3/UE#4 change theserving beams within Base station 1 and Base station 2,
respectively. This is the example of intra beam switching. In
contrast, UE#2 moves toward the target beam which belongs
to the neighboring BS. In this case, inter beam switching isrequired. The inter beam switching is very similar with a kindof general LTE handover procedure above mentioned. In thispaper, we are concerned about intra beam switching
instances.
B. Problems in intra-beam switching
Our proposed mmWave beamforming cellular approachprovides network throughput advantages up to over 100Gbps
and the solution to the wireless spectrum shortage. However,this approach also has a few technical limitations to be
overcome. Newly introduced interference is proportional to
the number of the overlapped beams which share and reuse
the same frequency band within a serving BS and neighboring
BSs. Another problem is more frequent switching between
beams because the cell coverage of mmWave beamforming
cellular systems is relatively small due to the properties ofhigh frequency band and beam area per beam is a little narrowin comparison with the LTE cell. When a UE moves around,the UE may experience more frequent switching between
neighboring beams although the UE stays in the same BS.
In LTE handover, MME always instructs the UE to change
the cell, i.e. network controlled handover. Therefore, theCopyright ⓒ 2016 GiRI (Global IT Research Institute)existing LTE handover procedure aims to get minimum
latency in case of cell changes [10]. While this type ofhandover has the advantage of better traffic load balancing[11], it is not suitable for a rapidly changing environment andhigh user density areas due to the associated signaling delay[12]. In the proposed mmWave beamforming cellular
systems, users experience rapidly changing beam
environment because of multiple narrow beams and thelimited propagation distances of mmWave beams. This
environment at mmWave frequencies makes UE controlled
handover schemes more attractive. This type of handover has
the advantage of a short reaction time and is suitable for small
cell systems [13].
Another beam switching problem is related to the handoverprocedures. In LTE handover a UE sends a measurement
report according to the measurement configuration specified
by RRC protocol entity in the serving BS. And the serving BS
decides handover initiation depending on this measurement
report. In accordance with handover decision algorithm,
handover preparation and handover execution phases are
performed. After the UE sends a handover completion
message to the BS, the handover procedure is migrated into
handover completion phase. In response to this completion
message, the target BS notifies the serving BS to release all
the resources utilized by the UE and the target MME to switch
the path of the packet to the target BS. This mobility
management is conducted in RRC entity in Layer 3.
Intra beam switching procedure has characteristics
different from above the LTE handover procedure because
any switching of the current LTE nodes is not required, i.e. theUE keeps the connection with the same serving BS.
Therefore, handover preparation phase is not required andhandover completion phase can be simplified to notify theserving BS the completion of intra beam switching. Only thebeam switching decision and execution processes need to be
carried out in handover decision and execution phase as in
LTE handover procedure. In particular, the handover
execution phase needs to be swiftly completed within thedomain of single BS. Thus, efficient fast intra beam switching
schemes are required.
C. Contention based beam switching scheme
During the handover execution phase, the UE is asked toconnect to the selected target networks. In this phase, the LTE
UE generally performs random access procedures for uplinktransmission timing synchronization and UL allocation for thehandover completion message [14]. This means that theincreasing distance between the UE and the BS requiresconsideration of propagation delay. However, the problem isthat it takes about 10 Transmission Time Interval (TTI) tocomplete these procedures as shown in [15], [16]. In the
result, these procedures cause considerable service
disruptions in the rapidly changing beam environments.
In the proposed mmWave beamforming cellular systems,
the beam coverage is relatively small, i.e. under about 500 m
and each BS can synchronize the UE uplink transmission
timing between its own beams. Thus, intra beam switching
can be done without UL timing alignment procedures. To
enable the efficient and low latency beam switching, the UE

Uplink Bandwidth

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

PUB
SCC
H

PUB
SCC
H

PUB
SCC
H

PUB
SCC
H

1 frame = 10 subframe

PUB
SCC
H

763

PUB
SCC
H

PUB
SCC
H

PUB
SCC
H

subframe

Fig. 4. Common contention based channels among all uplink beams, i.e.
named Physical Uplink Beam Switching Control Channels (PUBSCCH).

sends Beam Switching Request MAC message in use ofcontention based access schemes.
In [17], the authors show that in LTE networks, unlimited
use of contention based access is clearly useless since thecollision probability over contention based channels increases
rapidly. But limiting contention based transmissions oncertain frames, for example voice frames, improve thesatisfaction rate and the network throughput. In addition, in[18] authors proposed contention based uplink transmissionin order to allow uplink synchronized UE to transmit uplink
data without sending Scheduling Request message inadvance. Thus, considering the relatively rich wirelessresources at the high frequency bands and the smaller numberof users per beam in mmWave cellular systems, contention
based access schemes can be utilized for intra beamswitching. As shown in Fig. 6, the limited number of uplink
Resource Blocks (RBs) are dynamically scheduled on a persome subframe basis for beam switching. And the use of theseRBs is constrained only for sending the uplink beamswitching control element which is under a few bytes.
In our contention based beam switching scheme, somespecific radio regions are defined as common contention
based channels among all uplink beams, i.e. named Physical
Uplink Beam Switching Control Channels (PUBSCCH), as
shown in Fig. 4. This means that intra beam switching, i.e.
contention based transmissions, does not interfere with other
uplink transmissions for user data, i.e. contention free (CF)transmission. And the configuration for these common
channels are broadcast by using system information block 2(SIB 2) which is shared among the beams within single BS. In
addition, a group of contention based radio network
temporary identifiers (CB-RNTIs) is also defined in order to
identify the UE within the single BS domain and a CB-RNTI
is added into Beam Switching Request MAC PDU
transmitted over contention based channels. Therefore, a UE
can send the Beam Switching Request MAC CE through theserving beam without acquisition of PDCCH as in [17] since
the UE can already know the CB grants by using the SIB 2
pre-acquired over the previous or current serving beam.
D. Fast intra-beam switching scheme
In this paper, we propose UE controlled contention based
beam switching schemes done in MAC entity in Layer 2 asshown in Fig. 5. In our scheme, a UE measures RSRP
(Reference Signal Received Power) parameter on
beam-specific reference signal for each beam after themeasurement configuration is provided to the UE in
RRC-CONNECTED mode by the BS.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016764

Fig. 5. The fast intra-beam switching: Communication success procedures.

Fig. 6. The fast intra-beam switching: Communication failure procedures.

And the measurement results are managed by the RRC
entity in the UE instead of the serving BS. Considering therank of RSRP from the entire candidate beams along withserving beam, beam switching decision is made by the UE if
the RSRP of any candidate beams within the serving cellmeets the pre-deﬁned threshold.
In the following handover execution phase, connectionneeds to be re-routed from the serving beam to the selected
target beam by sending Beam Switching Request message tothe serving BS. Using the contention based uplink channels as
described in Section II-C, the UE transmits this control
message through PUBSCCH without monitoring PDCCH.
This is possible since the UE already acquires UL grants
through the PUBSCCH configuration in the SIB. Then the UE
starts the Beam Switching timer. This timer is stopped only
when the Beam Switching ACK MAC CE is received through
PDSCH. Whenever this timer reaches its time-out values, theUE retransmits the Beam Switching Request message. Due toa collision in PUBSCCH, the UE may fail in successfullyreceiving the Beam Switching ACK MAC CE within thespecified time interval. The collided UE starts above theprocess again based on the uniform backoff algorithm in LTE[19]. However, the backoff indicator parameters which define
the upper limit for a random backoff period is controlled by
the UE, not by the BS.
After receiving Beam Switching Request MAC CE, theMAC entity on the BS site corresponding to the serving beam
notifies the beam mapper and the RRC entity about the beamswitching request. The beam mapper commands the UE toswitch the serving beam to the target beam by sending Beam
Switching ACK MAC CE through PDSCH in the serving
beam using the same CB-RNTI in Beam Switching Request
MAC CE. After the MAC entity in the UE sends Beam
Switching Indication message to the RRC entity for beamswitching completion, the UE and the beam mapper changes
the serving beam to the target beam in the following subframe.
Then, the UE can receive the packets through the target beamusing the same C-RNTI used in the serving beam.
If the UE fails in intra-beam switching despite of a numberof random backoffs, the UE transmits the scheduling request(SR) message through random access as shown in Fig. 6. The

random access is necessary for applying for uplink resource
grants for sending the Beam Switching Request message. It
will take 2.8ms to complete this procedures in the best.
Although this channel access latency is faster than that of LTE
random access which provides about 14ms, it has significant
impact on capacity decrease in rapidly changing beam
environments.
IV. CONCLUSIONSIn the mmWave beamforming cellular systems, a pluralityof beams causes frequent beam switching even if the UE
moves around within the same base station. If the UE follows
the network controlled LTE handover procedures, the UE
experiences frequent service disruptions. In this paper, weshows how the mmWave beamforming cellular system canoperate and which kind of new handovers UEs canexperience. And we propose UE controlled beam switchingmechanism based on contention based uplink channels. In thismechanism, UEs switch the serving beam to the target beam
without random access delay using the pre-acquired
contention based channels.
ACKNOWLEDGMENT
This research was funded by ‘The Cross-Ministry Giga
KOREA Project’ [GK15N0100, 5G mobile communication
system development based on mmWave] of the Ministry of
Science, ICT and Future Planning, Korea.
REFERENCES
[1]

