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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Prof. Chih-Lin HU, National Central University, Taiwan
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Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
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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
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Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
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Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
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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 ⓒ 2017 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 ⓒ 2017 GiRI (Global IT Research Institute)

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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Volume. 6 Issue. 1

1 A Process-Aware Goal Description Language for the Internet of Things Community Computing

5J

966

Environments
Hyunah Kim*, Kwanghoon Pio Kim*
*Dept. of Computer Science, KYONGGI UNIVERSITY, Suwonsi Kyonggido, Republic of Korea

2 Virtual Antenna Mapping MIMO Techniques in a Massive MIMO Test-bed for Backward

971

Compatible LTE Mobile Systems
Seok Ho Won, Saeyoung Cho, Jaewook Shin
Mobile Communication Department, Electronics and Telecommunications Research Institute, Daejon, Korea

3 Experimental Validation of Multipoint Joint Processing of Range Measurements via SoftwareDefined Radio Testbed
Georgy Mashkov*, Eugene Borisov*, Grigorii Fokin** State University of Telecommunication, 22 Prospekt Bolshevikov, St. Petersburg, Russia

979

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

966
1

A Process-Aware Goal Description Language for the Internetof Things Community Computing Environments
Hyunah Kim*, Kwanghoon Pio Kim*

*Dept. of Computer Science, KYONGGI UNIVERSITY, Suwonsi Kyonggido, Republic of Korea
hyuna2486@naver.com, kwang@kgu.ac.kr

Abstract—This paper proposes an abstract language for describing process-aware goals to be accomplished by collaborative
smart-objects communities over the Internet of Things (IoT) platforms. The proposed abstract language is based upon the processdriven IoT-community computing model [1] that is derived froma conceptual integration of the process-aware collaborations
and the standardized IoT framework announced via the ITU-T
SG131 Y.2060. We assume that a group of collaborative smartobjects communities can be built-up statically, dynamically, orautonomously and their process-aware goals can be specified
and achieved adaptively over an IoT-based community computing
environment. We also strongly expect that the proposed abstract
language will deliver us a meaningful means in specifying
and achieving adaptive process-aware goals of the IoT-based
communities formed in a ubiquitous computing society.
Keywords-community computing model, the Internet of Things,
Web of Things, smart-objects collaboration, process-aware goal
description language, ubiquitous community computing architectures and systems

I. I NTRODUCTION
In recent, we are in the midmost era of the Internet ofThings[2], which provides a variety of smart-objects collaboration services on ubiquitous computing environments. The
widespread use of mobile smart-phones, smart-watches andO2O2 products is the evident, and these little gadgets havebecome an integral and intimate part of everyday life for many
millions of people, even more than the internet users. According to the report of [2], this situation opened a new form ofcommunication between people and things, and between things
themselves, which means that a new dimension, anything
connectivity, has been emerging in the world of informationand communications technologies characterized with anytime
connectivity and anyplace connectivity for anyone. These three
dimensions (anything, anytime, and anyplace) of connectivity———————————————————Manuscript received October 31, 2016. This work is a follow up of
the accepted conference paper as an outstanding paper in the 18th IEEE
International Conference on Advanced Communications Technology.
Hyunah Kim is with the department of computer science, Kyonggi University, 154-42 Kwangkyosan-ro Youngtong-gu Suwon-si Gyeonggi-do, 16227,
Republic of Korea (e-mail: hyuna2486@naver.com)
Kwanghoon Pio Kim is with the collaboration technology research laboratory in the department of computer science, Kyonggi University, 154-42
Kwangkyosan-ro Youngtong-gu Suwon-si Gyeonggi-do, 16227, Republic ofKorea (correponding author, phone: +82-31-249-9679, fax: +82-31-249-8949,
e-mail: kwang@kgu.ac.kr)
1 International Telecommunication Union, Telecommunication Standardization Sector, Study Group 13 - Future Networks
2 O2O stands for Online-To-Offline combining the online shopping and thefront line transactions, and it represents a recent IT convergence businessmodel and its related products.

give a clue to creating an entirely new dynamic network of
networks—the Internet of Things (IoT)—as a future network.
The Internet of Things (which is abbreviated as IoT) is aconceptual platform for the ubiquitous community computing
environments[3], and it is conceptually widened into the Webof Things (WoT)[4] and the Web of Objects (WoO), whichhave been recently issued in the RFID and sensor network(USN) literature. The IoT-based community computing environment supports the concept of ubiquitous community andsociety computing models and systems[3], and physically
implies a computerized situation or space formed by a groupof smart objects (or Things)[5], such as devices, sensors,
actuators, and people as well, each of which may have various
computing capabilities and/or ubiquitous networking capabilities. In the previous research of the authors’ research group,
we proposed an advanced and new community computing
concept, which is called process-driven community computing model[1], with aiming to be deployed on the IoT-based
community computing environment.
Imagine that a group of community-members (smart objects
or Things) on an IoT-based community conducts their own
roles to accomplish the community’s goal in a fashion ofprocess-aware collaborations[6][7]. For the sake of realizing
the imagination, we tried to extend the ITU’s standardized IoTframework by embedding the concept of process-aware collaborations into the standardized IoT conceptual architecture. As
the next step of the research work, this paper devises an XMLbased abstract language to describe a process-aware goal thatshould be accomplished by an IoT-based community over theInternet of Things community computing environment. The
XML-based abstract language is called a process-aware goal
description language that is based upon a set of XML-based
schema structures consisting of procedural combinations ofatomic role types[8] and compound role types[8], such as DAG(Directed Acyclic Graph) role, IF role, FOR role, WHILE role,and ALT (alternative) role.
II. P ROCESS -AWARE I OT C OMMUNITIESIn this subsection, we introduce a conceptual definition ofprocess-aware communities on the Internet of Things community computing environment. The IoT community computing
environment is a computerized situation and society where agroup of Things, including smart objects like devices, sensors,
actuators, and even people, is connected each other throughthe Internet, and a partial group of the members is organized
into a collaborative community (statically, dynamically, or
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

967
2

-

-

Fig. 1.

A Conceptual Architecture of the Process-Aware IoT Community Computing Environment

autonomously) to accomplish it’s goal[9]. The community
is broken up and rejoin its members into the corresponding
society after completing the goal. In this section, we simply
introduce how to abstract the goal of the process-aware collaboration community to be modeled, deployed, and enacted
over a process-aware IoT community computing environment.
Fig. 1 illustrates a conceptual architecture for the processaware IoT community computing environment[10] that isarranged by physical IoT society, virtual IoT society, and agroup of process-aware communities that can be statically,
dynamically or autonomously formed out of smart-objects inthe virtual IoT society. As shown in the conceptual architecture, the virtual IoT society spawns a series of collaborative
communities, and the role of each community is to perform its
own stepwise-activities described by a process-aware goal. The
previous research of the authors proposed the process-driven
IoT community computing model [1] as a formal description
methodology for abstracting the concept3 of process-aware
goals in the IoT community computing environment. As thenext step of the research, we concretize the concept and themodel [1] via a process-aware goal description language with
its XML-based schema.
III. XML S CHEMA OF THE P ROCESS -AWARE G OAL
D ESCRIPTION L ANGUAGE
Based upon the process-aware IoT community computing
model, we devise an XML-based description language designated for abstracting process-aware goals of the corresponding
Internet of Things communities. The major constructs of theabstract language consist of activities, sourcing and sinking
data repositories, and community-members. The activity construct is concretized by two types of roles: atomic role typeand compound role type.
3 Note that the process-aware goal is defined by a predefined or intended setof tasks or roles, called activities, and their temporal ordering of executions.
A process-aware IoT-community computing system helps to organize, control,and execute process-aware IoT-communities by defining their process-aware
goals that are represented by the process-aware IoT-community computing
model proposed in [1].

Process-Aware Goal

Metadata (name, domain, version, author, etc.)

Process-Aware Sub-Goal,, (Process-Aware Goal Body
Atomic Activity

name, activity type

,, (
,,)

Compound Activity

name

,, (
-

-, . -

.

