Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

A Study of Cooperative Sensing based on Energy Detection
Shunsuke FUJIMAKI† , Tadahiro WADA† , and Kaiji MUKUMOTO‡†

Graduate School of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email: {f0230135,tetwada}@ipc.shizuoka.ac.jp

Abstract
For cognitive radio systems, one of important keys tomake it possible to eﬀectively utilize of frequency bandis detection of the primary system. The energy detection is one of candidate of signal detection technologies. It is known that cooparating with other terminals
makes performance better. In this paper we evaluate
an eﬀect of cooperative sensing. We show results ofperformance in cases that the number of cooperative
terminals is ﬁxed and is poisson distributed.

Index Terms—cognitive radio, cooperative sensing,energy detection
1. Introduction
Recently, a shortage of available frequency band by
widely spreading of wireless apparatus is very serious
problem all over the world. Since the frequency band
which can be used by wireless communication is limited, it is possible that the frequency band which canbe used is drained. However in some cases, since communication is not perfomed continuously with respect
to time-course, there are a lot of vacant channels. If
such vacant channels with respect to time-course canbe used eﬀectively, it is possible to increase eﬃciencyof frequency use.
A cognitive wireless technology is one of candidates
enabling to eﬀectively use of frequency. The cognitive
radio technology is an intelligent radio that can be programmed and conﬁgured dynamically. Its transceiver
is designed to use the best wireless channels in its vicinity. This radio system automatically detects available
channels in wireless spectrum, then changes its transmission or reception parameters to allow simultaneoustransmissions in a given frequency band. Researches of
the cognitive radio system sharing frequency are done
briskly.
The terminal which can use a certain frequency
band preferentially is deﬁned as Primary Terminal(PT). The terminal which can not use a certain frequency band preferentially but can temporary use is de-‡Faculity of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN

Figure 1: Structure of energy detection

ﬁned as Secondary Terminal(ST). ST allows to use thefrequency band, unless it interferes to the wireless system of PT. However PT will be aﬀected if ST mistakenly communicates while PT is communicating. Therefore, it is important for ST to investigate whether PT
is communicating or not in advance. One of the methods for judging whether communicating is performed
in a certain frequency band is energy detection[1][2].
Energy detection is the method of judging whether PT
communicating or not and compares the electric signal
power at ST with a predetermind threshold value.
In usual ST investigates signal power. However itis thought that a sensing by only one terminal, namely
individual sensing, might be inaccurate because of fading, shadowing and so on. Then, it is expectable thatthe accuracy improves by sensing in cooperation with
two or more terminals[3][4]. The sensing method bycoorperation with plural terminals is called ”Cooperative Sensing”.
In this paper we evaluate an eﬀect of cooperative
sensing. We obtain results of performance evaluationin cases that the number of cooperative terminals is
ﬁxed and is poisson distributed.
2. Energy Detection
2.1. Procedures of Energy Detection
Figure 1 shows the receiver structure of energy detection. The energy detection is one of signal detection
methodes. As compared with other methods, the energy detection has following merits:· ST needs not to know PT modulation scheme, and· Structure of energy detection is easy and low cost.

From Fig.1, the energy detection is performed by thefollowing procedures:
1. The energy detection circuit receives a signal.
2. The signal is then band-pass-ﬁltered whose band
width is W . The ﬁlterd signal, s(t), can be expressed as,{n(t)· · · H0s(t) =(1)
hsP T (t) + n(t)· · · H1
where H0 means the hypothesis that PT is notcommunicating and H1 means that PT is communicating. h and n(t) mean a chennel coﬃcientand an addictive noise, respectively.
2

3. The signal is then squared, s(t) .

Figure 2: System model of cooperative sensing

4. After that, the signal is integrated over the interval T , i.e.,∫2s(t) dt '
T

2T
W
∑

ai .

(2)

i=1

T W is a important parameter for the energy detection and called as the ”degree of freedom”.
5. Finally the signal is compared with the thresholdδ.
If it higher than δ, ST decides PT is communicating.
On the other hand, if it is lower than δ, ST decides PT
is not communicating.

3.1. System Model
Figure 2 shows a system model of cooperative sensing. SUBTs mean cooperative terminals for ST.
In this paper, since ST and SUBTs are assumed to
be located at almost the same position relative to PT,
SNR of all received signal by ST and SUBTs is considered to be the same. And to simplify the discussion,these terminals have the same degree of freedom of energy detection. ST reveives information of the energy
detection from SUBTs and performs a synthetic decision.
3.2. Decision Rules for Cooperative Sensing

2.2. Issues of Detection Errors
The energy detection essentialy has two types oferrors, ”false alarm” and ”miss detection”. The ”false
alarm” means that ST mistakenly decides PT is communicating in spite of PT is not communicating. The”miss detection” means that ST mistakenly decides PT
is not communicating in spite of PT is communicating.
Probabilities of the ”false alarm” and the ”miss detection” under AWGN channels are expressed as[1],Γ(T W, 2δ ),Pf a =
Γ(T W )√√
Pmd = 1 − QT W ( 2T W γ, δ),

(3)

(4)

where Γ(, ) denotes incomplete Gamma function and γmeans SNR. The false alarm rate and miss detection
rate are important factors for evaluating the performance of the energy detection.
3. Cooperative Sensing based on Energy Detection

There are many decision rules for cooperative sensing. The following methods have been mainly considered as a synthetic decision method for cooperative
sensing,· Averaging-rule,· AND-rule,· OR-rule, and· Majority-rule.
In this maniscript, we forcus on Averaging-rule. In
Anveraging-rule, ST takes the average of all sensing values of ST and SUBTs, and compares it with a threshold δ. Firstly, ST and SUBTs perform energy detection
and have a sensing value. Then, each SUBT transmits
each sensing value to ST and ST calculates an average of all sensing values including the value obtainedby ST itself. Finally, ST compares it with a thresholdδ and decides judgement of cooperative sensing. The
false alarm rate using Average-rule is denoted as,
Γ((N + 1)T W, (N +1)δ)2,
Pf a =
Γ((N + 1)T W )(5)

Table 1: Assumptions
SNR

2.0[dB]degree of freedom

5.0

Channel

AWGN

Number o trials

100000where N means the number of SUBTs. The miss detection probability using Averaging-rule is denoted as,√√
Pmd = 1 − Q(N +1)T W ( 2(N + 1)T W γ, (N + 1)δ). (6)AND-rule places importance on ”false alarm”.
Firstly, ST and SUBTs perform energy detection. Then
ST and all SUBTs individually make a decision whether
PT is communicating or not. Next, ST collects all decisions from SUBTs. By AND-rule, ST ﬁnally decides
PT is transmitting if ST and all SUBTs decide that PT
is transmmitting.
OR-rule places importance on ”miss detection”.
Firstly, similar to AND-rule, ST and SUBTs perform
energy detection and they individually make a decision.
By OR-rule, ST ﬁnally decides PT is transmitting if ST
and all SUBTs decide that PT transmmitting.
Majority-rule is also simillar to AND-rule and ORrule. Majoruty-rule takes majority of collected individual dicisions by ST.