[2]
[3]
[4]

Nokia Solutions and Networks White Paper – ‗Enhance mobile
networks to deliver 1000 times more capacity by 2020‘
http://networks.nokia.com/sites/default/files/document/technology_vi
sion_2020_1000x_capacity_white_paper_0.pdf.
Y. Zhu, Z. Zhang, Z. Marzi, C. Nelson et al., ―Demystifying 60GHz
Outdoor Picocells,‖ in Proc. ACM MobiCom, 2014.
T. Rappaport et al., ―Millimeter Wave Mobile Communications for 5G
Cellular: It Will Work!,‖ IEEE Access, vol. 1, 2013, pp. 335–49.
W. Roh, J.-Y. Seol, J. Park, B. Lee, J. Lee, Y. Kim, J. Cho, K. Cheun,and F. Aryanfar, ―Millimeter-wave beamforming as an enabling
technology for 5G cellular communications: theoretical feasibility andprototype results,” Communications Magazine, IEEE, vol. 52, no. 2,
pp. 106– 113, 2014.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

765

Meejoung Kim, Yongsun Kim, and Wooyong Lee, ―Resource
Allocation Scheme for Millimeter Wave-Based WPANs Using
Directional Antennas,‖ ETRI Journal vol. 36, no. 3, pp. 385-395, June
2014.
[6] 3GPP TS36.300, ―E-UTRA and E-UTRAN; Overall description; Stage
2 (Release 11),‖ v.11.1.4, Dec. 2012.
[7] Nokia Solutions and Networks White Paper – ‗Technology Vision
2020:
Reducing
network
latencytomilliseconds‘
http://networks.nokia.com/sites/default/files/document/technology_vi
sion_2020_reduce_latency_white_paper_1.pdf.
[8] 3GPP TR 36.912, ―Feasibility Study for Further Advancements of
E-UTRA (LTEAdvanced)‖[9] 3GPP TS36.331, ―E-UTRA Radio Resource Control (RRC); Protocol
specification (Release 11)‖, v11.2.0, Dec. 2012.
[10] Y. S. Hussein, B. M. Ali, M. F. A. Rasid and A. Sali, ―Reduction of
Outage Probability due to Handover by Mitigating Inter-cell
Interference in Long-Term Evolution Networks,‖ ETRI Journal, vol. 36,
no. 4, p. 554, 2014[11] SNK Marwat, S. Meyer, T. Weerawardane, and C. Goerg, "Congestion
Aware Handover in LTE System for Load Balancing in Transport
Network,"
ETRI Journal,
July 2014(Early Access).
http://dx.doi.org/10.4218/etrij.14.0113.1034[12] J. Li, X. Wu, and R. Laroia, OFDMA Mobile Broadband
Communications:A Systems Approach. Cambridge U.K.: Cambridge
Univ Press, 2013.
[13] N. D. Tripathi, J. H. Reed, and H. F. VanLandingham, ―Handoff in
Cellular Systems,‖ IEEE Pers. Commun., Dec. 1998, pp. 26–37.
[14] M. Amirijoo, F. Gunnarsson, F. Andren, ―3GPP LTE Random Access
Channel,‖ IEEE Transactions on Vehicular Technology, vol. 63, no. 6,
pp. 2784–2793, 2014.
[15] D. Singhal, M. Kunapareddy, and V. Chetlapalli, ―LTE-Advanced:
Latency Analysis for IMT-A Evaluation,‖ Tech Mahindra, Tech. Rep.,
2010.
[16] Singhal, D., Kunapareddy, M., Chetlapalli, V., James, V.B., Akhtar,
N., "LTE-Advanced: Handover interruption time analysis for IMT-A
Evaluation," Signal Processing, Communication, Computing and
Networking Technologies (ICSCCN), 2011 International Conferenceon, vol., no., pp.81-85, 21-22 July 2011[17] L.K.S. Sunna, ‖Contention-based Uplink Transmission in
Unscheduled Access‖, M.SC. Thesis, Dept. Computer Science andElec. Eng., Lulea University of Technology, Jan. 2010.
[18] 3GPP ―Contention based uplink transmissions‖, R2-093812, June,
2009[19] J.-B. Seo and V. C. M. Leung, ―Design and analysis of backoff
algorithms for random access channels in UMTS-LTE and IEEE
802.16 systems,‖ IEEE Transactions on Vehicular Technology, vol.
60, no. 8, pp. 3975–3989, 2011.
[5]

Nak Woon Sung received the Ph.D Degree in
Computer Science from KAIST, Korea, in 2013. Hereceived his B.S. and M.S. degree in Computer
Engineering from Pusan National University, Korea
in 1997 and 1999. During 1999-2000, he was amember of researchers at ADD (Agency for Defense
Development). Since 2000, he has been currently theprincipal researcher at ETRI (Electronics andTelecommunications Research Institute) in Korea.
His research interests include the medium access control (MAC) of wirelesscommunication including the broadband wireless access (BWA) andmillimeter wave communication systems.
Yong Seouk Choi received his Ph.D degree from in
the Information and Communication System from
Chungbuk National University, Korea in 2007. Hereceived his bachelor‘s degree and M.S. degree inElectrical Engineering from Hongik University,
Korea, in 1990 and 1992. During 1992-1998, he
was a member of researchers at Hanhwa
Information and Communication Research Lab.
Since 1992, he has been the principal researcher at
ETRI (Electronics and Telecommunications Research Institute) in Korea.
His research interests are the wireless access technology and millimeter wave
communications system.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016766

Improving Beam Distribution Evenness in
3-Dimensional Beamforming with Carrier
Aggregation
Jun-woo Kim, Gosan Noh, Jang-won Moon, Youn-ok Park, Ilgyu Kim
Communications & Internet Research Laboratory, ETRI (Electronics and Telecommunications Research Institute),
Daejeon 34129, Korea
jwkim74@etri.re.kr, gsnoh@etri.re.kr, jwmoon@etri.re.kr, parkyo@etri.re.kr, igkim@etri.re.kr
Abstract— The 3-dimensional beamforming is a highly
attractive issue in 5G telecommunication. Equipped with
2-dimensional antenna arrays, it allows vertical sectorization
within a cell as well as horizontal one, by making a beamformingzone for the corresponding sector. However, there isconsiderable inequality among the areas of beamforming zone.
Since the farther from the base station, the bigger the
beamforming zone area is, the farther beamforming zone area islikely support more users than nearer beamforming zone.
In this paper, we propose to utilize carrier aggregation (CA)from additional base stations for relieving the uneven
beamforming zone area problem and prove this method is more
efficient in improving cell throughput especially in mmWave
environment. Even if the additional base station is more simple
type which offers only a few beamformings, it can effectively
improve the equality of UE’s radio resource occupation.

In 3-dimensioanl beamforming, each beam is formed andcontrolled by antenna arrays. Then each cell can be split into
multitude of beamforming zone as shown in Fig. 1.

Keyword—3-Dimensional Beamforming, Carrier Aggregation,
5G Telecommunication

I. INTRODUCTION

T

HE 3-DIMENSIONAL beamforming allows bothhorizontal and vertical beam pattern adaption in order toenhance system performance over the conventional
beamforming techniques [1][2]. Lots of recent researches in
5G communication also consider adopting 3-dimensional
beamforming for mmWave systems [4][5].