,,)

,,)

Fig. 2. The XML Schema Skeleton of the Process-Aware Goal Description
Language

Fig. 2 shows the XML-based schema skeleton that is madeup of four partitions with the primitive tag types—Metadata,
data-INs, data-OUTs, and BODY—to build the textual representation of a process-aware goal. Assume that the accomplishment of a process-aware goal implies to perform all of theassociated roles of the corresponding IoT-based collaborative
community. In particular, the subGoal-BODY portion of aprocess-aware goal description language is composed of abundle of procedural activities, each of which abstracts either
atomic role or compound role. The detailed schema of theatomic roles type and the compound roles type are describedin the following subsections. In terms of the notational XML
syntax[8], the following are used to simplify the descriptive
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

representations of the process-aware goals:• Elements and attributes of XML tags may have one of
the wildcard characters as follows: ? (0 or 1), * (0 ormore), + (1 or more), # (exclusive-existence)• Elements ending with “. . .” (e.g. <element . . . /> or<element . . . >) indicate that elements or attributes
irrelevant to the context are being omitted.
A. The Activity Construct
The process-aware goal description language is used forthe user to compose a process-aware goal by defining an
abstract model as well as a concrete model. The abstract
model is defined by a procedural combination of the activity
constructs, while the concrete model is defined by associating
either an atomic role or a compound role with each of thecorresponding activity constructs. Eventually, those definedprocess-aware goals are deployed and enacted on the Internetof Things community computing environment.
<activity name=“Name” type=“Type” from=“↵1 ” to=“↵2 ”><data-INs><data-IN name=“Name” type=“Type”><value> Constants </value>?
</data-IN>*</data-INs><atomic-ROLE name=“Name” type=“Type”/>#<compound-ROLE name=“Name” type=“Type”/>#<data-OUTs><data-OUT name=“Name” type=“Type”/>*</data-OUTs></activity>968
3

represented by the tag elements of ROLE with the exclusiveexistence wildcard character, #.
B. The Atomic Role TypeIn the process-aware goal description language, theatomic role type is built by three tag elements: Metadata,
data-INs, and data-OUTs. The metadata has a nameproperty and a type property. The name property is anunique identifier of a corresponding atomic role; the typeproperty characterizes its corresponding atomic role as aconcrete service provided a specific task chosen from agroup of tasks. Each task in the group is a concrete smartobject having a same functionality with probably different
performances (behaviors, QoS characteristics, costs, etc.),
and being implemented and deployed in the virtual societyof the Internet of Things community computing environment.
Assume that the affiliative information between atomicroles and concrete smart-objects is managed by the worklist
registry component of a process-aware goal IoT community
enactment system. Fig. 4 illustrates the XML scheme of theatomic role type with its XML language Skeleton.
<atomic-ROLE name=“Name” type=“Type”><data-INs><data-IN name“Name” type=“Type” source=“Source”? ><value> Constants </value>?
</data-IN>*</data-INs><data-OUTs><data-OUT name=“Name” type=“Type” saveto=“Loc”? />*</data-OUTs></atomic-ROLE>

Activity Scheme

Atomic Role Scheme<Activity>
name
type
from
to
<data-INs><data-IN>
nametype<value><atomic-ROLE>
name
type
<compound-ROLE>
name
type
<data-OUTs><data-OUT>
name
type

Fig. 3.

The XML Scheme of the Activity Construct

Fig. 3 shows a graphical structure of the XML-based activity
construct and its XML scheme with tag elements, such as
data-INs, data-OUTs, atomic-ROLE, compound-ROLE,
and attributes, such as name, type, from, to. A corresponding
activity is exclusively concretized by associating with either
an atomic role or a compound role. This association rule is<atomic-ROLE>
name
type
<data-INs><data-IN>
nametypesource<value><data-OUTs><data-OUT>
name
type
savetoFig. 4.

The XML Scheme of the Atomic Role Type

C. The Compound Role TypeThe process-aware goal description language is able
to provide a rich set of compound role types. That is, thecompound role type supports making a variety of collaborative
formations in a group of roles, which is sub-classified into
mesh-type, loop-type, and alternative-type formations. The
mesh-type collaborative formation is represented by DAG(Directed Acyclic Graph) compound role; the loop-type
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

collaborative formation is defined by FOR and WHILE
compound roles; and the alternative collaborative formation
is formatted by IF and ALT (Alternative) compound roles. In
this section, we design the detailed XML schema and formatsof those atomic role and compound role types after defining
the XML scheme of the activity construct. In particular, we
introduce only the DAG compound role type due to the page
limitation. Fig. 5 illustrates the XML scheme of the DAG
compound role type with its XML language Skeleton. A
DAG compound role type is a commonly used type to express
control flow dependencies among the affiliated atomic roles
in a corresponding compound role. The possible formationof the control flow dependencies is classified into sequential
and parallel dependencies inside of a DAG compound role.
<dag-ROLE name=“Name” type=“Type”><data-INs><data-IN name“Name” type=“Type” source=“Source”? ><value> Constants </value>?
</data-IN>*</data-INs><atomic-ROLEs><atomic-ROLE name“Name” type=“Type” from=“Role” >+</atomic-ROLEs><data-OUTs><data-OUT name=“Name” type=“Type” saveto=“Loc”? />*</data-OUTs></dag-ROLE>DAG Compound Role Scheme<dag-ROLE>
name
type
<data-INs><data-IN>
nametypesource<value><atomic-ROLEs><atomic-ROLE>
name
type
from
<data-OUTs><data-OUT>
name
type
savetoFig. 5.

The XML Scheme of the DAG Compound Role Type

IV. R ELATED W ORKS
So far, there exist several pervasive (or ubiquitous) community computing models[5][11] such as the context-aware
community collaboration model[5], the society collaboration
model[4][11], and the member collaboration model[6][11]for building an IoT-based pervasive community computing
environment[1][10][12]. The context-aware community collaboration model[5] supports a community goal, which canbe described by the goal description language proposed inthis paper, through a series of situations from the initiating969
4

situation to the terminating situation in the corresponding
community. Each of the member-objects fulfills its assignedrole in a specific situation in the community. It is assumed
that the defined community goal is accomplished if all of
the member-objects achieve their assigned goals. Additionally, context-aware community collaboration models as theprocess-aware Internet of Things[10] can be classified into thethree levels[11] of intellectualized community,i.e., the simple
collaboration model, the dynamic collaboration model, andthe autonomous collaboration model, according to the degreeof intellectualization. These models can be represented by
the process-aware goal description language in this paper.
The process-aware goal description language uses the society
collaboration model[11] to define a group of activities, whichis executable in a pervasive (or ubiquitous) intellectual mobilespace that are built upon the process-aware Internet of Thingsarchitecture and system[10].
V. C ONCLUSION
In this paper, we have proposed an XML-based description
language, which is so-called “process-aware goal description
language,” to specify process-aware goals of the Internetof Things communities. A process-aware goal is abstracted
by a group of activities and their procedural and temporal
combinations. Each activity is reified by either an atomicrole or a compound role. Therefore, we devised a set ofXML-based schema structures for the activity construct andits concrete components: atomic role type and compound role
type. In particular, we described the detailed specification of
the DAG (Directed Acyclic Graph) compound role type onlyin this paper. As future works, we have plans to extend the
XML schema so as to express all the remaining compoundrole types, and implement a process-aware goal modelingsystem supporting the process-aware goal description language
proposed in this paper.
R EFERENCES[1] H. Ahn and K. P. Kim, “A process-driven iot-community computing
model,” ICIC Express Letters, vol. 8, no. 12, pp. 483–488, 2013.
[2] “Itu internet reports 2005: The internet of things,” Executive Summary,International Telecommunication Union, 2005.
[3] Y. Jung and M. Kim, “Situation-aware community computing modelfor developing dynamic ubiquitous computing systems,” Journal of
Universal Computer Science, vol. 16, no. 15, pp. 2139–2174, 2010.
[4] e. a. Minkyo In, “Web of things (wot) standardization status,” TTA
Journal, vol. 138, pp. 79–84, 2011.
[5] e. a. Sangkeun You, “Smart mobile services - m2m technology and its
standardization status,” Telecommunications Trends Analysis, vol. 26,
no. 2, pp. 50–60, 2011.
[6] K. Kim and C. A. Ellis, Section II / Chapter VII. An ICN-based Workflow
Model and Its Advances, Handbook of Research on BP Modeling.
Hershey, Pennsylvania, USA: IGI Global, ISR, 2009.
[7] C. A. Ellis, J. Wainer, A. Rembert, and K. Kim, “Investigations on
stochastic information control nets,” Information Sciences, vol. 194, pp.
120–137, 2012.
[8] J. Qin and T. Fahringer, SCIENTIFIC WORKFLOWS, Programming,
Optimization, and Synthesis with ASKALON and AWDL. Springer,
2012.
[9] M. Skerlavaj, V. Dimovski, and K. C. Desouza, “Patterns and structuresof intra-organizational learning networks within a knowledgeintensive
organization,” Journal of Information Technology, vol. 25, no. 2, pp.
189–204, Jan. 2010.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017970
5[10] M. Kim, H. Ahn, and K. P. Kim, “Process-aware internet of things: A
conceptual extension of the internet of things framework and architecture,” KSII Transactions on Internet and Information Systems, vol. 10,
no. 8, pp. 4008–4022, Aug. 2016.
[11] W. Cho, u-SERVICE DESIGN: Ubiquitous Computing Experts Series.
Jinhan MB Publishing Co., 2009.
[12] D. Xu, W. H. Li, and S. Li, “Internet of things in industries: a survey,”
IEEE Transactions on Industrial Informatics, vol. 10, no. 84, pp. 2233–
2243, Jan. 2014.