Figure 3: The eﬀect of cooperative sensing with respect
to the number of SUBTs.

1 with N = 2. Both of Case 1 with N = 2 and Case 2
have the same average of SUBTs. It is because that, inCase 2, probability that the number of SUBTs is lessthan two is not so small, and the performance of thecase is signiﬁcantly aﬀected by situation of small number of terminals. Therefore, we can consider that theperformance is more strongly subject to the inﬂuenceof the situation with few terminals.

4. Numerical Examples

5. Conclusion

In this section, we give some numerical exapmles toevaluate eﬀect of cooperative sensing. We consider twocases as listed below:

We have shown the eﬀect of cooperative sensing.
We ﬁrstly introduced energy detection and dicisionrules of cooperative sensing. Then we have evaluatedthe performance of cooperative sensing in cases that the
number of SUBTs is ﬁxed and Poisson distributed. As
a result, we have shown that cooperative sensing withﬁxed number of SUBTs exhibits better performance.

· Case 1: cooperative sensing with ﬁxed number of
SUBTs· Case 2: cooperative sensing when the number of
SUBTs is Poisson distributed
Table 1 shows assumptions for numerical examples.
Figure 3 shows the performance of cooprative sensing with respect to the number of SUBTs. The varticalline shows the probability of miss detection and the horizontal line is the probability of false alarm. These lines
can be obtained by changing theshold value, δ. From
this ﬁgure, theotical analysys is rightly conﬁrmed by
the results of computer simulations. We can also ﬁndthat characteristic of cooperating with larger numberof terminals is better.
Figure 4 shows the eﬀect of the number of SUBTs is
not ﬁxed and is distributed. From this ﬁgure, a characteristic of Case 2 is worse than a characteristic of Case

References[1] H.Urknowitz, ”Energy Detection of Unknown Deterministic Signals,” Proceedings of the IEEE
Vol.55, No.4, pp.523-531, 1967
[2] F. Digham, M. Alouini, and M. Simon, ”On the energy detection of unknown signals over fading channels,” Proceedings the IEEE International Conference on Communications 2003(ICC2003), Vol.5,
pp.3575-3579, 2003
[3] S.Kyperountas, et.al, ”Performance Analysis of
Cooperative Spectrum Sensing in Suzuki Fading

Figure 4: The eﬀect of coopertive sensing when thenumber of SUBTs is Poisson distributed.

Channels,” Cognitive Radio Oriented Wireless Networks and Communications 2007, pp.428-432, 2007[4] Jayakrishnan Unnikrishnan, ”Cooperative Sensing
for Primary Detection in Cognitive Radio,” Proceedings of the IEEE Vol.2, No.1, pp.18-27, 2008

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

Introduction to Controller Area Network Communication
Junya WATANABE† , Tadahiro WADA† , Kaiji MUKUMOTO‡†

Faculty of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email:{f0011149, tetwada} @ipc.shizuoka.ac.jp‡

Division of Technical Services,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN

Abstract
In this paper, we introduce Controller Area Network(CAN) used as the standard of in-vehicle networks. And we describe the partial protocol of CAN.
In particular, we explain an arbitration which is one of
the important characteristic structures in CAN.

deployment of ECUs, communication capacity of CAN
may be short. Especially, an electronic control system
for a hybrid fuel cars and an electric vehicle requires
high transmission capability.
In this study, we aim to recognize the protocols of
CAN to improve the capability of communications.

1. Introduction

2. The protocol of CAN

In the second half of the 1970s to the 1980s, electronization of cars started as backgrounds of the emission control and fuel regulation on cars. From thosedays, electronic control of cars has been developed instead of mechanical control. By using the electric control, precise controls of the mixture ratio of air and fuel,and the optimal engine ignition timing can be achieved.
However, while control facilities of cars increase, electronic components increase and weight and a space of
wiring also increase. An in-vehicle network, which can
reduce wiring cost, is a system which controls broadly
cars such as a power train system, a body system, anda comfortable equipment system. The network also improves safety, reliability, tolerance to external factors,
cost for driving cars.
There are various protocols of in-vehicle networks.
Controller Aria Network(CAN) is a serial bus system developed for in-vehicle networks, and it is widely
spread as an in-vehicle network protocol. CAN canrealize reduction of wiring cost and the weight savingof cars by adopting bus communication between two
or more Electric Control Units (ECUs) instead of wire
harness.
Nowadays, with the increase in functions we ask
for, the number of ECUs is increasing every year. The
electronic control system of cars, therefore, becomescomplicated and large-scaled. To realize the control
having additional functions such as car navigation systems, parking assists, etc, and to improve the basic
movement of cars about a run or a drive such as engine control, the brakes assistance, etc, large number of
ECUs should be required. In particular, high real-timenature is required in the control system concerning a
run or a drive. However, with increase in number of the

In this chapter, we briefly introduce features of
CAN.
2.1. Line type structure
Generally, communications equipment connected to
network is called a node. In the case of an in-vehicle
network, an ECU becomes a node. CAN is adopted aline type. Since the line type can constitute a networkfrom connecting each node to bus simply, the network
becomes simple and the design of it is easy.
2.2. Multi-master system
CAN is adopted the ”multi-master method” which
can realize equal bus access priority for every node connected to the bus. If the bus is vacant, every node canstart transmission of data.
2.3. Access method
Usually, if data is freely transmitted by two or morenodes, a collision of data will occur.
However, this cannot be prevented when data issimultaneously transmitted to bus from two or morenodes.
CAN adopts the media access method similar to
CSMA/CD which is adopted by Ethernet. The difference between CAN and Ethernet comes from that
real-time nature is more important for CAN. From thestructure of CSMA/CD, transmissions are interrupted
if a collision is detected, and after having waited at a
certain random time, the node retransmits the message.
In this case, however, communication delay occurs andcauses degradation of communication performance.

On the other hand, in CAN, a concept of a priority of frame is applied. If frames are simultaneouslytransmitted from two or more nodes, a high priority ofa frame can continue transmission. By applying suchthe structure, a frame with a high priority can be transmitted to bus without being delayed.

1．Data frame
A data frame is used in order to transmit data. This
frame consists of seven fields. The structure of a data
frame is shown in Fig.1.