———————————————————————
Manuscript received October 9, 2015. This work is a follow up of theinvited journal of the accepted conference paper for the 17th International
Conference on Advanced Communication Technology. This research was
supported by ’The Cross-Ministry Giga KOREA Project’ of The Ministry of
Science, ICT and Future Planning, Korea. [GK15N0100, 5G mobile
communication system development based on mmWave]
Jun-woo Kim is with the Communications & Internet ResearchLaboratory, Electronics and Telecommunications Research Institute (ETRI),
Daejeon 305-700, Korea (Corresponding Author phone: +82-42-860-6682;
fax: +82-42-861-1966; e-mail: jwkim74@etri.re.kr).
Gosan Noh is with Electronics and Telecommunications ResearchInstitute (ETRI), Korea (e-mail: gsnoh@etri.re.kr).
Jang-won Moon is with Electronics and Telecommunications ResearchInstitute (ETRI), Korea (e-mail: jwmoon@etri.re.kr).
Youn-ok Park is with Electronics and Telecommunications ResearchInstitute (ETRI), Korea (e-mail: parkyo@etri.re.kr).
Ilgyu Kim is with Electronics and Telecommunications ResearchInstitute (ETRI), Korea (e-mail: igkim@etri.re.kr).

Fig. 1. Multiple beamforming zones using 3-dimensioanl beamforming.

One of the expected problems of 3-dimensional
beamforming is its inequality of each beamforming zone area
[3][4]. Since the farther from the base station, the bigger the
beamforming zone area is, the farther beamforming zone area
is likely support more users than nearer beamforming zone.
Even though the considered cell sizes are small due to the
propagation limitation of mmWave, most part of the cell area
was covered by only a small portion of 3-dimensional
beamformings [4]. Generating sharper beamforming forfarther area may be a solution [10]. But even if downlink
beamforming inequality problem is solved by this method,since UE cannot make as sharp uplink beamforming as that ofdownlink, the uplink beamforming still remains as a problem
to solve [12].
This inequality of beamforming zone area causes inequalityof users each beamforming supports, i.e. uneven averageradio resource occupation of each user equipment (UE) since
many UEs in wide area are likely supported from a small
number of beamformings in far region from the BS.
Prior researches considered relay systems to be adopted inthis situation. Relays are helpful in filling coverage holes but
they also can interfere neighbor beamforming zone area [4].
In this paper, we will show this inequality of radio resource

Copyright ⓒ 2016 GiRI (Global IT Research Institute)occupation can be alleviated by installing other BSs for
carrier aggregation (CA) [6] at adequate places.
The additional BS for CA can be the same type of the main
BS or simpler type with a few steering beamforming. Whilethe high-capacity BS for carrier aggregation can greatly
improve the unequal radio resource occupation problem, thesimpler BS with steering beamforming can moderately
improve this problem with less expensive installations.
The CA technology is frequently adopted to multiply thecell capacity. But this paper shows it can be used to alleviate
resource occupation inequality also.

II. THE INEQUALITY OF 3-DIMENSIOANL BEAMFORMING
ZONEA. The downward tilt angle and the beamforming zonearea
The area of beamforming zone can be varied due to theheight, downward tilt angle, vertical and horizontal
beamforming angles of base station (BS) antenna. This
relation is shown in Fig.2 and equation (1). The beam radius R
can be calculated from the BS antenna height H, downward
tilt angle ω, and vertical beamforming angle θ. The
horizontal beamforming angle is assumed to be the same withthe vertical beamforming angle.

Radius of beamforming zone (m)ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

120
100
80
60
40
20
0

0

H

θ

R
Fig. 2. The relation between 3-dimensioanl beamforming zone anddownward tilt angle.

R

H  tan     tan 
2(1)

Since the area of beamforming zone is dictated by the
beamforming radius R, it also increases rapidly depending on
the downward tilt angle especially when the downward tilt
angle is greater than π/4. Fig.3 shows the increase of
beamforming zone diameter as per downward tilt angle. The
BS antenna height H is assumed to be 50 m and beamforming

11.25

22.5
33.75
Vertical tilt angle (Degree)

45

56.25

Fig. 3. The beamforming zone diameter as per downward tilt angle.

B. Our exemplary system
If the BS antenna configuration is as shown in Table I, theexpected beamforming zone of that cell in flat terrain is as
depicted in Fig. 4 and Table II. The vertical beamforming iscomposed of 3 layers whose vertical beamforming angles are
all the same and downward tile angles are different from thoseof others.
TABLE I
BS CONFIGURATION PARAMETERS.
SymbolH

ω

767

ParameterθBS antenna height
Vertical beamforming angleω

Downward tilt angle

φHorizontal beamforming angle

L
R1
R2
R3

Number of vertical layers
Beam radius (1st vertical layer)
Beam radius (2nd vertical layer)
Beam radius (3rd vertical layer)

Value
50m
π/8
0, π/8, 2π/8
π/8
310.36 m14.64 m35.36 mFig. 4 clearly shows that the relation between the distancesfrom BS and the area of beamforming zone. The calculatedarea of each beamforming zone A, B and C under theparameter of Table I also certifies it as is shown in Table II.
The (a) of Fig.4 shows the lateral view of 3-dimensional
beamforming and the (b) is the ground plan of it.
In real cell deployment, each cell radius is much greater,
every beamforming angle is varied or steerable, and eachbeamforming zone shapes oval and surrounded byinterference zone [4]. However we apply simpler model ofbeamforming zone as shown in Fig. 4 for convenientverification of proposed method.

angle θ is 30°.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

θ θ

768

θ

H

A

B

C

R1

R2

R3

Fig. 5. Occupied Radio Resource per UE (%), Number of UEs.

(a) Vertical view of beamforming layers

φ

A

C

B

Vertical Layer 1
Vertical Layer 2
Vertical Layer 3(b) Horizontal view of beamforming layers
Fig. 4. The deviation of beamforming zone area with the distance.

While the transmission data rate of each beamforming does
not vary widely, its coverage is quite different according to
the vertical layer it belongs to. Table II shows the
beamforming zone of belonged to 3rd vertical layer is as wide
as 28-fold of that belonged to 1st vertical layer.

Fig. 5 depicts occupied radio resource by each UE
according to its belonged beamforming layer. Since eachlayer is assumed to be composed of 16 beamformings asshown in Fig. 4, among 1,000 thousand of uniformly
distributed UEs, only 30 UEs are supported by 1st verticallayer and they can occupy about 53.33% radio resource ofeach beamforming, and 142 UEs are supported by 16 2nd layer
beamformings while most UEs - 828 UEs- are belonged to 3rdvertical layer. Since 828 UEs have to share the resources of 16
beamformings in 3rd layer beamforming zone, each UE canoccupy only 1.93% of radio resources of each beamforming inaverage.
In this system, even if the serving BS offers CA with 2 more
frequency band, the benefit of CA converges to only 17.16
percent of UEs in the cell and overall resource occupation of
UE does not varied.

III. USING CA FOR RELIEVING BEAMFORMING ZONEDEVIATION
A. A CA-BS of the same BS type with the main BS

TABLE II
BEAMFORMING ZONE AREAS OF DIFFERENT VERTICAL LAYERS.
Vertical Layer

1st Layer (A)

2nd Layer (B)

3rd Layer (C)Single beamformingzone area (m2)

1347.56506. 537,922.4

Total beamformingzone area (%)

2.94

14.21

82.84

These unequal areas between beamforming zones cause
uneven service quality per equal area in the same cell. If users
are evenly distributed in that cell, 82.84% of users aresupported by beamformings of 3rd vertical layer while only
2.94% of users receive service of 1st vertical layer
beamforming.
Fig. 5 shows the uniformly distributed 1,000 user’s
occupation of radio resources under this circumstance. Allusers in each beamforming are supposed to share the equal
portions of radio resources.

Many remedies can alleviate this resource occupation
inequality. They are the sharper beamforming [10],heterogeneous networking (HetNet) [7][11], relaying [4][5],etc. CA can be one of those solutions also.
Generally, CA is used to improve data rates for UEs andmany scenarios are proposed of its deployment [6]. If CA isoffered by the same BS with the main serving BS, the benefit
is concentrated only a few UEs which are adjacently placed to
BS in 3-dimensional beamforming system.
However, if extra CA base station (CA-BS) is locatedadequately apart, it can reduce the inequality of user service
quality because its highly concentrated service zone is notoverlapped with that of main BS.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

θ θ

θ
θ

θ θ

Fig. 6. The effect of CA for relieving beamforming zone area deviation.