Hyunah Kim Dr. Kim is an adjunctive professor
and a faculty member of the collaboration technology research laboratory in the department of computer science at Kyonggi University, South Korea.
She received her B.S. degree in computer sciencefrom Korea Nazarene University in 2001. Also,
she received her M.S. and Ph.D. degrees in computer science from Kyonggi University in 2003 and2009, respectively. She has been on the operational
committees of several domestic and international
conferences including KSII, AP-IST, ICONI, andICACT. Her research interests include workflow systems, SCORM-based
e-Learning process models, BPM, BPI, ACM, workflow-supported social
networks discovery and analysis, and process-aware Internet of Things.

Kwanghoon Pio Kim Dr. Kim is a full professorof computer science department and the founder andsupervisor of the collaboration technology researchlaboratory at Kyonggi University, South Korea. He
received B.S. degree in computer science from Kyonggi University in 1984. And he received M.S. degree in computer science from Chungang University
in 1986. He also received his M.S. and Ph.D. degrees
from the computer science department at Universityof Colorado Boulder, in 1994 and 1998, respectively.
He had worked as researcher and developer at Aztek
Engineering, American Educational Products Inc., and IBM in USA, as well
as at Electronics and Telecommunications Research Institute (ETRI) in South
Korea. In present, he is a vice-chair of the BPM Korea Forum. He has beenin charge of a country-chair (Korea) and ERC vice-chair of the Workflow
Management Coalition. He has also been on the editorial board of the journalof KSII, and the committee member of the several conferences and workshops.
His research interests include groupware, workflow systems, BPM, adaptive case management (ACM), CSCW, collaboration theory, Grid/P2P/Cloud
distributed workflow systems, process warehousing and mining, workflowsupported social networks discovery and analysis, process-aware information
systems, data intensive workflows, and process-aware Internet of Things.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

971

Virtual Antenna Mapping MIMO Techniques in a
Massive MIMO Test-bed for Backward Compatible
LTE Mobile Systems
Seok Ho Won, Saeyoung Cho, Jaewook Shin
Mobile Communication Department,
Electronics and Telecommunications Research Institute, Daejon, Korea
shwon@etri.re.kr, csy1009@etri.re.kr, jwshin@etri.re.kr

Abstract—This paper proposes a virtual antenna mapping
method for backward compatible massive or large-scale antenna
multiple input multiple output (MIMO) base stations that provide
communication services for legacy user equipment (UE) that canrecognize only two or four base station antennas. The proposed
method adopts and improves the omnidirectional beamformingthat has been pioneered in previous works with proposing
new method of determining antenna array coefficients throughshifting the discrete Fourier transform (DFT) basis vectors for
Zadoff-Chu (ZC) sequences. In the proposed method, the numberof the parameters to be optimized is only two although thenumber of transmit antennas is hundreds or more (e.g., 500
antennas was proved in the paper as an example). Moreover,with the independent properties of the shifted versions of ZC
sequences, this paper proves the fact that the coefficient vectors
consisting virtual transmit antennas are independent when thechannel gains work as the coefficients of them. This characteristic
gives diversity with the pre-codes because two pre-code vectors
must independent which means their linear combination with
the non-zero channel gains or coefficients cannot be zero. The
computer simulation results provide four important findings; themost important is that the actual number of virtually mapped
physical antennas is inversely proportional to the transmit power
per antenna.
keyword—massive MIMO, virtual antenna techniques, omnidirectional beamforming, transmit diversity, precoding techniques

I. I NTRODUCTION
long with small cell technology, massive multiple input
multiple output (MIMO) is a promising technology forincreasing the capacity to fulfil the potential traffic increases
in next-generation mobile communication systems [1]. Whilesmall cell technology can increase capacity through reducing
the cell radius, massive MIMO adopts a large scale for theA

Manuscript received November 9, 2016. This work was supported by
Institute for Information & communications Technology Promotion (IITP)
grant funded by the Korea government (MSIP) (No. R0101-15-244, Development of 5G Mobile Communication Technologies for Hyper-connected smart
services) and a follow-up of the invited journal to the accepted & presented
the out-standing paper of the 17th International Conference on Advance
Communication Technology (ICACT2015).
Seok Ho Won is with the Department of Wireless Transmission Research,
ETRI (Electronics and Telecommunications Research Institute), Korea (Corresponding author, phone: +82-42-860-5449 e-mail: shwon@etri.re.kr.)
S. Cho and J. Shin are with Mobile Giga Transmission Research, ETRI,
Korea (e-mail: csy1009@etri.re.kr; jwshin@etri.re.kr)

transmit antennas. In small cell technology, in order to reducethe cell radius, the macro base station extends its antennas
with some radio parts called ‘remote radio heads’ (RRHs)
through connecting them with optical fibers, which results inhigh installation costs [1]. In contrast, by collocating many antennas to save the connecting costs massive MIMO technology
has a relatively cheaper installation cost than the small cell
technology [1]. Furthermore, massive MIMO has many radio
frequency (RF) chains and antennas; thus, the transmission
power per antenna decreases given that total transmission
power is constant, which also results in decreasing RF part
costs. Moreover, theoretically, massive MIMO technology caneliminate fast fading while increasing throughput and terminal
numbers independent of the cell size [1-3]. Therefore, we designed massive MIMO test-bed as the pilot for next-generation
mobile systems and future LTE-A system [4].
For a massive MIMO base station, dozens of or even
hundreds of transmitting antennas are essential to give theeffects described above. However, currently the LTE-A specification (e.g., Release-10 or later versions) allows eight transmit
antennas and is expected to have more transmit antennas in
the future [5, 6]. Therefore, just for analogy purpose, we first
restrict the number of transmit antennas to eight in the paper
although the massive-MIMO test-bed will have much moretransmit antennas. However, with the simple example it isshown the possibility of extending them to hundreds by using
the proposed method (e.g., 500 antennas in Figure 3b in thispaper).
As for the backward compatibility, LTE-A eNode-B (i.e.,base station) that has eight (or more) transmit antennas should
provide service to legacy LTE (e.g., Release-8, 9) UEs that canrecognize only two or four transmission antennas of eNode-B
without notifying any information through additional control
channels or signaling. Therefore, this paper introduces virtual
antenna technique that can improve UE performance without
increasing complexity of UE or eNode-B.
The proposed virtual antenna technique or backward compatible MIMO technique that converts eight physical transmit
antenna to two or four logical antennas uses pre-codes thatenable UE to use wireless channel’s degrees of freedom anddiversity efficiently by having robustness for high spatial
correlations. In order to give diversity with the pre-codes, two
pre-code vectors must independent which means their linear
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017combination with the non-zero channel gains or coefficientscannot be zero. The generation method for pre-coding matrix
is similar but not same with cyclic delay diversity (CDD)
that current LTE specification defines. The validation of theperformance is proved by computer simulation because in thedesign stage, computer simulations can also be useful for good
designing while test-beds are used in lab tests as a replacement
for field tests.
In the following chapter, we introduce proposed virtual
antenna mapping as the backward compatible MIMO techniques in massive MIMO test-bed with key technique ofomnidirectional beamforming (OB), and we discuss about thetransmitter and receiver structure for legacy user equipment(UE). Next, we evaluate the proposed method with someassumption for simple analysis. After evaluation, we conclude
the paper with some important findings.
II. P ROPOSED V IRTUAL A NTENNA M APPINGThe proposed virtual antenna mapping that converts eightphysical transmit antenna to two or four logical antennas isbased on the OB as shown in Figure 1. With a OB beam, we
can have two or four orthogonal beams by phase shifting of
the beam precoding vector of the OB beam as we will show
later.
A. Omnidirectional Beamforming (OB)The simplest way to make OB beam is sending transmit
signal through only one antenna but this make the all transmit
signal power converged into only one antenna and all powersignals flow the radio frequency (RF) circuits of that antenna
path only. As a result, high cost RF circuits like high power
amplifier (HPA) must be needed [7]. Therefore, we can havefollowing two requirements (RQs) for OB:• RQ1. All radiating power for each transmit antenna in
the MIMO BS must be equal or nearly equal.
• RQ 2. The gains of beam pattern have the least variations
within the angle range of the service sector (e.g., 120
degree for 3 sector cell).
To find OB with above RQs, pioneering technique called
random beamforming (RBF) has been used [7-9]. We make972

RBF fancier technique by approaching the problem for satisfying above RQs with more systematic way of using the duality
property of the digital Fourier transform (DFT) instead ofusing randomness which is described in next few paragraphs.
As the initial model, planer array antennas are commonlyused and practical model can be develop based on this simple
starting model. Therefore, for a simple analysis this paper
assumes the system of using a uniform linear array (ULA)
antenna and the beamforming array vector is given as follows
[10]:
iT
h
2π
2π
a (θ) = 1, e−j λ d sin(θ) , . . . , e−j λ (M −1)d sin(θ)(1)
where λ is the RF wave length, d is the distance between
two adjacent antenna elements, θ is the angle variable representing the direction of the beam pattern generated by thebeam steering vector. Note that the analysis in reference [11]uses φ the angle of incidence of the line-of-sight onto thetransmit antenna array instead of using θ (i.e., θ= π2 −φ) andso uses directional cosine (i.e., Ω:=cos(φ)) constructing theunit spatial signature for the angular domain analysis [11].
Suppose that w is the weight coefficient vector with complex
number entries {w (m) , m = 0, 1, . . . , M − 1} for theantenna elements where M is the total number of base station
antennas. Then the resulting beam pattern transmitted by the
total base station transmit antennas can be expressed as follows[10]:

G (θ) = wH · a(θ) =

M
−1
X

2π

w∗ (m) · e−j λ mdsin(θ) .