2.4. ID(Idetifier)
In Ethernet, origin of transmission address and destination address are included the transmitted frame.
On the other hand, there is no such concept in CAN.
Instead, CAN is applied a concept of the identifier(ID).
An ID is added to the transmitted frame, and this ID
shows the kind of contents of data and the priorityof frame. When frames are simultaneously transmitted from two or more nodes, the node which continues
transmission and the node which should wait for transmission are determined by comparing their IDs. This
operation is called ”arbitration”.
2.5. Arbitration

Figure 1: The structure of a data frame
The seven fields in a data frame is listed below.
• SOF(Start of frame)

2.5.1. Bus level• Arbitration field

In CAN, frames consist of binary digits. Signals of
0s and 1s are called as ”Dominant”and ”Recessive”, respectively. Dominant means ”being preferential”, andthe value of a bus will become dominant if this value is
outputted from at least one node. On the other hand,
Recessive means ”being receptive”, and the value ofa bus will become recessive if the output from all thenodes is recessive.

• Control field

2.5.2. Frame structure
In CAN, there are four kinds of frames as listed
below:• Data frame• Remote frame• Error frame• Overload frame
In the data frame and the remote frame, there are
two kinds of formats, a standard format and an expansion format. A standard format has 11 bits of the ID,
and an expansion format has 29 bits of the ID. In thispaper, we take up a standard format for examples.

• Data field• CRC1 field• ACK field• EOF(End of frame)
SOF shows the start of a frame. SOF serves as a
1-bit dominant bit.
The arbitration field consists of an identifier (ID)and a RTR (Remote Transmission Request) bit2 .
Here, the length of the identifier is specified as 11
bits by the standard format. An identifier is transmitted from the Most Significant Bit(MSB). It is forbiddenthat all of top 7 bits are recessive. Moreover, the identifier must be unique, that is, diﬀerent roles of frames
have diﬀerent IDs.
The control field consists of 6 bits. 4 bits out of
6 bits are a data length code(DLC0〜DLC3), and remaining 2 bits are used as reservation for future extension. A data length code determines the length (0 to 8
bytes) of the data field.
A data field consists of data to transmit and it istransmitted from most significant bit. As explained,
the number of bytes of data varies 0 to 8 bytes.
1 Cyclic Redundancy Check
2 RTR expresses distinction between a data frame and a remote frame. In the case of a data frame, the value of a RTR
bit is Dominant. In contrast, the value of a RTR bit becomes
Recessive for a remote frame.

The CRC field is a field to check errors in the frame
and it consists of 15 bits CRC sequence and a 1-bit
CRC delimiter which is the bits as distinction with the
ACK field.
The ACK field is the field for reporting that thetransmitted message is rightly received by receivers. It
consists of 2 bits: 1 bit for ACK slot and 1-bit for ACK
delimiter.
EOF shows the end of a frame.

• when the internal state of a receiving node requires delay of the following data frame or a remote frame and,• when the bit which becomes dominant is detected
in inter-frame space. 3
2.5.3. The process of the arbitration

2．Remote frame
A remote frame is a frame for requesting transmission of a data frame for other nodes. Diﬀerence between
the data frame and remote frame is only the existenceof a data field or not. The structure of a remote frame
is shown below.

Figure 3: An arbitration of CAN

Figure 2: The structure of remote frame• SOF(Start of frame)• Arbitration field• Control field• CRC field• ACK field• EOF(End of frame)
The contents of each field are fundamentally thesame as the data frame, except for the value of RTR.
3．Error frame
The error frame is a format in the case that a nodedetects an error and it notifies the error.
4．Overload frame
An overload frame is transmitted in the cases that:

Figure 3 shows the state diagram that three nodes
connected to the network try to transmit their messages
simultaneously. Their message frames of CAN startwith SOF and the bus level at this time is dominant.
Then, the identifier of the arbitration field is transmitted. The arbitration begins from the MSB of thisidentifier.
Every node can monitor the bus level. In the caseshown in Fig.3, the 10th bit of ID is the same, i.e.,
Recessive, for all nodes.
Thus , the bus level corresponds to the 10th bit isrecessive.
In this figure, from 10th to 6th bits, all nodes havethe same bit level. Thus by monitoring the bus line,
all nodes judge that they can continue transmission.
At the 5th bit in arbitration field, we can see that
Nodes 1 and 3 transmits Dominant signal and Node
2 transmits Recessive signal. While having transmitted the identifier, a logical product (AND) is taken forevery bit, and the arbitration is performed.
The logical product at the 5th bit is ”0” because
at least one node sends Dominant signal, ”0”. As a
3 Between a certain data frame and the data frame (or remoteframe) which comes to the next, it is specified that the space
more than a triplet is provided and it dissociates, and this iscalled inter-frame space.

result, the bus level at the 5th bit becomes Dominant.
By monitoring the bus level, Node 2 can find that the
bit which Node 2 transmits is diﬀerent from the bus
level. In this case, Node 2 stops its transmission.
Similarly, Node 1 stops transmission at the 2nd bit
in the arbitration field. Finally Node 3 can transmit
its frame by this arbitration.
3. A micro computer system modeling CAN

Figure 6: CAN Bus Line Scope

Figure 4: PIC-CAN 360 Evaluation Board
Fig.4 shows the PIC-CAN 360 Evaluation Board,made by Micro Application Laboratory, Inc. Threenodes are put on this board. One of three nodes becomes CAN communication monitor. Furthermore, we
can see a CAN bus by connecting USB and a PC.

Fig.6 shows a screen shot of CAN Bus Line Scope.
We can monitor a data flowing through a bus.
4. Conclusion
We have described the outline of CAN and explained that CAN is required for high transmission capacity by electronization of cars.
And then, we have explained the structure of thearbitration of CAN.
Finally, we have introduced the environment equipment for understanding structure of CAN.
References[1] S. Igarashi, M. Sato, R. Tamashiro, ”CAN introduction lecture, The CAN protocol and programming which are studied with an inclusion microcomputer”, Dempa Shimbun, 2008.

Figure 5: MPLAB IDE
Fig.5 shows the screen shot of programming software, MPLAB IDE. It is provided by Microchip Technology, Inc, and is used for writing a program.