Fig. 6 is the lateral view of the proposed CA-BS
disposition. It shows that adequately located another CA-BS
can offer effectively dense beamformings in large part of thin
3rd layer beamforming zone area by means of carrier
aggregation.
Fig. 7 depicts ground plan of an exemplary CA-applied
cells. Each CA-BS is supposed to be the same type with themain service BS but they use different component carrier
band. This figure shows a large part of cell edge region can besupported by CA -BS.

769

Fig. 8 depicts the calculated number of UEs and their radio
resource occupation in each beamforming. Among uniformly
distributed 1,000 UEs, now 486 UEs are supported by solely
3rd vertical layer, and they can occupy 9.88% ofbeamforming’s radio resource.
88 UEs are supported by the 1st layer and as many as 426
UEs are supported by the 2nd layer beamformings. This means
more than half of UEs in the cell can occupy more thanmoderate part of radio resources.
The resource occupation of UEs in the 1st layer and the 2nd
layer does not much increase as the addition of component
carriers. This means that our proposed method is for
improving frequency resource sharing equality rather thanenhancing data rate for small number of users through carrier
aggregation.
Although the exact radio resource occupation percentage
can be varied with different resource assignment strategy or
CA-BS parameters, this picture shows that each UE canoccupy at least about 10 % of radio resource of eachbeamforming, and can enjoy better degree of freedom inresource assignment.

Vertical Layer 1
Vertical Layer 2
Vertical Layer 3

Fig. 7. Beamforming layer of CA-applied cell.

Fig. 8. Occupied Radio Resource per UE (%), Number of UEs with CA.

B. A CA-BS of the different BS type with the main BS

Fig. 7 depicts the situation of CA with 3 different
component carrier (CC) frequency bands are used. Each
CA-BS is supporting two adjacent neighbor cells also and its
3rd vertical layer beams can be adjustable to avoid inflicting
interference to particular UEs.
With the cooperation of CA-BS, more than half of the UEsof our exemplary system can be supported by either 1st or 2ndvertical layer signals as shown in Table III.
TABLE IIIBEAMFORMING ZONE AREAS UNDER CA CIRCUMSTANCES
CA type

1st Layer +3rd Layer

2nd Layer +3rd Layer

3rd Layer +3rd Layer

Total beamformingzone area (%)

8.82

42.64

48.55

The CA-BS can be more simplified form compared to main
BS. While main BS have to cover all its service zone with
densely packed beamforming zones not to miss any
unsupported UE, CA-BS can offer a few steering beams to
support a small most-needed area [13]. It can dynamicallychange its beamforming’s tilt angle and beamforming angle
vertically and horizontally which dictates the location andsharpness of beamforming.
Fig. 9 shows this kind of CA-BS can support the required
area flexibly. Since in most cases UEs are not evenly
distributed but tend to be concentrated in relatively small area[9], so a small number of steering beams from CA-BS can beeffective in supporting this hot-zone.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

770

diameter-reduced cells in the rainy condition.

IV. CONCLUSIONS

Vertical Layer 1
Vertical Layer 2
Vertical Layer 3
CA-BS beamforming

Fig. 9. The CA-BS with Dynamic steering beamforming.

When the CA-BS offers dynamically steering
beamformings as shown in Fig. 9, the resultant improvement
is shown in Fig.10. The capacity of CA-BS is assumed to be
the same with the vertical layer 2. Although in this case, CA
beamforming can afford only a small number of UEs
compared to the case of Fig. 7, this kind of CA-BS can coverwide range of area with its steering beamformings.
This kind of CA-BS has relatively small capacity and it
offer only one component carrier of frequency resource.
However it is also helpful in supporting moderate data rate to
many disadvantageous UEs.

Fig. 10.Number of UEs in each beamforming layer with dynamic steering
beamforming.

This displacement of extra CA-BSs is more desirable in
mmWave system. Since the propagation limitation ofmmWave is sterner compared to other commercial bandsignal [8], its cell size is relatively small and the extra
CA-BSs’ signal can easily cover the wide range of each cell,while any possibility can be evaded by beam steering.
Generally CA signals are transmitted from the same serving
BS to enhance user data rate. However in the aspect of
beamforming zone area inequality in 3-dimensional
beamforming, we can see that offering CA by separated BS isdesirable.
The separated CA-BSs are also important in the case of
propagation deterioration such as rainfall attenuation, sincemmWave is especially vulnerable to the effect of rainfall [14],separated CA-BSs can fill the coverage gap between the

In this paper, we showed the reason and the seriousness of
the inequality of 3-dimensional beamforming zone area andits solution from adopting additional CA base station. Since
the area of each beamforming becomes wider according to the
distance from the base station, a large part of cell cannot help
being supported from only a small number of beamformings.
Our research is about effectively alleviating this inequality
to share cell capacity more evenly. Whether the CA basestation’s beamforming is the fixed beamforming or steering
beamforming, if it is adequately placed, the radio resource
occupation equality is greatly improved. Usually the CA isused to multiply the capacity of cell, but our research shows it
can be used to alleviate resource occupation inequality also.
Various terrain, UE distribution, and service environment
influence the most efficient cell disposition, and our researchshows cooperation with remote CA base station is also vital inuser service quality in 3-dimensioanl beamforming
environment.

REFERENCES[1]

J. Koppenborg, H. Halbauer, S. Saur, C. Hoek, "3D beamforming trials
with an active antenna array," Smart Antennas (WSA), 2012
International ITG Workshop on, 7-8 March 2012, pp. 110-114[2] B. Ku, D. Ahn, S. Lee, A. Shishlov, A. Reutov, S. Ganin, A. Shubov,"Radiation Pattern of Multibeam Array Antenna with Digital
Beamforming for Stratospheric Communication System: Statistical
Simulation," ETRI Journal, vol. 24, no. 3, Jun. 2002, pp. 197-204.
[3] J. Jung, "Method and apparatus for jointly transmitting/receiving asignal in a mobile communication system," K.R. Patent
10-2013-0127192, Nov. 22, 2013.
[4] J. Bae, Y. Choi, J. Kim, M. Chung, "Architecture and Performance
Evaluation of MmWave Based 5G Mobile Communication System,"
ICTC 2014, 22-24. Oct. 2014, pp.847-851[5] J. Bae, H. Park, S. Lee, Y. Choi, Jun Suk Kim, Min Young Chung,"System Coverage of MmWave Based 5G Mobile Communication
System," APCC 2014, October 1-3, 2014, pp.1-4[6] Z. Shen, A. Papasakellariou, J. Montojo, D. Gerstenberger, F. Xu,
Overview of 3GPP LTE-advanced carrier aggregation for 4G wirelesscommunications. IEEE Communications Magazine, 50(2), 2012, pp.
122-130.
[7] J. Wang, J. Liu, D. Wang, J. Pang, G. Shen, "Optimized Fairness Cell
Selection for 3GPP LTE-A Macro-Pico HetNets," VehicularTechnology Conference (VTC Fall), 5-8 Sept. 2011, pp.1-5[8] M. Kim, S. Hong, Y. Kim, J. Kim, “Analysis of Resource Assignmentfor Directional Multihop Communications in mm-Wave WPANs,”ETRI Journal vol.35, no.1, pp. 120-130, Feb. 2014.
[9] K. Son, S. Chong, "Dynamic Association for Load Balancing andInterference Avoidance in Multi-Cell Networks," WirelessCommunications, IEEE Trans., Volume 8, July 2009, pp. 3566-3576[10] W. Roh, et al. "Millimeter-wave beamforming as an enabling
technology for 5G cellular communications: theoretical feasibility andprototype results," IEEE Communications Magazine, 52(1), 2014,
pp.106-113[11] J. Kim, Y. Han, S. Lee, "An Efficient Downlink CoordinatedBeamforming for Heterogeneous Networks," The 78th VehicularTechnology Conference (VTC Fall), Sept. 2013[12] B. K. Chalise, A. Czylwik, "Robust uplink beamforming based upon
minimum outage probability criterion," Global Telecommunications
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Applications," ETRI journal, vol. 34, no. 1, Feb. 2012, pp. 1-8.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016771

[14] C.Enjamio, E. Vilar, F. Perez-Fontan, "Rain Scatter Interference in
mm-Wave Broadband Fixed Wireless Access Networks Caused by a
2-D Dynamic Rain Environment," IEEE transactions on wirelesscommunications, Vol. 6, July 2007, pp.2497-2507