(2)

m=0

Let’s define a function Ω (θ) := 2π
λ dsin (θ) and use thisas the substitution variable. Note that reference [11] defines
directional cosine (e.g., Ω:=cos(φ)) instead of Ω (θ) definedin this paper. Through the substitution, the beam pattern canbe expressed as follows:

G (θ) =orW (Ω (θ)) =W (Ω ) =

N
−1
X

m=0
N
−1
X

w(m) · e−jΩ(θ)mw(m) · e−jΩm .

(3)

m=0

This equation is a discrete-time Fourier transformation(DTFT) that transforms the discrete-time signal w(m) with afinite duration into a continuous and periodic frequency signal
W (Ω ). Therefore, using the inverse DTFT (IDTFT) for (3),we can obtain the sequence elements as follows:Z1w (m) =W (Ω ) · ejΩm dΩ .
(4)
2π 2π
Fig. 1. Virtual antenna mapping concept for backward compatible MIMO techniques

As a result, our problem refers to finding the sequence{w (m) , m = 0, 1, . . . , M − 1} through simply performing
the IDTFT of the continuous beam pattern W (Ω ) that was
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

originally from G (θ) whose variance is minimal. Therefore,
the sequence must be satisfied regardless of the domains that itis transformed to using (3) and (4) (i.e. DTFT and IDTFT), andthe amplitude or power of each element of the sequence must
be constant. The typical sequence with those characteristics is
Zadoff-Chu (ZC) sequence and it is expressed as follows [12]:s (m) = e−jπµm (m+hM i2 )/M ,(5)

where µ is an integer less than and relative coprime to M
and the notation hxiy denotes the remainder of x modulo y.
Through inserting the circular shift parameter k into (5), we
can obtain following equation:α (m)transform or its inverse transform of the ZC sequences canchange the total average sequence amplitude and phases but
can not change the amplitudes or powers of the sequence
elements independently by the properties of the sequences as
described in the bottom of the figure. For parameter optimizing
procedures, up to date the most pioneered method has turned
out to be the method of Yang, et. al.’s [7] which we follows
in the point of variance minimizing target. Assume that for
entire section {θ1 ≤ θ ≤ θ2 | θ1 = 0, θ2 = 2π} and2d = λ/2h then thei expected value of |G (θ)| is given as M
2(i.e., E |G (θ)| = M ) [7]. Therefore, by using (2) and (4),the square variance can be calculated as follows (the detailed
solving procedures are given in [7]).

:= s (m + k)=e(−jπµ(hm+kiM ) (hm+k+hM i2 i

M

)/M

.

(6)

With (6) and given the total number of base station transmit
antenna elements M , the goal becomes narrowing this tolocate only two parameters of µ and k that can generate theunique circular-shifted ZC sequence with a large number of
elements. Our goal is to make OB in angular domain whichgets discrete coefficients for each antenna element through
IDFT: so we can make resulting beam generated by the antennas in the original angular domain which will be the target
constant amplitude beam again. Therefore, by considering
beam steering vectors as the DFT operators, we choose theweighting sequence whose amplitude remains constant before
and after DFT.

973

σ4 =
=

=

1
2π

Z 2π

1
2π

Z 2π

2

2

(|G (θ)| −M ) dθ
0

(

M
−1
X

2

e

−jψm

·e

−jπm sin(θ)

2−M ) dθ

0
m=0
M
−j
M
−j
M
−1
X X X

cos(ψm+j − ψm − (ψn+j − ψn )).

j=1 m=1 n=1(7)

B. Parameter Optimization Procedure

Yang, et. al. obtained the optimum patterns by taking thepartial derivatives with respect to ψm to reach the minimum
value of σ 4 . However, it is usually too complex to find thesolution especially when the number of variables to find isseveral hundred with the common massive MIMO systemnature.
Instead of using the partial derivative method, this paper
uses relatively simple algorithm called the simplex search

In order to optimize parameter values for the antennacoefficient sequences, we first look at the relationships between antenna elements and beam patterns. Figure 2 shows
the relationship of the sequence with the antenna weights{w (m) , m = 0, 1, .., M }, approximated beam pattern
W (Ω ), and exact beam pattern G(θ) (see upper parts ofthe figure). The antenna weights w(m) are transformed toapproximated beam pattern W (Ω ) by DTFT, and turn further
into exact beam pattern G(θ) by the variable substitution
process (see the upper part of the figure). In the process ofthe variable substitution and given the array length L for thearray antennas, the sine domain or directional cosine domain
1(i.e., Ω domain) has uniform resolution ( 2L
) but the angulardomain (i.e., φ domain) has non-uniform resolution [11]. With
these relationships analysed in this paper, interesting readers
can solve the exercise 7.8 in [11].
Figure 2 also shows the relationship between discrete
Fourier transform (DFT) and DTFT to identify the sampling
errors occurred in the process of converting signals between
digital and continuous domains (see the lower box in the
middle part of the figure). The sampling errors also give
severe effects to increasing the variance of beam pattern andthese errors can be decreased as the M increases, which arefavorable to massive MIMO systems. With these relations, theidea of the proposed method come from the fact that Fourier

Fig. 2. The relationships for the antenna weights w(m),approximated beam pattern W (Ω ), and exact beam patternG(θ). Important error sources of the sequences are also shownin two boxes located in right side.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

method of Lagarias et. al. [13] which is a direct searchmethod that does not use numerical or analytic gradients withexpanding the method to n-dimensional space. While Yang’s
method tried to optimize M variables for the antenna elements,
the proposed method tries to optimize only two variables ofthe parameters µ and k in (6) to make the sequence uniquelydetermined. Therefore, by substituting α(m) in (6) to w(m)for (2) and (3), we can get following equation:974

matrix. Further, suppose the first weighting sequence can be
expressed as follows.
(1)

w0 =

N−3
X

αi vi

(9)

i=2

where ∀i, |αi | = 1 for i-th complex scalar sequence to satisfy
RQ1. In (9), we truncate the sequence or use only i=2,. . . ,N3 instead of using all N elements of the sequence which
M
−j
M
−j
M
−1
means just only partial sequence elements can be used for the
X X X
σ4 =
cos(Ψm+j+k −Ψm+k −(Ψn+j+k −Ψn+k )) weights. Next, to fulfil RQ2 we can get the first OB weight(1)
j=1 m=1 n=1
w0 by choosing and truncating the weighting sequence which(8) satisfies (10) as follows.
where Ψx = −πµ(hxiM )(h(x + hM i2 )iM )/M .
While the beam pattern square variances σ 4 in (7) and (8)look alike, the functions Ψx and ψm are defined differently.
More precisely, the phase function of the sequence in (8) is
more general form and that in (7) is specific form.
Figure 3a shows the polar plot for the foundoptimum antenna weights for 32 antenna elements{w∗ (m), m= 0, 1,. . ., 31}. These sequence is generated
by the circular shifted Zadoff-Chu sequence rule with the
optimized parameters of {µ∗ = 1, k ∗ = 1} which are foundedby using the simplex search method with the initial value of{µo = 1, ko = 1} also for the proposed method. The computed
values for them are {µ∗ = 1.0157, k ∗ = 0.7366} with theproposed method (see Figure 4). However µ must be therelative coprime to the number 32 and k must be integer: so
we choose them for the closest numbers that satisfy these
conditions for the sequence. Figure 3b shows the same casewith the Figure 3a except for the number of transmit antenna
elements of 500.
The parameters for this sequence do not need to be foundbut predicted by the fact shown in the Figure 4 which shows
the change trends for two optimized parameters {µ∗ , k ∗ } forthe circular shifted Zadoff-Chu sequence as the number oftransmit antenna increases. In the figure, they are converged
into special value but we choose them for the closest numbersthat satisfy these conditions for the sequence (i.e., relative
coprime and integer).
To compare practical usefulness for the conventional method
and proposed method in this paper, suppose the massive
MIMO system that has 100 transmission antenna elements.
Then, the number of variables to find in the proposed method
decreases 50 times (i.e., 100:2) in these example procedures.
Moreover, we found in the proposed method the optimum
values of the two parameters of µ and k changed withnearly negligible values as shown in above. Therefore, theproposed method can give good approximation information
to the future analysis for the massive MIMO system withtheoretically infinitive number of transmit antenna elementsfor (i.e., M → ∞).
C. Virtual Antenna Mapping
Let DFT matrix be denoted by v := [v1 , v2 , · · · , vN ] and{vi , i = 1, 2, . . . , N } are the column vector set for the DFTsαi∗ = argmin{αi , i=m,...,n}1
θ2 − θ1