[2] M. Sato, ”In-vehicle network system thoroughness description, The protocol of CAN, LIN, and
FlexRay, and mounting ” , CQ publishing company, 2005.
[3] A. Ogawa, 「PIC CAN Technical Guide」, Micro
Application Laboratory Inc..
[4] ”What is CSMA/CA ?, IT glossary binary”,
http://www.sophia-it.com/content/CSMA/CA[5] ”What is CSMA/CD ?, IT glossary binary”,
http://www.sophia-it.com/content/CSMA/CD

[6] ”Insider’s Computer Dictionary:CSMA/CD”,
http://www.atmarkit.co.jp/icd/root/96/
5787296.html
R[7] ”MPLAB⃝ICD
3 In circuit debugger User’s
guide”, Microchip Technology Inc.,

http://akizukidenshi.com/download/ds/
microchip/DS51766A_JP.pdf[8] Dr.Conal Watterson, ”Application Note, The
mounting guide of CAN application （Japaneseversion）”,
http://www.analog.com/static/
imported-files/jp/application_notes/
AN-1123_jp.pdf[9] ”The outline of CAN, and the feature of FUJITSU
CAN”,
http://edevice.fujitsu.com/jp/catalog/
find/18-1j/pdf/j18-1-2.pdf[10] ”First CAN”, Vector Japan Inc.,
http://download.vector-japan.co.
jp/portal/medien/cmc/beginners/For_
Beginners_CAN.pdf

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

Studies on Performance Evaluation of a Remote Monitoring System
Using Meteor Burst Communications
Yasuhiro TAKEUCHI† , Kaiji MUKUMOTO‡ , and Tadahiro WADA††

Graduate School of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email: f0330121@ipc.shizuoka.ac.jp‡

Division of Technical Service, Shizuoka University.
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN

Abstract
Meteor Burst Communications(MBC) is one of beyond line of sight communication modes, which is
known to have many advantages for systems with delay
tolerance and low data transmission rate. This paper
considers a remote monitoring system collecting fixedlength monitored data in a fixed period from many remote stations deployed in large area to one master station by using MBC. In this paper, we propose a datatransmission protocol for this system, named randomtransmission scheme, and evaluate the performance ofthe scheme by numerical analyses and computer simulations. Optimization of the transmission probability
is also investigated.

ofheight0km

about 10

Transm
itter

iver
Rece

Earth’s surface

less than 2000km

Figure 1: Meteor burst communication

1. Introduction
Meteors are space dust particles entering the earth’s
atmosphere. Although most of the meteors cannot
be seen by naked eyes, billions of tiny meteors enter
the earth’s atmosphere daily. When a meteor entersthe earth’s atmosphere, an ionized gas column (meteor
burst) is generated at the height of about 100 kilo meters. The typical length of a meteor burst is several
tens kilo meters. Meteor bursts disappear in a few seconds due to diﬀuse. However, during its brief existence,a meteor burst works as good reflection medium of radio waves in low VHF band (30-100MHz) and enables
over-the-horizon communication between two stations
within 2,000 kilo meters. (See Figure 1).
Meteor bursts are roughly classified into two categories: “under-dense burst” and “overdense burst” according to the reflection properties. The typical received signal level variations reflected from an underdense burst and an overdense burst are shown in Figures 2 and 3, respectively. The received signal levelfrom an underdense burst exhibits abrupt increment
at the instance of the meteor burst occurrence and attenuates exponentially with time. The overdense burst
is characterized by relatively long duration time.
Deﬀerence of the reflection properties depends onelectron line density of the meteor burst. The boundary value is about 2 × 1012 [electrons/m] and the value
corresponds to a meteor burst generated by a space
dust particle of several milligrams. The number of meteors is inversely proportional to the weight of space
dust particles. Therefore, most of the meteor bursts
are underdense burst.
Among a great number of meteors entering theearth’s atmosphere, only those entering at proper location and correct orientation support communication
between two specific points. From past experiments, it
is known that the generation interval depends on exponential distribution with the average of several tens
seconds.
Duration time of a meteor burst diﬀers from burst to
burst. It is known that the duration time also depends
on exponential distribution with the average of several
hundreds milli seconds.
This paper considers an MBC network which collects monitoring data from many remote stations to
one master station in a fixed time period.
The rest of this paper is organized as follows: In
Section 2, we describe a meteor burst channel model,
ON-OFF channel, assumed throughout the paper. We ᵹᤊࡃ࡯ࠬ࠻ㅢା〝ߪ‫ࠆߊߡߒ౉⓭ޔ‬ቝቮႲߩᄢ߈ߐߦࠃࠅ‫ߩߘޔ‬ធ⛯ᤨ㑆߇⇣ߥࠆ‫ޕ‬ቝቮႲ߇Ყセ⊛ዊߐ޿႐ว‫ޔ‬ฃାାภ㔚ജߪᜰᢙ㑐ᢙ⊛ߦᷫ⴮ߒ‫ޔ‬ᄢ߈޿႐วߪㅢା〝ߪᲧセ⊛㐳ߊ⛮⛯ߐߖࠆ߇‫ޕ߁઻ࠍࠣࡦࠫ࡯ࠚࡈߚߒߣࠅߊߞࠁߦ⥸৻ޔ‬೨⠪ߪࠕࡦ࠳࡯࠺ࡦࠬ‫ޔ‬ᓟ⠪ߪࠝ࡯ࡃ࡯࠺ࡦࠬߣ๭
߫ࠇࠆ‫ޕ‬࿑㧞ߦߎࠇࠄߩฃା⁁ᘒߩ৻଀ࠍ␜ߔ‫ޕ‬

Received signal level࠺ࡦࠬ㧞㧞ࠕࡦ࠳࡯࠺ࡦࠬ࡮ࠝ࡯ࡃ࡯࠺ࡦࠬmߒߡߊࠆቝቮႲߩᄢ߈ߐߦࠃࠅ‫ߩߘޔ‬ធ⛯ᤨ㑆߇⇣ߥࠆ‫ޕ‬ቝቮႲ߇Ყ ON stateߪᜰᢙ㑐ᢙ⊛ߦᷫ⴮ߒ‫ޔ‬ᄢ߈޿႐วߪㅢା〝ߪᲧセ⊛㐳ߊ⛮⛯ߐߖࠆ࡯ࠫࡦࠣࠍ઻߁‫ޕ‬೨⠪ߪࠕࡦ࠳࡯࠺ࡦࠬ‫ޔ‬ᓟ⠪ߪࠝ࡯ࡃ࡯࠺ࡦࠬߣ๭
ᘒߩ৻଀ࠍ␜ߔ‫ޕ‬Time[ms]OFF statelFigure 2: Underdense burst

Received signal level      Figure 4: The state transition
diagram of the ON-OFF
C ࠕࡦ࠳࡯࠺ࡦࠬ
D ࠝ࡯ࡃ࡯࠺ࡦࠬ
model             ࿑㧞 ฃା㔚ജߩᄌേ
!