Jun-woo Kim received the B.S. degree in electronics
engineering from Kyongpook National University,
Daegu, Korea, in 1996, the MS from KAIST, Daejeon,
Korea in 1998, and the Ph.D degree from Chungnam
National University, Daejeon, Korea in 2013.
From January 1998 to September 2001, he was a
researcher of Dacom Corporation. Since October
2001, He has been working in ETRI, Daejeon, Korea,
where he currently works in a Giga wirelesstransmission research section as a senior engineer. His current researchinterests are VSLI, on-chip communication architecture, and various modem
design.
Gosan Noh received the B.S. and Ph.D degrees inElectrical and Electronic Engineering from Yonsei
University, Seoul, Korea, in 2007 and 2012,
respectively.
From March 2012 to February 2013, he was a
Postdoctoral Researcher at the School of Electricaland Electronic Engineering, Yonsei University, Seoul,
Korea. Since March 2013, he has been with theElectronics and Telecommunications ResearchInstitute (ETRI), Daejeon, Korea, where he is a Senior Researcher. His
research interests include millimeter wave transmission and polarization
diversity/multiplexing techniques.
Jang-won Moon received the BS degree in electronics
engineering from Shibaura Institute of Technology,
Tokyo, Japan, in 2007, the MS degree in wirelesscommunication engineering from Waseda University,
Tokyo, Japan in 2009. Since 2010, He has beenworking in ETRI, Daejeon, Korea, where he currently
works in a Giga wireless transmission research section
as a senior engineer. His current research interests are
VSLI, on-chip communication architecture, andvarious modem design.
Youn-Ok Park received the B.S. degree inElectronic Engineering from Hanyang University,
Seoul, Korea, in 1986, M.S. and Ph.D degrees inInformation and Communication Engineering from
Chungnam National University, Daejeon, Korea in1997 and 2011 respectively.
From December 1985 to January 1987, he was a
researcher of Samsung Electronics. Since February
1987, He has been with the Electronics andTelecommunications Research Institute (ETRI), Daejeon, Korea, where he is
a Principal Member of Technical Staff of Giga wireless Transmission
Research Laboratory Section.
Il-gyu Kim received the B.S. and M.S. degrees inelectronics engineering from the Seoul City
University, Seoul, Korea, in 1993 and 1995,
respectively. In 1995, he joined the Network
Implementation Section of Shinsegi Telecomm,Inc. (STI), Seoul, where he was involved in theimplementation of CDMA cellular systems. Since
2000, He has been working in ETRI, Daejeon,
Korea, where he currently works in a Gigawireless transmission research section as a Section Leader.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

772

Differentiated Assignment of Extrinsic Informationin Iterated Decoding of Fixed Weight Codewords
Wonsun Bong*, Yong Cheol Kim*
*Dept. of Electrical and Computer Eng., University of Seoul, Korea
gaam@uos.ac.kr, yckim@uos.ac.kr

Abstract—Constant amplitude multi-code (CAMC) CDMA has
the same structure as a recursively generated single parity checkproduct code. A top-level codeword of CAMC is recursively
constructed from lower-level codewords. In the iterative decodingof CAMC, log likelihood ratio (LLR), a priori information andextrinsic information (EI) of a codeword is a weighted sum of
LLR values of associated codewords from which it is despread orinto which it is spread. In this paper, we show that differentiatedassignment of EI in the computation of LLR can improve theperformance of bit error correction. The weights of CAMC
codewords are fixed at two fixed values. We let EI converge fast
to saturation value when a codeword has the correct weight.
The proposed method achieved performance improvement of
0.1 ∼ 0.3 dB in Eb /N0 over the regular iterated decoding of
CAMC. When compared with despreading ON/OFF control, again of about 0.1 dB is achieved, which is meaningful near the
Shannon capacity limit.
Keywords—Constant Amplitude Multi Code, Code Weight, Extrinsic Information, Iterated Decoding, Single Parity Check Product
Code

I. I NTRODUCTIONRODUCT codes were first introduced by Elias in 1954 [1].
The concept of product codes is that powerful long block
codes can be constructed by concatenating two or more shorter
constituent codes. Single parity check product code (SPCPC)
is a product code in a simple structure, where a parity bit isappended to a sequence of information bits [2].
A codeword of 3-D (dimensional) SPCPC is shown in Fig. 1,
which is composed of the data block, the parity checks along
all three directions and parity on parity check bits. Multidimensional SPCPC are constructed in a similar way. In theencoder, a parity bit is appended to each of (n − 1)-bitlong sequences along all the dimensions of a Q-D hypercube
Qconsisting of (n − 1) information bits. The encoded output ofQnQ bits is a Q-D product code with a code rate of (1 − 1/n) .

P

Manuscript received date is November 9, 2015. This research was supported
by Basic Science Research Program through the National Research Foundationof Korea funded by the Ministry of Education, Science and Technology (grant
number NRF-2013R1A1A2012745). This paper is a follow-up of the invitedjournal to the accepted conference paper of the 16th International Conference
on Advanced Communication Technology.
Wonsun Bong is with the department of Electrical and Computer Engineering, University of Seoul, Seoul 02504, Korea (email: gaam@uos.ac.kr). Yong
Cheol Kim is with the department of Electrical and Computer Engineering,
University of Seoul, Seoul 02504, Korea (corresponding author; +82-2-64902331; e-mail: yckim@uos.ac.kr).

v00
N 

p

p

p

p

p

p

p

p

p p
v00 v01 v02

p

p

p

p

p

v
v
vp
v00 v0110v0211  12v20 v21 v22v12 
vv10 vv11 01

02

GGGG 20 vv
21 v
v10 vv11
22
12

GGGG

v20 v21v22
GGGG GGGG GGGG GGGG Q 

Fig. 1: 3-D SPCPC: Parities are in all three directions.

Kim presented a recursive SPCPC, where a codeword isrecursively constructed by pseudo-Hadamard spreading of
three lower-dimensional codewords concatenated with theirparity bits [3]. Recursive SPCPC was originally developed asa constant amplitude multi-code (CAMC) CDMA.
Multi-code CDMA is a technique of providing versatile data
rates by assigning multiple channels to a single user [4] [5] [6].
Multi-code signal has a large amplitude variation since it isa sum of random binary signals from several channels. Large
variation signal requires a highly linear power amplifier, which
consumes a large power. CAMC was developed to perfectly
remove the amplitude fluctuation of multi-code signal.
The encoder of CAMC accommodates M (= 3Q ) information bits, bM = [b0 , b1 , ..., bM −1 ], and generates a N (= 4Q )bit-long string, vN = [v0 , v1 , ..., vN −1 ], of constant amplitude
by pseudo-Hadamard spreading in a recursive manner. The
code rate of CAMC is R = (3/4)Q , which is equivalent tothat of SPCPC with n = 4. Throughout this paper, recursive
SPCPC with n = 4 is interchangeably referred to as CAMC.
Previous works show that CAMC outperforms conventional SPCPC. CAMC has some advantageous features asfollows [7] [8]. First, CAMC benefits from the despreading
process which is performed after the iterative decoding. Second, CAMC does not have any low weight codewords whichusually degrade the performance at low SNR. The weights ofcodewords are evenly distributed at two fixed values. Third,
the property of fixed weight can guide the computation of
extrinsic information (EI) which is the key element in theiterative decoding.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

Analysis on the first and the second features were reported
in [8] [9]. In this paper, we focus on the third feature. We
show that, with differentiated assignment of EI based on the
integrity of code weights, we get performance improvement of
0.1∼0.3 dB when compared to previous works.
This paper is organized as follows: In Section II, encoding
and decoding of CAMC are briefly presented. In Section III,
iterated decoding of conventional SPCPC is described. In
Section IV, iterative decoding and despreading of CAMC arepresented. In Section V, ON/OFF control of despreading after
iterative decoding is presented. In Section VI, the proposed
differentiated assignment of EI, based on the fixed weightproperty of CAMC, is presented. In Section VII, computer
simulation results on performance improvement are presented.
Finally, a conclusion is drawn in Section VIII.
II.