Z θ2

2

2

2

(|G (θ)| −E[|G (θ)| ]) dθθ1(10)
where G(θ) is given by (2) and E[·] denotes the expect value
operator.
With an OB beam, eNode-B can map one virtual antenna
port to multiple physical antennas (e.g., 8 transmit power

(a)

(b)

Fig. 3. Polar plot for the found optimum antenna weightsfor { (a) 32, (b) 500 } antenna elements {w∗ (m),m= 0, 1,. . ., 31 or 499} by the proposed method. Some values are overlapped.
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

975

With the same manner, the two physical broadcasting channel(PBCH) symbols for transmit diversity (e.g., Alamouti coded
two symbols) can be mapped to OB beam 0 and 1 respectively.
TABLE I
GENERATED BEAM WEIGHTING SEQUENCES(1)

Beam 0 (e.g., w0 )0.10406 + j*(-0.24340395)
0.21346 + j*(-0.16386)
0.33082 + j*(+0.15179)-0.16340 + j*(+0.38933)-0.01444 + j*(-0.31956)-0.08058 + j*(+0.40658)
0.40698 + j*(+0.03329)
0.20309 + j*(-0.25417)(2)

Beam 1 (e.g., w0 )-0.10406 + j*(+0.243403)
0.21346 + j*(-0.163868)-0.33082 + j*(-0.151797)-0.16340 + j*(+0.389337)
0.01444 + j*(+0.319562)-0.08058 + j*(+0.406583)-0.40698 + j*(-0.033293)
0.20309 + j*(-0.254177)

D. Base Station’s Transmitter Structure for Legacy UEs
Fig. 4. Optimum circular shifted Zadoff-Chu sequence parameter value change as the number of transmit antenna elements
increase for the proposed method

amp and antennas). To get one more virtual antenna port fordiversity transmission, we need to get the second OB beam
(2)
(1)
and this is given by w0 as the circular shifted version of w0
as follows.
(2)

(1)

(1)

(1)

(1)

(1)

w0 = [ . . . , wN−3 , wN−2 , wN−1 , w0 , w1 , . . . ](11)With the same manner, we can get third and fourth OB beams
for four virtual antenna ports. However, we consider two OB
beams for two virtual antenna ports and more than two OB
beam case is left for future study.
In order to give diversity between two virtual transmit anten(1)(2)
nas, the coeficient vectors w0 and w0 must be independentwhen the channel gains work as the coefficients of them.
Therefore, theorem 1 must be justified.
(1)

(2)

Theorem 1. Two vectors w0 and w0 generated by she
shifted versions of ZC sequence are independent.
(1)With the method described in previous chapter, we know
how to get virtual antenna mapping. To find most efficient
way to use the mapping for base station’s transmitter, we
now consider two virtual antenna ports for the transmitterwith eight physical antennas with following mapping cases:
Case 1. Each port maps to two physical antennas withturning off the rest of the antenna PAs• Case 2. Each port maps to two identical OB beams• Case 3. Each port maps to two orthogonal OB beams
with open loop space time block code (STBC)• Case 4. Each port maps to two orthogonal OB beams
with closed loop feedback for codebook index•

Case 1 violates RQ1. and Case 2 has no diversity gain for two
identical beams mapped by two virtual antenna ports, which
will be proved at the evaluation chapter. On the other hand,
Case 3 has diversity gain for two orthogonal beams mappedby two virtual antenna ports and used for STBC encoding
as shown in Figure 5. This fact will be also proved at theevaluation chapter. For Case 4 we consider the transmitter andreceiver mechanism shown in Figure 6. For the Case 4, weused LTE codebook index as shown in Table II [5].

(2)Proof. Suppose that w0 and w0 are dependent. Then there(1)(2)
exits non-zero coefficients c1 , c2 such that c1 w0 + c2 w0 =(1)
(2)
0. However, w0 and w0 are ZC sequences and with the autocorrelation property they are orthogonal each other and cannot
be represented by linear combination of the other vector.
With the manner described above, we can get sequences
for beams 0 and 1 for eight physical antennas with power
amplifiers (PAs) and example sequence values are shownin Table I. With the two OB beams of equations (9) and(11), the MIMO BS can map the two antenna ports to these
two beams respectively. As an application example of themethod for the traffic signals of the legacy UEs, cell-specificreference signals (CRSs) for diversity transmission with twoantenna ports can be denoted by CRS0 and CRS1 and they
can be directly mapped to OB beam 0 and 1 respectively.

Fig. 5. Transmitter and receiver structure for Case 3
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017976

The codebook indices zero and one have zero valued
element which means turning off the counterpart virtual
antenna OB beam. The other codebook indices except those
two show power distribute equally for the two beams whichmeans equally combining of the two beams. The element’s
negative sign and imaginary component are intended to workfor the signal phase change; so we can easily predict Case
3 and 4 will show the double (3 dB) performance difference
which proved by the computer simulation results shown at
the evaluation chapter.

TABLE IICODEBOOK FOR CASE 4

Id
Vl

 0 
1
0

 1 
0
1

 2 
s
s

 3 
s
s

 4 
s
js

 5 
s
js

Fig. 7. Gains and their sum of the two OB beams∗s = √12 , s = −sIII. E VALUATION
Proposed virtual mapping method for backward compatible
MIMO is evaluated by the computer simulations.
A. Computer Simulation Setup and Method
The computer simulation parameters are set as shown in
Table III. Transmitters at eNode-B and UE receiver are set
according to the Cases 1 - 4 described in above section.
TABLE IIISIMULATION PARAMETERS

Parameter
Antennaconfiguration
Modulation
Channel model

Packet length
CSI
feedback
period(for Case4)

Values8 elements, ULA, d =λ/2
QPSK
Random fading (not change for one
frame duration and uncorrelated
between antenna paths)
4x130 bits/packet(260 symbols/packet or QPSK)
1 frameFig. 6. Transmitter and receiver structure for Case 4.

Fig. 8. Beam power variations with time for two orthogonalbeams

B. Simulation Results and Discussion
Figure 7 shows the gains and their sum of the two draft
OB beams obtained by the equations (9) and (11) that haveexample weighting sequences shown in Table 1. Approximately, each beam 0 and 1 fluctuates with 5 dB as the figure
shows. However, we can get more flat OB beams by tuning
the parameters of the equations but we leave this future study.
Figure 8 shows beam power variations with time for two
orthogonal beams. In the figure, the power difference between
the two beams shows distinctively and so gives diversity gain
which can be seen in the Figure 9.
Figure 9 shows uncoded bit error rate (BER) performanceversus transmit power per antenna for QPSK (a) and 16QAM(b). These figures as the simulation results give us following
four findings:i) Approximately, the uncoded BER results for the Case 3
and Case 4 are 6 dB (4 times) more than that of Case
1.
ii) Approximately, the uncoded BER results for the Case 4
are 3 dB (2 times) more than that of Case 3.
iii) The curve for Case 1 is steeper than that for Case 2.
iv) The uncoded BER performance results for QPSK and
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

(a)

(b)

Fig. 9. Uncoded bit error rate performance versus transmit
power per antennna: (a) QPSK, (b) 16QAM

977

that can recognize only two or four antennas. The proposed
method adopts the omnidirectional beamforming that satisfies
the two research questions defined in this paper, and it provides
a more systematic approach than previous pioneering works,
as discussed in the introduction. The proposed method is
to determine antenna array coefficients through shifting thediscrete Fourier transform (DFT) basis vectors for ZadoffChu (ZC) sequences. With this method, this paper showed
the possibility of extending the number of transmit antenna
to hundreds (e.g., 500 antennas in Figure 3b in this paper)with only two parameters to be optimized. Moreover, with
the independent properties of the shifted versions of ZC
sequences, this paper proved the fact that the coefficient
vectors consisting virtual transmit antennas are independentwhen the channel gains work as the coefficients of them. This
characteristic enables the communication link having diversity
with the two or more virtual transmit antennas. That is, inorder to give diversity with the pre-codes, two pre-code vectors
must independent which means their linear combination with
the non-zero channel gains or coefficients cannot be zero.
This paper also provides four types of transmitting structuresat the base station in order to demonstrate the performanceof the proposed virtual antenna mapping, and it derives four
findings through a simple computer simulation (see Chapter
III-B). Among these findings, the most important is that a fourtimes (6 dB) higher uncoded BER performance was achieved
when mapping eight physical antennas compared with that of
mapping two physical antennas. Therefore, the actual numberof mapped physical antennas is inversely proportional to thetransmit power per antenna.
R EFERENCES