open state (ON state) and closed state (OFF state), as
shown in Figure 4. A packet transmitted in ON stateis supposed to be succeeded in transmission, if there isࡈ࠶࠻ࡊ࡝ࡦ࠻ߣߪ‫ޔ‬ㅍାߒߚ㔚ᵄ߇㧝ߟߩᵹᤊࡃ࡯ࠬ࠻ߦࠃࠅ෻኿ߐࠇዯߊ▸࿐ߢ޽ࠆ‫ޕ‬㧔࿑㧟㧕
no collision with other packets. The signal transmitted
in OFF state is not received at all. The state of the࿾਄ߢߩㅢାน⢻▸࿐ߪ㚍かဳߦߥࠆߎߣ߇ᄙ޿߇‫ޔ‬ᒻ߿ᄢ߈ߐߪࡃ࡯ࠬ࠻ߩ૏⟎‫ⷺޔ‬ᐲ߿㐳ߐߦࠃchannel is assumed to change according to a Markov
ࠅ㆑޿߇޽ࠆ‫ߩߘߚ߹ޕ‬ᄢ߈ߐߪ‫ޔ‬ᵹᤊࡃ࡯ࠬ࠻߇ㅍାዪߦㄭߌࠇ߫ᄢ߈ߊߥࠅ‫ޔ‬ฃାዪߦㄭߌࠇ߫
process, and the state transition rate m from ON state
曜日
Time[ms]
to OFF state and the state transition rate l from OFFዊߐߊߥࠆ‫ޕ‬
Figure 3: Overdense burst
state
to ON state are set constant. That is, the channelࠬ D ࠝ࡯ࡃ࡯࠺ࡦࠬ
inOFF
state will be in ON state after the time expo
nentially distributed with the average of 1/l seconds,  ࿑㧞 ฃା㔚ജߩᄌേ
ᵹᤊ䊋䊷䉴䊃
and the channel in ON state will be in OFF state af
briefly descirbe a remote monitoring system using MBC
ter the exponentially distributed time with the average䊐䉾䊃䊒䊥䊮䊃
and propose
our transmission protocol for the system inof 1/m seconds.
Due to the noise of the receiver cirࡐ࠶࠻! Section 3. In addtion, Section 3 gives the performance
cumference, the value of 1/l is bigger than the above౉኿ᵄ
analysis of the protocol. In Section 4,
the performancementioned 1/l0 , and the typical value will be about෻኿ᵄ 㧔࿑㧟㧕ߚ㔚ᵄ߇㧝ߟߩᵹᤊࡃ࡯ࠬ࠻ߦࠃࠅ෻኿ߐࠇዯߊ▸࿐ߢ޽ࠆ‫ޕ‬of the transmission protocol is evaluated by numericalseveral minutes from tens of seconds. However, it isresults.Finally, in Section 5, we conclude this paper.
ߦߥࠆߎߣ߇ᄙ޿߇‫ޔ‬ᒻ߿ᄢ߈ߐߪࡃ࡯ࠬ࠻ߩ૏⟎‫ⷺޔ‬ᐲ߿㐳ߐߦࠃ
known that the average duration time of meteor bursts
hardly changes even if the detection threshold valueߪ‫ޔ‬ᵹᤊࡃ࡯ࠬ࠻߇ㅍାዪߦㄭߌࠇ߫ᄢ߈ߊߥࠅ‫ޔ‬ฃାዪߦㄭߌࠇ߫
changes and typical value of 1/m is hundreds of milli
水曜日
2. Channelmodelseconds which are almost the same typical as 1/m0 .

㧞㧟! ࡈ࠶࠻ࡊ࡝ࡦ࠻࡮ࡎ࠶࠻ࠬࡐ࠶࠻!

Generationof a meteor burst channel and its du
ration time are probabilistic. Both generation interval࿾⴫㕙 3. The remote monitoring system using MBC䊷䉴䊃and duration
time of the meteor burst channel for two
stations follow exponential distributions as stated preIn this paper, we consider an MBC network which               ࿑㧟 ࡈ࠶࠻ࡊ࡝ࡦ࠻
viously. Here,䊐䉾䊃䊒䊥䊮䊃
we denote the averages of generationcollects monitoring data periodically generated inintervaland duration time by 1/l0 and 1/m0 , respecmany remote stations to one master station in a fixed෻኿ᵄ
tively. The values of 1/l0 and 1/m0 depend on various
time T , called observation period. Let us denote the ዪ㑆ߩㅢା〝ߩਛߢ‫ߦ․ޔ‬෻኿ല₸ߩ⦟޿ࡃ࡯ࠬ࠻߇⊒↢factors,ࡎ࠶࠻ࠬࡐ࠶࠻ߣߪฃାዪߣㅍାዪߩ
such as system transmit power, frequency, antotal number of remote stations as M . Each remotetenna beam width, and communication range. Morestation is assumed to generate monitoring data simulover,ߔࠆⓨၞߩߎߣࠍ޿߁‫ࠆ޿ߡࠇߐߣ࠻࠶ࡐࠬ࠻࠶ࡎߩߎޕ‬ⓨၞߪ‫⋥ޔ‬ᓘ߇ਔዪࠍ⚿߱✢਄ߦ޽ࠆᬦ౞
as for 1/l0 , there are significant diurnal and seataneously. Generated monitoring data are contained insonalߩ๟਄ߢ‫ޔ‬variations. ߎߩᬦ౞ߩ⋥ᓘߪዪ㑆〒㔌ࠃࠅ߿߿㐳ߊ‫ޔ‬a fixed⍴ᓘߪ㜞ᐲߩ⚂
length packet. Ifthepacket can not succeed୚ߣߥࠆߎߣ߇⍮ࠄࠇߡ޿ࠆ‫ޕ‬For the purposes of analysis and/or simulations, varin transmission within the observation period T , it isious channel
models are proposed. In this paper, in
discarded.

order
to
evaluatethefundamental
performanceoftheAs the transmission protocol of such an MBC data࿾⴫㕙㧞㧠೑↪᭴ᚑMBC
network, we adopt the simplest model called ONacquisition system, various schemes such as repetition
OFF channel model.
transmission scheme[3], polling scheme, and power conᵹᤊࡃ࡯ࠬ࠻ㅢାߪ‫ޔ‬ㅢᏱߩⴡᤊࠍ↪޿ߚㅢା߿⍴ᵄㅢାߣᲧߴ‫ᤨ↢⊒ޔ‬ೞ߿ㅢାᤨ㑆ߥߤߦ೙㒢 ࿑㧟 ࡈ࠶࠻ࡊ࡝ࡦ࠻
In theON-OFF channel model, the state of the chantrol scheme[5] have been proposed. Polling schemenel fortwo
stationsis
supposed
to
be
one
of
two
states:can further be classified into individual polling, group߇޽ࠆ‫ޔ߼ߚߩߘޕ‬៤Ꮺ㔚⹤ߥߤߩࠃ߁ߦᏱᤨㅢାࠍⴕ߁ߩߦߪะ޿ߡ޿ߥ޿߇ࡄࠤ࠶࠻ㅢାߦߪ᦭

ㅍାዪߩ  ዪ㑆ߩㅢା〝ߩਛߢ‫ߦ․ޔ‬෻኿ല₸ߩ⦟޿ࡃ࡯ࠬ࠻߇⊒↢ലߢ޽ࠆ‫ޕ‬๟ᵄᢙᏪߪ 㨪/*\ ࠍ↪޿ࠆߎߣ߇น⢻ߢ޽ࠅ‫ޔ‬๟ᵄᢙ߇ૐ޿߶ߤᒝ޿෻኿߇ᓧࠄࠇࠆ‫ޕ‬࠶࠻ࠬࡐ࠶࠻ߣߐࠇߡ޿ࠆⓨၞߪ‫⋥ޔ‬ᓘ߇ਔዪࠍ⚿߱✢਄ߦ޽ࠆᬦ౞
!
"!