In this Section, the generation of CAMC signal vectors isbriefly described [3]. Throughout this paper, the polarity of a
bit is bipolar, either (+1) or (-1). An encoded CAMC vector
at J-level has a length of L(= 4J ) bits. Then, for every three
J-level vectors, a bit-by-bit J-level parity vector is generated.
Spreading the concatenation of three J-level vectors and theirparity vector generates a (J + 1)-level vector with a length of
4L bits. Inversely, despreading a J-level vector results in three(J − 1)-level signal vectors and their bit-by-bit parity vector,
each with a length of L/4 bits.
In the following notations, a superscript represents thesize of the vector, except when the subscript is of the form
i/4. Integer subscripts of {0, 1, 2, 3}, if any, stand for the
distinct number of vectors. Either viL or vL is a L-bit-long
CAMC vector, There is no meaningful difference between
them. Subscripts of {0/4, 1/4, 2/4, 3/4} represent the index
of the four quadrants of a regular CAMC vector. For example,L
vi/4, i ∈ {0, 1, 2, 3}, is not a regular CAMC vector by itself,
but just a quadrant of a regular L-bit-long CAMC vector, vL ,as shown in (1). The vertical bar represents concatenation.
L
L
L
L
vL = [v0/4| v1/4| v2/4| v3/4](1)

Fig. 2 and Fig. 3 show the generation of parity bits in a
CAMC vector [3]. An input of M information bits is divided
into M/3 strings of three bits each. Each 3-bit-long string is
encoded at the basic-level encoder Q4 . For input [b0 , b1 , b2 ],a parity bit p3 = −b0 · b1 · b2 is appended to them. Then, four
bits of unit amplitude, v04 = [v0 , v1 , v2 , v3 ] are generated fromspreading by 4 × 4 Hadamard matrix.
1
v04 = · [b0
2



1
 1
4
H =
1
1

p3 ] · H

4

b1

b2

1
−1
1
−1


1
1
1 −1 
−1 −1 
−1
1

b0
b1

b3

(2)

In a similar way, [b3 , b4 , b5 ] and [b6 , b7 , b8 ] are encoded into
v14 and v24 of constant amplitude. A 4-bit-long parity p4 is

~
H4

b3

v4

Fig. 2: Generation of 4-bit CAMC vector

b M/3
0

bM

ý3
3

v 1N/4Qb M/3
2

v 0N/4

QN/4

b1M/3

NÝN

N/4

v

Q N/4

~
HN

N/4
2

1
2

vN

v N/4
3

Fig. 3: Generation of N-bit CAMC vector

generated from bit-by-bit product (denoted as .∗) of v04 , v14
and v24 .
p4 = −v04 . ∗ v14 . ∗ v24(4)

For the spreading of concatenation of three CAMC vectors
and one parity vector, we use a pseudo-Hadamard matrix, H̃N .
This is in the form of Hadamard matrix with ”1” element in(3) replaced by an identity matrix IN/4 of size N/4 × N/4.
As a special case, H̃4 is identical to H4 .
 N/4

I
IN/4
IN/4
IN/4
 IN/4 −IN/4
IN/4 −IN/4 H̃N = (5)
 IN/4
IN/4 −IN/4 −IN/4 
IN/4 −IN/4 −IN/4
IN/4
Both H̃N and HN are orthogonal, subject to a scaling factor.
H̃N · (H̃N )t = H̃N · H̃N = 4 · IN

(6)

HN · (HN )t = HN · HN = N · IN

(7)

The 16-bit-long concatenation of v04 , v14 , v24 and p4 is
spread into v16 of unit amplitude.
v16 =

(3)

1
2

4Ý4

b2

S/P

distributor

C ONSTANT A MPLITUDE M ULTI C ODE

773

1· [v04 | v14 | v24 | p4 ] · H̃16
2

(8)Continuing this way, the output of three QN/4 encoders(3N/4 bits, in all) and their bit-by-bit parity vector (N/4 bits)
are generated.
N/4

pN/4 = −v0

N/4. ∗ v1

N/4. ∗ v2(9)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

N/4

N/4

N/4

Finally, thee N -bit-long concatenation of v0 , v1 , v2
and pN/4 is spread by H̃N , into vN = [v0 , v1 , ..., vN −1 ] ofunit amplitude.
vN =

1
N/4
N/4
N/4
· [v0 | v1 | v2 | pN/4 ] · H̃N
2(10)

While a top-level codeword vN is encoded from recursive
spreading by pseudo-Hadamard H̃N , decoding of vN is obtained from one-time despreading by a regular HN .
v N · HN

The values of correlation vector, D, of noise-free vN are
di = ±1. But, D obtained from a noisy received signal at thereceiver takes on non-binary values. In this case, we hard-limit
D into binary values. The correlation vector [d0 , d1 , d2 , ...] isin the form of a hypercube consisting of information bits andparity bits, just like a conventional SPCPC as shown in Fig. 1.
The information bits are extracted from the corresponding
positions. Bits {d3 , d7 , d11 , d12 , d13 , d14 , d15 , d19 , ...} are in
the positions which correspond to the parity positions in thehypercube. Removing such bits, we get the information-onlybits [b0 , b1 , b2 , b3 , b4 , b5 , b6 , b7 , ...].
III.

Eq (Xk )). X = [X0 , X1 , ..., XN −1 ] is the input vector andY = [Y0 , Y1 , ..., YN −1 ] is the received signal vector through abinary-input AWGN channel.
Lq(Xk ) = log

Pr(Xk = +1 | Y)2
= 2Yk+Eq(Xk)+Aq(Xk) (12)
Pr(Xk = −1 | Y) σ
[ N −1
∏

(
)]
2
A
(X
)+
Y
2
q
j
j
σ
Eq (Xk ) = 2 tanh−1
tanh
2
j=0,j̸=k

(11)

N/4
N/4
N/4
N/4
= 2[v0 HN/4 | v1 HN/4 | v2 HN/4 | v3 HN/4 ]= 2(log4 N −1) [v04 H4 | v14 H4 | v24 H4 | v34 H4 | . . .]= 2log4 N [d0 , d1 , d2 , d3 , d4 , . . .]I TERATIVE D ECODING OF A P RODUCT C ODE

The decoding process of a product code is similar to solving
a crossword puzzle. In the product array, one symbol is
associated with two values through diversity effect: One is thevery value of the received symbol itself and the other is theextrinsic value which can be inferred from the other symbols.
When bit errors through a channel , these two values may
be different. This discrepancy can be relaxed in the iterative
decoding process where the range of the possible values ofa target symbol is adjusted through exchange of EI amongneighboring symbols.
Being a product code, SPCPC has powerful error correcting capability, SPCPC has the same basic features of turbo
codes: interleaving, iteration and soft-output decoding [10].
Hagenauer developed a soft input, soft output-based decoding
algorithm for a multi-dimensional product code [11].
Rankin [2] extended the works of [11] and proposed an
iterative decoding algorithm for SPCPC. It is an iterativealgorithm which refines the log likelihood ratio (LLR) for
each bit by iteratively exchanging information in a relaxational
scheme.
In SPCPC, a parity bit is appended along all the dimensionsof a Q-D interleaved hypercube. The LLR, Lq (Xk ), for the kth bit in the q-th dimension is iteratively refined by exchanging
the extrinsic information between dimensions.
LLR consists of three terms: the channel reliability which
is proportional to the signal strength, the a priori information (API, Aq (Xk )) and the extrinsic information (EI,774

Aq (Xk ) =Q∑

Ei (Xk )

(13)

(14)

i=1,i̸=q

IV. D ECODING OF CAMC
CAMC is generated by direct sequence spreading of lower
dimensional codewords concatenated with their bit-by-bit parity vector. The internal structure of CAMC has both aspectsof a product code and a spread spectrum signal.
The decoding process of CAMC consists of two stages: theiterative decoding for SPCPC and the despreading for spread
spectrum signal. First, the noisy received bits, Y, are iteratively
decoded into X, which consists of information bits and parity
bits, as encoded in the recursive encoder. X is despread into
D, which consists only of information bits. Fig. 4 illustrates
the flow of data, the recursive spreading, transmission througha noisy channel, iterated decoding and despreading.
Since CAMC belongs to SPCPC, a similar decoding algorithm could be used for CAMC. The decoding algorithm forconventional SPCPC described in Section III, however, cannot
be directly applied to CAMC. Unlike SPCPC, there are no raw
parity bits in a CAMC vector since parity bits are mixed withinformation bits through the spreading process.
Kim developed a decoding algorithm which separates parity
bits and then iteratively refines the LLR of the bits [7]. Parity
bits required for the iterative decoding are recursively extracted
by decomposing higher level CAMC vectors into lower level
vectors. One J-level vector, vL , is despread by H̃L into four
L/4
L/4
L/4
(J − 1)-level vectors, (v0 , v1 , v2 , pL/4 ).
1 L· v · H̃L
2