16QAM show the same trend but the power differenceshows 6 dB approximately.
The reason of i) is that with the same total powers of
all transmitting physical antenna for each case the power
per antenna is different. That is, Case 3 and Case 4 useeight transmit antennas while Case 1 uses two which result
is four times (6 dB) power difference. The reason for ii)
is that codebook index feedback of the Case 4 results incombining diversity gain and the phase control of the two
beams. For example, the index (Id) 0 and 1 in Table 1 have thevalues (Vl) [1, 0]T and [0, 1]T respectively which mean fulltransmit power allocation for the good transmission antenna
path while no power allocation for the bad. Therefore, theBER performance becomes as double as the open loop transmit
diversity (Case 3). For the fact of iii), Case 2’s two identicalbeams show no diversity gain as Case 1 of STBC encoding
which result in steeper uncoded BER curve. From the iv),
we can predict high order modulation (e.g., 64QAM) canwork with the same trend and can be adopted in the proposed
scheme.
IV. C ONCLUSION
This paper proposed a virtual antenna mapping method forbackward compatible massive MIMO base stations in orderto provide communication services for legacy user equipment[1] A. Chockalingam and B. Rajan, Large MIMO Systems, Cambridge
University Press, 2014.
[2] T. L. Marzetta, “Noncooperative cellular wireless with unlimited numbersof base station antennas,” IEEE Trans. Wireless Commun., vol. 9, pp.
3590-3600, Nov. 2010.
[3] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors,and F. Tufvesson, “Scaling up MIMO: Opportunities and challenges withvery large arrays,” IEEE Signal Processing Mag., 2012.
[4] S. H. Won, S.C. Chae, S. Y. Cho, I. Kim, and S. C. Bang, “Massive MIMO
test-bed design for next-generation long term evolution (LTE) mobile
systems in the frequency division duplex (FDD) mode,” Information
and Communication Technology Convergence (ICTC), 2014 International
Conference on, Busan, Korea, 2014.
[5] 3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); PhysicalChannels and Modulation (Release 11), TS 36.211 v11.2.0 (2013-02),
http://www.3gpp.org/ftp/Specs/archive/36 series/36.211/36211-b20.zip[6] 3GPP, “Study on Elevation Beamforming/Full-Dimension (FD)MIMO for LTE,” technical document number RP-141644, 2014.
http://www.3gpp.org/ftp/tsg ran/TSG RAN/TSGR 65/Docs/RP141644.zip[7] X. Yang, W. Jiang, and B. Vucetic, ”A random beamforming techniquefor omnidirectional coverage in multiple-antenna systems,” IEEE Trans.
Veh. Tech., vol. 62, pp. 1420-1425, Mar. 2013.
[8] J. Wei and X. Yang, ”An enhanced random beamforming scheme for signal broadcasting in multi-antenna systems,” Personal Indoor and Mobile
Radio Communications (PIMRC) 2012.
[9] J. Chung, C. Hwang, K Kim, and Y. Kim, ”A random beamforming
technique in MIMO systems exploiting multiuser diversity,” Sel. Areas inComm., vol.21, issue 5, pp. 848-855, June 2003.
[10] J. Litva and T. Lo, Digital Beamforming in Wireless Communications,
Artech House, 1996.
[11] D. Tse and P. Viswanath, Fundamentals of Wireless Communication,
Cambridge University Press, 2004.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017978[12] C. Chu, ”Polyphase codes with good periodic correlation properties,”
IEEE Trans. Inf. Theory, vol. IT-8, pp. 531-532, 1972.
[13] J. Lagarias, J. Reeds, M. Wright, and P. Wright, ”Convergence propertiesof the Nelder–Mead simplex method in low dimensions,” SIAM Journal
on Optimization, vol. 9.1, pp. 112-147, 1998.
Seok Ho Won received his B.S. degree in clinicalpathology and electrical engineering from Kwangwoon University, Seoul, Rep. of Korea, in 1985 and1990, respectively, and his Ph.D. degree in electricalengineering from Chungnam National University,
Daejeon, Rep. of Korea, in 2002. Since 1985, hehas been a clinical pathologist at Sin-Chon General
Hospital, Gyeonggi-do, Rep. of Korea. Since 1990,
he has been a principal engineer at ETRI, Daejeon,
Rep. of Korea. He was a research faculty member at
Virginia Tech, USA, in 2005. His research interests
include information theory, error correction coding, MIMO, and beamformingwith an emphasis on mobile communications.
Saeyoung Cho received the B.E. and M.E. degrees in department of Electronic and Information Engineering for Chonbuk National University,
Jeonju, Chonbuk, Korea in 2008 and 2010, respectively. Since 2011, he has been with Electronics
and Telecommunications Research Institude, Daejon, Korea, where he is the Research Staff of Wireless transmission research department. His researchinterests include digital communication and MIMOOFDM system.
Jaewook Shin received the M.S. degree from theKyungpook National University, South Korea in1994 and Ph.D. degree in computer science fromthe Chungnam National University, South Korea in2005. He has been working for Electronics andTelecommunications Research Institute (ETRI) as a
researcher since 1994. He was a visiting researcher
at the University of California, Irvine in 2012. He
is currently a director of radio transmission technology section in ETRI. His current research interests
include 5G mobile telecommunication, D2D andM2M.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017979

Experimental Validation of Multipoint
Joint Processing of Range Measurements
via Software-Defined Radio Testbed
Georgy Mashkov*, Eugene Borisov*, Grigorii Fokin** State University of Telecommunication, 22 Prospekt Bolshevikov, St. Petersburg, Russia
MashkovGM@sut.ru, kafedra@sut.ru, grihafokin@gmail.com

Abstract—In this paper we present an algorithm for
multipoint positioning in multilateration (MLAT) radio
navigation system and prove that taking into account redundantprimary estimates such as sum of range measurements between
the object of location and a pair of transceiver stations could
improve single range accuracy estimation after joint processing
with appropriate weighting coefficients. The approach forexperimental evaluation is a testbed including four SDR-basedtransceiver stations, three of which perform primarymeasurements collection and one acts as the object of location
estimation and measurement processing unit. Transceiver
stations are working on National Instruments (NI) Universal
Software Radio Peripheral (USRP) hardware with LabVIEW
software, performing transmission, reception and processing of
signals. Experiment was conducted in mobile scenario with slow
pedestrian object of location movement and revealed position
Mean Square Error (MSE) of one meter for range
measurements with ten trial results accumulation.
Keyword— Analytical model, Distance measurement, Radarsignal processing, Radio navigation, Software radioI.

INTRODUCTION

T

HE field of application of presented results arenavigation, radar and sonar systems which consist ofmultiple transceiver stations gathering measurements, objectof location estimation and measurement processing unit.
Increasing positioning accuracy is an important task forradio navigation systems in civil and military applications toaccurately locate an aircraft or vehicle [1-3].
One of the ways for such flight tracking are MLAT systems
widely developed in several countries. Its construction,———————————————————————
Manuscript received on November 15, 2016. This work was supported by
Ministry of Science and Education of the Russian Federation within thescope of Federal Target Program «Research and development in priority
sectors of scientific and technical complex of Russian Federation in
2014-2020 years» within the project «The development of experimental
model of multi-position fast deployment self-contained radar system groundinfrastructure for landing aircraft on unprepared ground» (unique project
identifier RFMEF160714X0057).
Georgy Mashkov is with the State University of Telecommunication, 22
Prospekt
Bolshevikov,
St.
Petersburg,
Russia(e-mail:
MashkovGM@sut.ru).
Eugene Borisov is with the State University of Telecommunication, 22
Prospekt Bolshevikov, St. Petersburg, Russia (e-mail: kafedra@sut.ru).
Grigorii Fokin is with the State University of Telecommunication, 22
Prospekt Bolshevikov, St. Petersburg, Russia (corresponding author phone:
8 921 985 80 04; e-mail: grihafokin@gmail.com).

navigation signals processing principles, base transceiver
station synchronization approaches, functionality andoperating modes are defined by conditions of practicalapplication such as accuracy demands and location area
topography [4].
Examples of widely spread Automatic Dependent
Surveillance – Broadcast (ADS–B) multilateration radio
navigation systems employed in aircraft navigation arepresented in [5,6].
Multilateration navigation technique is based on TDOA
(Time Difference of Arrival) measurements gathered by anumber of ground base transceiver stations, which are placedin known locations and cover surrounding airspace. Time of
Arrival (TOA) based algorithms and its comparison with
TDOA are presented in [7]. Influence of geometricarrangement of base transceiver stations in conjunction withradio wave propagation conditions on the positioning
accuracy by means of computer simulation and experimental
validation is evaluated in [8]. Dependence of the basetransceiver stations number and its geometric arrangement on
the positioning accuracy is developed in [9]. Joint processingof range measurements in multilateration radio navigation
system is investigated [10] and [11], however these work lackof experimental evaluation in field conditions. Experimental
validation of multipoint joint processing of primary range
measurement via software-defined radio (SDR) testbed is
performed in [12].
The aim of this paper is to evolve the approach of analyticaland experimental evaluation proposed in [12] and validate
joint processing of primary range measurement algorithms
influence on positioning accuracy in field conditions withslow pedestrian object of location movement.
The material in the paper is organized in the following
order. Multipoint radio system under consideration, algorithm
for joint processing and its analytical accuracy performance
evaluation are presented in the second part. Developed
software-defined radio MLAT, including experimental
scenario and its accuracy performance evaluation results arepresented in the third part. Finally, we draw the conclusions in
the fourth part.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017II.