㑆〒㔌ࠃࠅ߿߿㐳ߊ‫ޔ‬⍴ᓘߪ㜞ᐲߩ⚂  ୚ߣߥࠆߎߣ߇⍮ࠄࠇߡ޿ࠆ‫ޕ‬polling , and global polling schemes. In the literature[4], numerical analysis and computer simulations are
performed to compare the performance of such polling
schemes and it is shown that the group polling scheme
outperforms other schemes. However, in order to keep
the number of remote stations in a group optimal, thegroup polling scheme requires group reorganization,
which may become a heavy burden. In this paper,
we thus propose another approach, random access typeglobal polling scheme, as is explained below.
3.1. The random transmission scheme
In the random access type global polling scheme,
the master station transmits polling packets addressing
to all the remote stations. A remote station transmits
its data packet responding to the polling packet when
it has a packet to be transmitted. Polling packets inglobal polling scheme are always addressed to all the
remote stations and the role is to examine the existenceof the meteor burst channel. Therefore, global polling
packet is also called probe packet (PP). Since remotestations which received PP always return their data
packets (DP), collision may occurs when more than one
remote stations transmit DP simultaneously. Thus, inthis paper, we propose random transmission scheme inwhich a remote station received PP sends DP by probability p to reduce the packet collision. When the masterstation receives the data packet, it returns an Ack (acknowledge) packet (AP) including the remote station
ID. The remote station which succeeded in reception of
the AP will be in hibernation and does not respond to
PP till the next observation period.
3.2. Performance analysis
Figure 5 shows an example of data collection process in the random transmission scheme. The PP
length, the DP length, the AP length, and the waitingtime of DP response are denoted by dP , dD , dA , anddW , respectively. Let us consider the case where n reTs (n)dPMaster

P
P

P
P

P
P

dD

dA

P
P

DP
DP

P
P

P
P

DP

AP

P
P

DP

P
P

P
P

DP

AP

P
P

DP

P
P

P
P

・・・

dW
Meteor Burst
One ofremotes

One ofother remotes

P
P

P
P

DP

P
P

mote stations are left in not receiving AP, i.e., (M − n)remote stations are in sleeping state. The average time
until any one of n remote stations succeeds in DP transmission and it receives the AP is represented by T̄s (n).
The average time until all of n remote stations succeedin AP reception is expressed by T̄all (n). Then, thefollowing recurrence equation obviously holds.
T̄all (n) = T̄all (n − 1) + T̄S (n)Therefore, the average time until all M stations succeed
in reception of AP is obtained by the next equation.
T̄all (M ) =Figure 5: The example of data collection process in the
random transmission scheme

M∑

T̄S (n)(2)

n=1

In the following, we show the calculation of T̄s (n). The
average number of DPs responded to a PP is calledtraﬃc and denoted by G(n) when the remaining number of remote stations is n. Moreover, the traﬃc per
remote station is denoted by g(n). Then, the averagetransmission interval of PP, T̄P (n), is calculated by thenext equation.
T̄P (n) = dP +dW +dD ·SD (n)+dA ·SDS (n)·exp(−mdD )(3)Here, SD (n) is the probability that at least one DP istransmitted corresponding to a PP, and is given by thenext equation.
SD (n) = 1 − (1 − g(n))n(4)

SDS (n) is the probability that AP is transmitted, thatis, the probability that transmission of the DP is successful and it is given by the next equation.
SDS (n) = ng(n)(1 − g(n))n−1 · exp(−mdD )(5)

If T̄P (n) is given, the traﬃc per remote station,g(n), can be estimated as follows. The period until
the channel state between one remote station and themaster station changes from OFF state to ON state andreturns to the OFF state again is called channel cycle.
From the assumption of the ON-OFF channel model,
the average number of the PP transmissions within one
channel cycle is given by (1/l+1/m)/T̄P (n). Therefore,
if the average number N̄D of DP transmitted from theremote station within one channel cycle is found, theDP transmission probability, or g(n), will be given by
the next equation.

送信
受信

(1)

g(n) =T̄P (n) · N̄D (n)1/l + 1/m

(6)Here, N̄D (n) can be calculated using the next expression.

N̄D (n) =∞∑Pr(ND > i)

(7)

i=0

Each remote station succeeds in PP reception when
the channel state to the master station is in ON-state.
Therefore, the probability Pr(k) that the number of PP
receptions within a channel cycle is k can be estimatedby the next equation, where X is a random variable
expressing the duration time of the ON state.

Pr(k)= Pr{k T̄P (n) < X < (k + 1)T̄P (n)}= exp(−mk T̄P (n)){1 − exp(−mT̄P (n))}(8)T̄P (n)·g(n) =
1/l + 1/m
+

∞
∑

{∞
∑

PDT0̄ (k) · Pr(k)k=1}

PDT0̄1̄ (k) · Pr(k)

k=h+1

[
T̄P (n)p · exp(−mT̄P (n))=
1/l + 1/m 1 − (1 − p) · exp(−mT̄P (n))]
p2 · exp(−m(h + 2)T̄P (n)){1 − (1 − g(n))n−1 }+{1 − (1 − p) · exp(−mT̄P (n))}2(13)

The probability that the remote station transmits DP
more than once in this channel cycle is given by thenext equation.

The final value of g(n) and T̄P (n) are numerically calculated from simultaneous equations (3) and (13). If
g(n) is found, the probability that the master station
succeeds in reception of DP and the AP is received in
the remote station is given by the next equation.

PDT0̄ (k) = 1 − (1 − p)k(9)

SA (n) = ng(n)(1 − g(n))n−1 · exp(−m(dD + dA )) (14)Next, let us calculate the probability of transmitting
DP twice or more. Since PP is not received during the
DP transmission, if DP is transmitted, the number of
the PP reception will decrease. The decreased number
of PP reception, h, is estimated by

Therefore, when the remaining number of remote stations are n, the average time needed for arbitrary remote station to receive AP is found as follow.

dD.
T̄P (n)T̄S (n) =T̄P (n)+ dD + dA
SA (n)(15)(10)

3.2.1. The optimal value of DP transmission probability

The probability that the remote station transmits DP
exactly once within the channel cycle is given by

Let denote the optimal DP transmission probability
which minimizes the average time T̄S (n) of equation(15) as popt . The system traﬃc G(n) is given byh={
PDT1 (k) =1 − (1 − p)k(k − h)p(1 − p)k−h(k ≤ h)(k > h).