(15)

1
L/4
L/4
L/4
· [v0 | v1 | v2 | pL/4 ] · H̃L · H̃L
4
L/4
L/4
L/4
= [v0 | v1 | v2 | pL/4 ]=The fourth vector, pL/4 , is the bit-by-bit parity vector of thethree preceding CAMC vectors. Each of the three vectors,
L/4
L/4
L/4
{v0 , v1 , v2 }, can be despread into four (J − 2)-level
L/16
L/16
L/16
CAMC vectors, [v0 | v1 | v2 | pL/16 ].
The parity relation among four J-level vectors holds onlyin the context of the J-th dimension. Likewise, EI and API
associated with J-level CAMC vectors are valid only in the
J-th dimension. For other dimensions, we need to spreador despread CAMC vectors as needed. Hence, when EI is
exchanged between dimensions, it needs to be spread or

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

࡮

ࢂ

Recursive
Spreading࢈૙ ǡ ࢈૚ ǡ ‫ ڮ‬ǡ ࢈ࡹି૚

ࢅ

෡
ࢄൌࢂ

Iterative
Decoding࢟૙ ǡ ࢟૚ ǡ ‫ ڮ‬ǡ ࢟ࡺି૚Noisy Channel࢜૙ ǡ ࢜૚ ǡ ‫ ڮ‬ǡ ࢜ࡺି૚ࢅ࢟૙ ǡ ࢟૚ ǡ ‫ ڮ‬ǡ ࢟ࡺି૚෡ࡰൌ࡮Despreading࢞૙ ǡ ࢞૚ ǡ ‫ ڮ‬ǡ ࢞ࡺି૚775

ࢊ૙ ǡ ࢊ૚ ǡ ‫ ڮ‬ǡ ࢊࡹି૚Fig. 4: Received bits (Y) are iteratively decoded (X) and then despread into information bits (D).

S

S

D

¦

¦
API

YN/4
D

DecoderD

top level

API
EI

YN/16

Decoder=Despreading

S

EI

YN/64
D

DecoderThird level

Second level

D

D

¦
API

EI

YN

D

S

=Spreading

Fig. 5: EI is despread/spread between dimensions.

despread to fit into the structure of three CAMC vectors plus
one parity vector in the corresponding dimensions.
Fig. 5 illustrates the block diagram for the decoding of
CAMC for N = 64. The blocks S(.) and D(.) stand forthe spreading process and the despreading process, respectively. Expressions for LLR (Lq (Xkq )), EI (Eq (Xkq )) and API(Aq (Xkq )) for CAMC are shown in (16) through (18).
Xkq is the k-th bit of the vector reconfigured into the q-th diq
qmension. [X0q , X1q , . . . , XL−1
] and [Y0q , Y1q , . . . , YL−1], (L =q4 ), stand for an input vector and the received vector reconfigured into the q-th dimension.
Lq (Xkq ) =Eq (Xkq ) = 2 tanh−1

Aq (Xkq ) =

q−1
∑
i=1

2 q
Y + Eq (Xkq ) + Aq (Xkq )
σ2 k

(16)

[ N −1∏()]
Aq (Xjq )+ σ22 Yjq
tanh
(17)
2
j=0,j̸=k

S(Ei (Xkq )) +

Q
∑
i=q+1

D(Ei (Xkq ))(18)

The final estimate of XkQ , is obtained from hard-limitingof the top-level LLR. In the case of conventional SPCPC, thedecoding process would end here. Unlike SPCPC, the decoding
process of CAMC passes through one more stage, as shownin Fig. 4. In CAMC, the decoded output is obtained fromdespreading of iteratively decoded signal. The LLR values of
the iteratively decoded bits are multiplied by HN and then theinformation bits are extracted from the despread hypercube.
Though the structure of CAMC is similar to conventional
SPCPC, some distinct features of CAMC provides advantage
over SPCPC. The first advantage is the processing gain whichcomes from the spread spectrum property of CAMC. While,in conventional SPCPC, correction of bit errors is performed
only in the iterative decoding stage, additional correction ofbit errors is also achieved in the despreading of the iteratively
decoded bits in CAMC.
Another advantageous feature is the fixed weight of CAMC,
as will be described in Section VI. In error correcting codes,
the minimum distance of the code has been regarded as animportant factor for BER performances. Battail [13] suggested
that, in SPCPC, the weight distribution is more important thanthe minimum distance.
Later, Biglieri [14] showed that iterated product codes have
Gaussian weight distribution even when they are relatively

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016776

BER of CAMC and SPCPC
Uncoded BPSKCAMC 2-D
SPCPC 2-D
CAMC 3-D
SPCPC 3-D
CAMC 4-D
SPCPC 4
4-D
D
Shannon (4-D)

-1

10

-2

10

-3

B
BER

10

-4

10

CAMC
-5

10

SPCPC
-6

10

-2

-1

0

1

2

3

4

Fig. 7: Over iterations, |EI| converges to |Emax |5

Eb/No [dB]Fig. 6: CAMC, SPCPC and Shannon limit

short codes. The random-like criterion for designing a product code (or a turbo code) was supported by several otherworks [15]. A product code with Gaussian weight usually has
good performance. Some of the codewords, however, at the
tail of the Gaussian distribution have small weights and canaffect the performance adversely.
While conventional SPCPC usually has Gaussian weight
distribution, the codeword weights of CAMC are very close
to the N/2. Hence, CAMC does not have any low weightcodewords which usually degrade the performance at low SNR.
As a comparison of (12),(13),(14) with(16),(17),(18) shows,
iterative decoding of CAMC is more expensive than theiterative decoding of conventional product codes since thecomputation is crossing over the whole dimensionality of
CAMC. This computational cost, however, helps to achieves aperformance improvement in error correction.
In Fig. 6 is shown the performance of CAMC compared withthat of corresponding SPCPC with n = 4 [7]. BPSK modulated
signal is transmitted through a binary-input AWGN channel.
The code rates are R2 = 9/16 (2-D), R3 = 27/64 (3-D) andR4 = 81/256 (4-D), respectively. For BER of 10−5 , CAMC
outperforms SPCPC by 1.3∼1.4 dB. The Shannon capacity
limit of the binary-input AWGN channel for code rate 81/256
is −0.55 dB. Hence, 4-D CAMC is only 0.95 dB away from
Shannon limit [12].
V.

O N /O FF C ONTROLLED D ESPREADING OF CAMC

EI is a rough estimate of the reliability for the received
signal. We examined how the distribution of EI changes overthe iterative steps [8]. Fig. 7 shows the histogram of |EI| during
the process of the iterated decoding. At the initial stage, a largepart of the EI values are randomly distributed in the range
between −Emax and +Emax . Gradually, EI converges either
to +Emax for a positive bit or to −Emax for a negative bit.
±|Emax | is the saturation value of EI which is set to prevent
EI from diverging.

The convergence of EI is highly correlated with the processof error correction. Fast convergence to ±|Emax | implies
that errors continue to be corrected. On the contrary, slow
convergence or no convergence imply that soft decision values
are close to zero and the polarity of EI randomly toggles.
Practically, no errors are being corrected when EI does notconverge.
We can use EI as a performance predictor of despreading
control, where the information bits are finally obtained fromdespreading of iteratively decoded signal. In the despreading
process of the previous work [7], the LLR values of all theiteratively decoded bits are multiplied by HN .
We modified this scheme such that such that only those bitswith EI converged to ±|Emax | keep their LLR values. The
other bits with |EI| < |Emax | are assigned LLR values ofzero [8]. The basic idea is that we perform despreading only
when it is likely to help to reduce the bit errors. This algorithm
has been tested and it brings a performance gain of about 0.2
dB for 4-D CAMC [16].
VI.

D IFFERENTIATED A SSIGNMENT OF EI BY W EIGHT

An interesting feature of CAMC is that, unlike conventional
SPCPC, the weights of CAMC are evenly distributed at two
fixed values [9].
√w(vN ) = (N ± N )/2(19)
This is true of codewords at any level. When a N -bit-longcodeword is despread into sub-level
codewords of L-bit-long,√they also have weights of (L± L)/2. Weights for code lengthof N = 4 ∼ 64 are shown in Table I.
TABLE I
Weights of CAMC for N = 4 ∼ 64
Code Length (N)Dimension
Weights

4
1
1,3

16
2
6,10

64
3
28,36

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

777

YL
D

DecoderNo

Is weight(YL/4 )correct??