ANALYTICAL RESULTS ON MULTIPOINT JOINTPROCESSING OF RANGE MEASUREMENTS

T

A. MLAT with Joint Processing of Range Measurements
Operating Principle
Multipoint joint processing radio system underconsideration operates in the following way. Transceiver
stations transmit unique signals based on pseudorandombinary sequence and then receive their retransmitted copies
from object of location and thus gather time of arrival (TOA).
Object of location estimation is an active reflector which
retransmits unique signals. Finally measurement processing
unit gathers TOA measurements from transceiver stations andderives range estimates between transceiver stations andobject of location and after that performs joint processing ofderived ranges to locate the object in a three-dimensional
space based on known transceiver stations reference
positions.
B. Joint Processing of Range Measurements Problem
Statement
In general case MLAT joint processing of range
measurements problem statement is formulated as follows.
We assume that it includes N transceiver stations forgathering of measurements and so we get N possible range
measurements between transceiver stations and object ofwhere H  R1 , R 2 ,..., R N , R 12 , R  21 , R 13 ,..., R N  – row
vector of range and sum of range measurements of size
1 N 2 ; A – matrix of coefficients of size N2  N , withelements equal to one for available in (1) range measurement
and zero otherwise; S   R1 , R 2 ,..., R N  – row vector of
unknown range estimates of size 1 N .
Solving (2) with nonlinear least squares estimation theory
methods [11] we get the following solution:Trange measurements denoted by R 12 , R  21 ,...R N N1 .
Algorithm for joint processing of range measurements waspresented in prior work [12] that's why here we only rewrite
main equations and notations.
It is a well-known fact, that system is redundant when N  3[10], that’s why approach used for independent measurements
acquisition enables us to form system of N2 linear equations
which is substantially redundant:

1
S   AT ΛW1A  AT ΛW1  H, 







where W – square matrix of primary range measurements
accuracy of size N 2  N 2 , with range and sum of range
2
primary measurement error variance  R1
,  R2 2 ,...,  R2N as
diagonal elements, and possible correlations between them as
non-diagonal elements:
2
 R1

2
 R1

W





location denoted by R1 , R 2 ,...R N and N  N  1 sum ofR1  1  R1  0  R 2  0  R 3  ...  0  R N ,R 2  0  R1  1  R 2  0  R 3  ...  0  R N ,R1  0  R1  0  R 2  0  R 3  ...  1 R N , R 12  1  R1  1  R 2  0  R 3  ...  0  R N , R  21  1  R1  1  R 2  0  R 3  ...  0  R N ,R 13  1  R1  0  R 2  1  R 3  ...  0  R N ,R  31  1  R1  0  R 2  1  R 3  ...  0  R N ,R N  1  R1  0  R 2  0  R 3  ...  1  R N .

980



 


 R2 2 
N 



Λ – diagonal matrix of coefficients j of size N  N
22with diagonal elements equal to one for available in (1) range
measurement and zero otherwise:



j 0
0 j
Λ
0 0

0 0

0
0

0
0 



j



Geometric arrangement of MLAT under consideration isdepicted in Fig. 1.
Multipoint range measurement radio system depicted inFig. 1 has 3 spatially distributed transceiver stations withknown reference positions in a three-dimensional space x1 , y1 , h1  ,  x 2 , y2 , h 2  ,  x 3 , y3 , h 3  for gathering of rangemeasurements R1 , R 2 , R 3 between them and object oflocation.

In matrix form system of N2 linear equations (1) can be
represented as H  AS,          
Fig. 1. MLAT radio navigation system with 3 transceiver stations

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

Developing an approach in [12] and taking into accountrange and sum of range primary measurement error varianceequalto R21   R2 2   R2 and  R212   R2 21   R2
respectively, we can get following analytical solutions forunknown range estimates:
R1  R4  6 R2  R2 

R1 

2R 2 R2  R2 2   8     2   8   R  4    
2R  



2



8



2



    8    
R  4    R  4     2   8     2    8   R  4    R  4     2   8     2   8   R  4    , 2   8   
2
R

2
R

2
R

3

2
R

2
R

2
R

2
R
2
R

2
R

 21

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

 23

2
R

2
R

2
R

2
R

2
R

2
R

2
R

13

2
R

2
R

2
R

2
R

2
R

R 2  R4  6 R2  R2 



2
R

2
R

3

2
R

2
R

2
R

2
R
2
R

2
R

 21

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

13

4
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

3

4
R

2
R

2
R

2
R

2
R

 21

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

12

2
R

4
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R

2
R



4
R

2
R

2
R

2
R

2
R

 23

2
R

2
R

2
R

2
R

 
2 R4  R2

4 2
2 R R6 R4  R2   R2  R4 

Replacing range and sum of range primary measurement
error variance in (6) – (8) by range estimate variancescomputed beforehand, we can get optimal estimation
algorithm for range estimation in the case of unknown error
variances.
It can be shown that
 3 1 111lim  AT W 1 A    1 3 1  R2  
 R 
8
 1 1 3 

1 0 0 
1lim  A T W 1 A   0 1 0   R2 
 R 
0 0 1 lim  AT W 1 A  

 7 2 2 
1 
2 7 2   R2 
27 
 2 2 7 

lim  AT W 1 A  

 7 2 2
1 
2 7 2  R2 
27 
 2 2 7 

1

 R  R

2
R

2
R

2
R

13

2
R

2
R

 32

2
R

2
R

2 R4  R2
6  R2   R2  R4
2 R4  R2
4
R

2
R

2R 2 R2  R2



2
R

2
R

2
R

 31

2
R

 R2
16  10 R2  R2   R4

2
R

2
R

2
R

1

4
R

6 R4  R2   R2  R4

2 R4  R2
2 R4  R2
2R 2   8     2   8   R   6  
4R 



 2   8     2   8    
R  4    
4R 



 2   8     2   8   R  4    R  4     2   8     2   8   R  4    .
2   8   2R

 RK2 

2R

2R1 R2  R2

R3 

2
R

2
R

 23

2
R

2
R

2
R

2
R

2
R

2
R

 32

2
R

2
R

4
R

2
R

2
R

12

2
R

 31

2
R

 , then computed range

estimates variance for (6) – (8) can be expressed by2R

 2   8     2   8   R  4    
2R   2   8     2   8    
R  4    
R 4



 2   8     2   8   R  4    
4R 



 2   8     2   8   R  4    , 2   8   
2Rvariance as  RK2  diag AT W1A

2R

2R1 R2  R2

R2 

2
R

2
R

2
R

2
R

2
R

2
R

 32

2
R

12

2
R

2
R

2
R

 31

2
R



981

2
R

2
R



1

 R  Rlim  AT W 1 A   0, lim  AT W 1 A   0 
1

 R 01 R 0

From the analysis of (10) – (14) we can conclude that
increasing range and sum of range primary measurement error
variance to maximum does not affect correct joint processing
algorithm operation hence it is possible to increase range
estimation accuracy by primary redundant measurement trials
accumulation [12].

2
R

2
R

2
R

C. Joint Processing Range Estimates Accuracy Analysis
Equations (6) – (8) are range estimates between transceiver
stations and object of location derived by measurement
processing unit from primary range measurements received
from all transceiver stations. Let's define range estimates

III. EXPERIMENTAL RESULTS ON MLAT
WITH JOINT PROCESSING OF RANGE MEASUREMENTS
A. Developed Software-Defined Radio MLAT Testbed
To realize experimental validation of proposed algorithm
for multipoint joint processing of range measurements withtrial results accumulation we developed a software-definedradio MLAT testbed by means of model based design via
software defined radio [14] on National Instruments hardware
NI USRP-2932 [15] and LabVIEW software [16]. It consistsof three transceiver stations gathering measurements, one
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017transceiver station realizing functions of an object of location(active reflector), and one processing unit. Layout of thedeveloped multipoint software-defined radio testbed for joint
processing of range measurements experimental evaluation isdepicted in Fig. 2.
NI USRP-2932 worked in 433 MHz Low Power Devices(LPD) band with power constraint of 20 dBm forcommunication with binary phase shift keying signals of 2
MHz bandwidth modulated by unique pseudorandom Goldsequence with 500 ns pulse duration and 3 ms pulse-repetition
cycle.
It is worth noting, that previous experimental validation[12] was carried out for indoor laboratory conditions in thestatic scenario when transceiver stations and object oflocation were placed in the room with spatial separation ofseveral decades of meters and worked stationary. To ensure
autonomous mobile deployment for outdoor field conditionsof presented MLAT testbed we manufactured 4 prototype
models of transceiver stations with self-contained power
supply and cooling depicted in Fig. 3.