G(n) = ng(n)(11)

Since the probability that the remote station transmits DP twice or more in the channel cycle is obtainedby subtracting the probability that the remote station
sends DP just once from the probability of DP transmitting two or more times in the case of k > h andmultiplying the probability that the first transmission
is failure, it is given by the next equation.

PDT0̄1̄ (k) = (PDT0̄ (k) − PDT1 (k)) · {1 − (1 − g(n))n−1 }(12)Therefore, in the equation (7), if the probability thatthe DP is transmitted three or more times within one
channel cycle is ignored and g(n) is calculated using
the equation (6), the next equation is obtained.

(16)

The equation (14) can be simplified when G(n) 
mdD , mdA by using the approximation of (1 −g(n))n−1 ' exp(−G). Then, the equation (15) becomes a function only depending on G as follows:
dP + dW + dD · {1 − exp(−G)}+ dD + dA
G · exp(−G)(17)
The next equation is given by diﬀerentiating the equation (17) with G and putting it to 0.
T̄S (n) 'dD= (1 − G) · exp(G)dP + dW + dD

(18)

The G which satisfies the equation (18) is the optimal
traﬃc Gopt and Gopt does not depend on the numberof remaining remote stations. The optimal traﬃc gopt
per remote station is given by substituting Gopt for the

the case of p = 0.1, the time required to collect monitoring data from all the remote stations when 1/l = 10[sec] is longer than that when 1/l = 20 [sec]. Thisis caused by increment of DP collisions. However, bycontrolling the DP transmission probability p to the
optimal value using equation (20), the data collection
completion time is kept in short.
5. Conclusion

Figure 6: T̄all VS. 1/l (in the random transmission
scheme)equation (16). The corresponding average PP transmission interval is approximated by the next equationfrom the equation (3).
T̄Popt = dP +dD ·{1−exp(−Gopt )}+dA ·Gopt ·exp(−Gopt )(19)
The optimal value popt of the DP transmission probability can be calculated by substituting gopt and T̄Popt
to the equation (13) and solving it for p. By assuming that PP is transmitted at random, it can also beapproximated as follows.
(m)· exp(mdP )(20)popt (n) ' gopt (n) · 1 +l
4. Performance evaluation
In this chapter, we show the performance evaluation of the random transmission scheme by using a
numerical example. In the followings, we set the total number M of remote stations at 2000 stations, anddP = 0.01 [sec], dW = 0.02 [sec], dD = 0.03 [sec], anddA = 0.03 [sec], respecvtively. Moreover, the average
duration time of meteor bursts is set at 0.3[sec]. In asimulation, transmission-and-reception swichover times and the radio wave propagation time a are taken into
consideration as s = 0.003 [sec] and a = 0.0063 [sec].
Figure 6 shows T̄all versus 1/l. The dashed line in
the figure expresses the analytical value when the DPtransmission probability p is fixed at 0.1. The solid line
expresses the analytical value when p is controlled tothe optimal value which is given by the equation (20).
Marks ◦ and 2 are simulation results for the cases of
p = 0.1 and popt , respectively.
The figure shows that both simulation results arewell approximated by their the analytical curves. In

In this paper, we have proposed a data transmission
protocol suitable for a remote monitoring system using MBC network named random transmission scheme.
We also have shown the optimal control method for
DP transmission probability in the random transmission scheme.
By introducing random transmission in global
polling scheme, we have succeeded to mitigate the DP
collision problem. We evaluate the delay time required
to collect monitoring data from all the remote stations
by numerical analysis and computer simulations. By
controlling the DP transmission probability at the optimal value, the probability of the DP collision is further reduced and thus the performance improvement isacheived.
References[1] J.Z.Schanker，Meteor Burst Communication，Artech House，Boston 1990．
[2] A. Fukuda, Meteor burst communications，Corona
Publishing, Tokyo, 1997 (in Japanese).
[3] K. Mukumoto, A. Fukuda, “Data Collecting System with Unattended Sensors via Meteor Burst
Channel, ” IEICE Trans. (B)，vol.J68-B，no.6，
pp.670-677，June 1985 (in Japanese).
[4] M. Nagasawa，K. Mukumoto, A. Fukuda, “Analysis of Data Collection System via Meteor Burst
Channel, ”IEICE Trans.(B-I)，vol.79-B-I，no.6，
pp.424-435，June 1996 (in Japanese).
[5] M. Nagasawa, K. Mukumoto, A. Fukuda, “Properties of Meteor Burst Channel and Power Controlled
Probe, ” IEICE Trans.(B-II)，vol.J81-B-II，no.11，
pp.1038-1047，Nov．1998 (in Japanese).

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

Study on Go-Back-i-symbol ARQ Scheme
Joint Workshops
Shotaro ITOH† , Kaiji MUKUMOTO‡ , and Tadahiro WADA††

Faculty of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN‡Division of Technical Services,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN

Abstract
In this paper, we introduce of Go-Back-i-symbol-ARQ
scheme which is symbol-wise retransmission. The GBiARQ scheme is proposed in MBC, and the GBi-ARQ
scheme exhibits better performance than the GBNARQ scheme.
1. Introduction
Today, wireless communications are used in various
scenes. However, compared to wired communication
links, wireless communication channels suffer from anumber of problems such as ambient noise, fadings, interferences, etc. Thus, in general, we cannot use wireless communications without error control. Error control schemes can be classified into three categories: Forward Error Correction (FEC), Automatic Repeat reQuest (ARQ), and their combination, i.e., Hybrid-ARQ(HARQ). The schemes in each of these categories areactively studied for various communication systems.
In this paper, we present a sort of HARQ scheme
named Go-Back-i-symbol ARQ (GBi-ARQ), which was
proposed by our laboratory[1]. In conventional ARQ
schemes, data are transmitted in block manner andblock-wise retransmission is performed when error is
detected. In the GBi-ARQ scheme, transmission data
are sequentially encoded by a convolutional encoderand symbol-wise retransmission is achieved by evaluating the reliability of decoded data using modified
Viterbi Algorithm(VA) [3].
The GBi-ARQ scheme was proposed for Meteor
Burst Communications (MBC)[2]. MBC is one of beyond line of sight communication techniques whichuses reflections from ionized meteor trails called meteor
bursts. In MBC, channels open randomly and the received signal power passed through the channel mostly
exponentially decays with time. Due to intermittent
nature of the meteor burst channel, errors tend to occur
in the tail part of a packet. Therefore, the GBi-ARQ
scheme, which can evaluate the reliability of received
data and perform symbol-wise retransmission if the reliability is not enough, is very suitable for MBC.