ࡺି૚ࡱࡵ ൌ ૛‫ିܐܖ܉ܜ‬૚ ෑ ‫ ܐܖ܉ܜ‬Ǥ
࢐ୀ૙
࢐ஷ࢑

Yes

EI-2

ࡱࡵ ൌ േ ࡱ࢓ࢇ࢞EI-1

Switch between EI-1 and EI-2

EI

Fig. 8: EI converges fast to ±|Emax | if weight is correct.

The property that CAMC has fixed weight can be used toLdetect erroneous received√ vectors. When the weight ofLy
is not equal to (L ± L)/2, then at least one bit of y iserroneous. The uncertainty range of the bit error is the entire
length of the received vector. This range can be reduced to
1/4 by despreading yL into four quarter-sized vectors andexamining the weight of each of them. This is the motivationof differentiated assignment of EI to codewords in accordancewith their weight integrity.

A. Reduction of Uncertainty Range by Despreading
The received signal yL is a sum of a CAMC vector and anoise vector.
yL = vL + nL
vi ∈ {+1, −1}{
no error −1 ≤ ni ≤ +1
error
if vi = −1 AND ni ≥ +1
yi
error
if vi = +1 AND ni ≤ −1

(20)

Despreading yL by H̃N generates three CAMC vectors anda parity vector. Using (6) and (10), we get:1 L
· y · H̃L
2

(21)

1
L
L
· [v0/4+ nL0/4 | v1/4
+ nL1/4 | . . .] · H̃L
2
L/4
L/4
L/4
L/4
=
[y0 | y1 | y2 | y3 ]=Then, it follows that each of the four despread vectors,
L/4
L/4
L/4
L/4
{y0 , y1 , y2 , y3 } is a sum of a regular CAMC vector(or a parity vector) and noise vectors.
nL = [nL0/4 | nL1/4 | nL2/4 | nL3/4 ]

(22)

}
1{ L
n0/4 + nL1/4 + nL2/4 + nL3/4
(23)
2{
}
1
L/4
L/4
nL − nL1/4 + nL2/4 − nL3/4
y1 = v1 +2 { 0/4}
1
L/4
L/4
nL0/4 + nL1/4 − nL2/4 − nL3/4
y2 = v2 +
2{
}
1
L/4
L/4
y3 = p+nL0/4 − nL1/4 − nL2/4 + nL3/4
2
The added noise is one half of the sum of four quadrants of
the noise vector. If we assume that nL is an AWGN random
L/4
process, then the noise component in yi is also an AWGN
L/4
random process and the noise variance in each bit of yi isLthe same as that of each bit of y .
With equal noise power, the expected number of bit errors
L/4
L/4
L/4
L/4
is also the same in yL and in [y0 | y1 | y2 | y3 ]. For
example, if yL has a single bit error, then it is highly likely
L/4
that only one of yi , i ∈ {0, 1, 2, 3}, is in error and the otherthree are free from errors. In that case, we can easily find which
one of the four is in error by examining the weight of each of
L/4yi . As a result, the uncertainty range of the position of bit
error is reduced to 1/4 of the initial range.
L/4

y0

L/4

= v0

+

B. Assignment of EI Based on Weight Integrity
The fixed value of code weight can guide the iterative
decoding. In a product code, the magnitude LLR of a bit is arough estimate of the reliability of the bit, that is, how well
the parity relation is consistent with other bits. The magnitudeof LLR for noisy bits is usually low.
In the iterative decoding of CAMC, the LLR of a codeword
is a weighted sum of LLR values of associated codewords
from which it is despread or into which it is spread. If any of
the codewords have a wrong weight, then they are definitely inerror. The iterative decoding can be improved by differentiatedhandling of EI of codewords in accordance with their weight
integrity. We can give larger confidence to those codewords
with correct weights.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

BER with EI assign control ON/OFF-1

10

EI OFF 2-D
EI ON 2-D
EI OFF 3-D
EI ON 3-D
EI OFF 4-D
EI ON 4-D

-2

10

-3

10

BER

We propose to let EI quickly converge to ±Emax if theweight is correct. When the weight of the codeword has thecorrect weight, EI for the bits in the codeword takes on thesaturation value. We set EI = +|Emax | or EI = −|Emax | if
the sum in (17) is positive or negative, respectively. For theother bits in codewords with wrong weights, the computationof EI follows the normal computation in (17).
Fig. 8 is the modified block for EI computation which
replaces the dotted block in Fig. 5. The modified version of
EI computation in (17) is shown in (24).

778

-4

10

-5

10

[∏
(A (X q )+ 2 Y q)]

q
N −1
j
−1
σ2 j

2
tanh
tanh

j=0,j̸
=k2
qbits in wrong codeword
Eq (Xk ) =

±E

max

bits in correct codeword

-6

10

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Eb/No [dB]Fig. 9: Differentiated EI vs. plain decoding(24)

VIII. C ONCLUSIONS√ The codewords of CAMC have fixed weight of (N ±N )/2. This feature provides outperformance of CAMC overconventional SPCPC. The uncertainty range of the position ofbit errors can be reduced to 1/4 by examining the weights ofdespread quarter-sized vectors. We showed that differentiatedcomputation of EI depending on the integrity of a code weighthelps to improve the performance.
In the proposed scheme, a codeword with a correct weightis given a larger confidence. EI values for their bits quickly
converge to ±Emax . Performance improvement of 0.1 ∼ 0.3
dB in Eb /N0 are obtained, when compared with plain iterated
decoding. A gain of about 0.1 dB is when compared with
despreading control of CAMC where the improvement is

Comparison of despreading control and EI control-1

10

DS 2-D
EI+DS 2-D
DS 3-D
EI+DS 3-D
DS 4-D
EI+DS 4-D

-2

10

-3

10

BER

VII. R ESULTS OF D IFFERENTIATED EI
The performance of the differentiated EI assignment istested in a computer simulation. Comparison with two previous
results are presented. Both results are for BPSK modulations of
2-D, 3-D and 4-D of CAMC in binary-input AWGN channel.
First, a comparison with plain iterated decoding of CAMC [7]is shown in Fig. 9. A gain of 0.1∼0.3 dB in Eb /N0 is achieved.
Second, a comparison with despreading On/Off control in
Section V is shown in Fig. 10. We get a gain of about 0.1
dB. Though the amount of gain appears small, this gain ismeaningful near the Shannon capacity limit, which is achieved
by CAMC. We can observe that most of the improvement are
obtained in channels of high SNR. For low SNR channel, little
improvement is obtained. A logical analysis is as follows:
There are two possibilities of a codeword having the correct
weight. One is that an erroneous codeword with even numberof bit errors with opposite polarity canceling each otherhappens to have a correct weight. When the channel SNR islow, this probability is not negligible. Favored assignment of
EI to these unqualified codewords does not help. It may evenslow down the convergence of EI.
The other possibility is that the codeword is indeed errorfree. This probability gets larger as SNR is higher. Preferredassignment of EI at high SNR helps.

-4

10

-5

10

-6

10

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Eb/No [dB]Fig. 10: Differentiated EI vs. despreading control

mostly found in high SNR channel. Near the Shannon capacity
limit, which is achieved by CAMC, even slight values of gains
are meaningful.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 1, January 2016

[7]

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Wonsun Bong received the B.S. in electrical engineering from Cheongju University, Korea, in 2009
and the M.S. degree in the electrical and computerengineering from the University of Seoul, Korea,
in 2011. He is currently working toward the Ph.D.
degree at the department of electrical and computerengineering in the University of Seoul, Korea.
His research interests are wireless communications, signal processing with an application to security surveillance systems from image sand videos.

Yong Cheol Kim (M’93) received the B.S. degree in electronics engineering from Seoul National
University, Korea, in 1981 and the M.S. degree inelectrical engineering from KAIST, Korea, in 1983
and the Ph.D. degree in electrical engineering from
University of Southern California, Los Angeles, in
1993.
From 1993 to 1996, he was a team leader in Military Digital Communications Sector in LIG Nex1,
Korea. In 1996, he joined the faculty at the Department of Electrical Engineering, University of Seoul,
Korea. He is currently a Professor at the Department of Electrical andComputer Engineering. His research interests include mobile communications,image processing and computer vision.
Professor Kim is a member of Association for Computing Machinery,
Institute of Electronics Engineers of Korea and The Korean Institute ofCommunications and Information Sciences.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)