PC

PC

WLAN

NI USRP-2932

Transceiver station
gathering measurements

NI USRP-2932

WLAN

982

Transceiver stations are realized on National Instruments
hardware NI USRP-2932 and work under specially developed
LabVIEW
softwareapplications.
Developed
software-defined radio testbed operates in the following way[12]. Transceiver stations based on NI USRP-2932 andworking on Core I7 microPC under Windows 7 withLabVIEW application “Server” transmit unique signals basedon pseudorandom binary sequence and then receive theirretransmitted copies from object of location and thus gather
TOA. Object of location estimation is an active reflector
based on NI USRP-2932 and working on Core I7 microPCunder Windows 7 with LabVIEW application “Repeater”
which retransmits unique signals, received from “Server”.
Finally, measurement processing unit working on Core I7
microPC under Windows 7 with LabVIEW application“Active Positioning” gathers multiple TOA primarymeasurements from transceiver stations, process them andvisualize result.
To provide investigation of positioning accuracy foroutdoor mobile conditions we mounted manufactured
transceiver stations and antennas on carts with slow
pedestrian object of location movement capability as depicted
in Fig. 4.
We used 2 omnidirectional antennas AH-433: one fortransmit and one for receive channel as depicted in Fig. 5.

Transceiver station
gathering measurements
Measurement
uniqu e s ignal

WLAN
connection

PC
NI USRP-2932
Object of locationPC
NI USRP-2932

WLAN

Transceiver station
gathering measurements

PC
network switch

WLAN

Fig. 4. Mobile organization of experiment layout

Measurement
processing unit

Fig. 2. MLAT radio navigation system with 3 transceiver stations

Fig. 5. Antenna AH-433

Fig. 3. Prototype of mobile autonomous manufactured MLAT transceiver
stationB. Experimental Scenario and its Accuracy Performance
Evaluation Results
Experiment was carried out in the court of theBonch-Bruevich St. Petersburg State University of

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017

Telecommunications with object of location track movement
according to layout, depicted in Fig. 6.
During experiment 3 manufactured MLAT transceiver
stations operated in the mode of gathering primarymeasurements and one transceiver station operated as theobject of location and measurement processing unit withspatial separation of several decades of meters under line ofsight (LOS) conditions.
Network connection between transceiver stations gathering
primary measurements and processing unit is based on IEEE
802.11n WLAN. Estimated positioning accuracy wasvisualized in the LabVIEW application “Active Positioning”
running on measurement processing unit and true ranges were
estimated by laser ranger.
We performed positioning accuracy performance
evaluation in terms of range measurements MSE for two casesof joint processing: with and without primary redundantmeasurement trials accumulation. During experimental
evaluation we got following results in terms of MSE depicted
in Fig. 7 and Fig. 8.

Measurement
Unit

Measurement
Unit

983

We have the following notations in Fig. 7 and Fig. 8
considering experimental evaluation in terms of MSE: upper(red) line indicates range and sum of range primarymeasurement error variance and lower (white) line indicatesrange estimation error variance after joint processing overtime in the steady-state condition; Fig. 7 demonstrates therange MSE in meters for the case without trial results
accumulation while Fig. 8 demonstrates range MSE in meters
for the case of joint processing and ten trial results
accumulation. It can be seen, that increasing the number oftrial results accumulation during joint processing can double
positioning accuracy estimation in terms of MSE up to one
meter and these field results confirm the result reported in[12] for laboratory testbed evaluation.

IV.

CONCLUSION

In the conclusion we can state that joint processing of range
measurements with trial results accumulation can double
positioning accuracy and approach MSE of one meter. An
algorithm for such processing was proposed, then it wasevaluated analytically and finally it was experimentally
validated with software-defined radio testbed speciallymanufactured for outdoor mobile conditions.

Object of location

REFERENCES[1]
Measurement
unique signal
WLAN
connection

Measurement
Unit

Object of location track of
movement during the experimentFig. 6. Experiment layout in the court of The Bonch-Bruevich St.
Petersburg State University of Telecommunications

Fig. 7. MSE for range measurements without trial results accumulation

V. Averianov, Multiposition radar station and system. Science andTechnics, Minsk, 1978.
[2] V.S. Chernyak, The multiposition radar. Radio and Communications,Moscow, 1993.
[3] V.S. Chernyak, Fundamentals of Multisite Radar System. Gordon andBreach Science Publishers, 1998.
[4] ICAO: Multilateration (MLAT) Concept of Use, 2007.
[5] ERA company. (2015, February 27). NEO datasheet. [Online].
Available: http://www.era.cz/downloads/data-sheets/NEO.pdf[6] ICAO organization. (2011, September 2). ADSB, WAM, MLAT ATM
International & Airports ADSB, WAM, MLAT implementations.
ADSB/WAM
integration
Systems.
[Online].
Available:
http://www.icao.int/NACC/Documents/Meetings/2011/ADSBMLT/D
ay01-04-INDRA-INDRA.pdf[7] M. Leonardi, A. Mathias, G. Galati, “Closed form localization
algorithms for mode S wide area multilateration”, in Radar
Conference, pp. 73-76, EuRAD, 2009.
[8] J. Honda, Y. Kakubari, T. Koga, “DOP simulation considering
LOS/NLOS for aircraft positioning on airport surface”, in: IEEE 5th
Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), pр.
773-776, 2015.
[9] P. Xinxin, L. Yong, L. Wei, Z. Yanbo, W. Yongchun, “An approach of
fast stations selection algorithm for multilateration surveillancesystem”, in Integrated Communications, Navigation and Surveilance
Conference (ICNS), pp. P1-1-P1-7. Herndon, VA, 2011.
[10] G.M. Mashkov, E.G. Borisov, A.G. Vladyko, A.I. Gomonova, “TheUse of Software-Defined Radio Systems in Multilateral Navigation
Radio Systems”, Infocommunications Journal, vol. VII. n. 2. pp.
26-31, 2015.
[11] E.G. Borisov, G. M. Mashkov, A. G. Vladyko, “Analysis of Object
Positioning Accuracy Provided by Range-Finding Systems of Various
Types”, Russian Aeronautics. Vol. 58 № 4. pp. 401-406, 2015.
[12] E. Borisov, G. Mashkov, G. Fokin, “Experimental Validation ofMultipoint Joint Pro-cessing of Range Measurements via
Software-Defined Radio Testbed”, in: 18th International Conference
on Advanced Communication Technology (ICACT), pp.268-273,
2016.
[13] E.G. Borisov, S.V. Bachevskiy, G. M. Mashkov, “Increasing
Estimation Accuracy of Unknown Parameters by Means of
Cooperative Processing of Redundant Measurements in a System of
Spatially Distributed Sensors”, Sensors an Systems. № 12 (198).
pp.16-20, 2015.

Fig. 8. MSE for range measurements with ten trial results accumulation
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 1, January 2017984

[14] G.A. Fokin, D.V. Bulanov, D.B. Volgushev, “Radio communication
systems model based design via software defined radio”, Vestnik
sviazy, vol. 6, pp. 26-31, 2015.
[15] National Instruments company. (2015, August 17).
NI
USRP-292x/293x datasheet. Universal Software Radio Peripherals.
[Online]. Available: http://www.ni.com/datasheet/pdf/en/ds-355[16] LabVIEW home page. [Online]. Available: http://www.labview.ru/
Georgiy Mashkov was born in 1954 in Uzlovaya,
a town and the administrative center of Uzlovsky
District in Moscow Region. He graduated Kiev
Higher engineering school in 1977 as the engineer at
speciality air defence systems, then received Ph. D
and D. Sc there in 1984 and 1993 respectively.
He is working is working in St.Petersburg
University of Telecommunication as first Vice-rector(Vice rector for Academic Affairs). His studies focus
on radio navigation.
Dr. Sc. Mashkov is Honorary Worker of Higher Professional Educationof Russia.
Evgeny Borisov was born in 1967 in Novosibirsk.
He graduated Kiev Higher engineering school in 1990
as the engineer at speciality air defence systems, thenreceived Ph. D in 2000 in Smolensk Higher
engineering school.
He is working is working in St.Petersburg
University of Telecommunication as leading
researcher
ofSt.Petersburg
UniversityofTelecommunication.

Grogorii Fokin was born in 1984 in Pskov. He
graduated
St.Petersburg
UniversityofTelecommunication in 2005 as the engineer at
speciality radiocommunications, then received Ph. D
there in 2009.
He is working is working in St.Petersburg
University of Telecommunication as Associate
Professor of Department of Radio Communications
and Signal Broadcasting..

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