Figure 1:
R=3,K=3

Example of the convolutional encoder,

The rest of this paper is organized as follows. Section 2 describes convolutional code, Section 3 describes
Viterbi algorithm. Section 4 explains GBi-ARQ schemeand shows the algorithm for GBi-ARQ scheme by
Viterbi algorithm modified. Secotion 5 shows the performance of GBi-ARQ shceme in MBC. Finaly, Section
6 concludes the paper.
2. Convolutional CodeIn this section, we briefly explain the forward error
correcting code used in the GBi-ARQ scheme, i.e., convolutional code. An encoder for a non-recursive binary
convolutional code consists of m shift-registers and v
modulo-2 adders. Input bits are shifted along the registers one bit at a time. Output bits are obtained byadding the current input value and/or values in specified registers. The maximum number of input symbols
affecting the current output value is called constraint
length K and it is given by m+1. Code rate for a convolutional code, R, is defined as the number of input
bits divided by the number of output bits at each time
step.
Figure 1 shows an example of the encoder for a
convolutional code with R=1/2 and K=3. Input bit
sequence u = (u0 , u1 , u2 , ...) is fed into the leftmost
register. Then, the elements of the output bit sequence

(1)

(2)

(1)

(2)

ci

(1)

=

ui ⊕ ui−2

(1)

(2)
ci

=

ui ⊕ ui−1 ⊕ ui−2(2)c = (c0 , c0 , c1 , c1 , ...) are obtained by the following equations:

It is convenient to denote the input and output sequences as polynomials:
U (D) = u0 + u1 D + u2 D2 + · · ·
and

(j)

(j)

(j)

C (j) (D) = c0 + c1 D + c2 D2 + · · ·

(3)
(4)

Then, by using generator polinomialsg (1) (D) = 1 + D2 , g (2) (D) = 1 + D1 + D2 ,(5)

Figure 2: Trellis diagrom for Viterbi algorithm

we can express the above operations as
C (j) (D) = g (j) (D)U (D).

(6)

Note that the convolutional encoder in Figure1 is specified by the generator matrix
G(D) = [g (1) (D), g (2) ] = [1 + D2 , 1 + D + D2 ].

(7)

3. Viterbi Algorithm
The GBi-ARQ scheme uses modified VA explainedin the next section. We thus review ordinary VA in thissection. The VA was proposed by Andrew J. Viterbi
as a maximum likelihood decoding algorithm for convolutional codes.
In the followings, we explain the algorithm by using
a simple example. Transmitted data are assumed to be
encoded by a convolutional encoder in Figure1. Usually, the registers start with all zero state and a code
sequence is terminated at all zero state by sending mdummy zeros. The trellis diagram corresponding theencoder is shown in Figure 2. In the figure, nodes S0 ,S1 ,S2 , and S3 correspond to internal states of the encoder. Branches expressed by solid and broken lines
correspond to code elements produced by input bit 0
and 1, respectively. The corresponding input bit andcode symbol are shown beside each branch. For example, 1/01 represents that a code symbol 01 is produced
by an input bit 0.
Each branch in the trellis diagram has a value called
branch metric. Branch metric expresses a log likelihoodof the code symbol of the branch for the received symbol. Path metric is calculated by adding the branch
metrics along the path. At each node at each level, VA
selects the path having the largest metric and discards
the other paths. The selected paths are called survivors. The decoder repeats the same operation until
the final level. Since codes are assumed to be terminated at all zero state, there is only one survivor at

the final level. The final survivor is clearly the pathhaving the largest metric, so that the code sequencecorresponding to the final survivor is the maximum
likelihood estimate for the received sequence.
Here, we consider the Viterbi decoder in a practical application. The code sequence is assumed to bemodulated by the binary phase shift keying (BPSK)
and transmitted over additive white Gaussian noise(AWGN) channel with one sided noise spectral density N0 . Let us denote the i-th channel symbol by xi
which takes values +1 or −1 corresponding to a code
symbol element 1 and 0, respectively. In this case, thenormalized output ri of the i-th received symbol fromthe optimum receiver is given byp(8)ri = 2E/N0 + ni
where E is a signal energy per symbol and ni is a Gaussian random variable with zero mean and unit variance.
It is known that xi ri serves as a sufficient measure forthe log likelihood function. Thus, we can use xi ri instead of lnP r(ri|xi).
In the example of Figure 2, the i-th code symbol is(1) (2)constructed by two elements,ci , ci .
Therefore, denoting the corresponding channel(1)(2)
symbol as xi = (xi , xi ) and its receiver output as(1) (2),ri = (ri , ri ) the branch metric corresponding to isgiven by(1) (1)mi (ci ) = xi · ri = xi yi(2) (2)+ xi yi .

(9)

4. GBi-ARQ scheme
The GBi-ARQ scheme was proposed by our laboratory as a symbol-wise HARQ scheme. In conventional ARQ schemes such as GBN-ARQ scheme, data
are transmitted in block manner and block-wise retransmission is performed when error is detected. On

Massage

K=7

MassageConvolutional encode

Convolutional encode

Block 1
Block 2Block 3
Packet in GBN-ARQ

Packet in GBi-ARQ

K=3

Figure 3: Packet structure in GBN-ARQ and GBiARQ schemes.

the other hand, in the GBi-ARQ scheme, transmission
data are sequentially encoded by a convolutional encoder and symbol-wise retransmission is achieved byevaluating the reliability of decoded data using modified VA as described below.
The modified VA is also explained by a trellis diagram. Similar to the ordinary VA, at each node on thetrellis diagram, a path with the largest metric among
the paths merging into the node is selected as a survivor. However, it is different from the ordinary VA
that the survivor survives with label X if the difference
between the metrics of the survivor and the others issmaller than a threshold U , a predetermined positive
constant. Label X indicates that the path is unreliable
and it is not removed as long as the path survives.
This procedure is continued until the best survivor,
i.e., the survivor with the largest metric at the current
level, is labeled X, then the receiver stops decoding
process at that level. Since the VA decoding is truncated on the way of processing, the decoder needs todetermine a node terminating the VA to decide the received data symbols in this transmission. After that,the receiver requests the transmitter to send the remaining data symbols in the next transmission. In the
GBi-ARQ scheme, the termination node is determinedby the following procedure: After stopping the VA atsome level, say t, the decoder firstly goes back d levels, where d is a predetermined integer, and then traceback all survivors from the nodes at level t − d to theircommon node. If the value d is properly selected, we
can expect that the correct path is survived at levelt − d, so that the common node is connected to thecorrect path with high probability. Note that U and d
are the parameters to optimize the performance of the
GBi-ARQ scheme.
5. Performance of GBi-ARQ scheme in MBC
MBC is one of beyond line of sight communication
techniques which uses reflections from ionized meteor
trails called meteor bursts. In MBC, channels open
randomly and the received signal power passed throughthe channel mostly exponentially decays with time.

Figure 4: Performance coparison between GBi-ARQ
and GBN-ARQ schemes.

Due to intermittent nature of the meteor burst channel, errors tend to occur in the tail part of a packet.
Therefore, the GBi-ARQ scheme is very suitable for
MBC.
In this section, we compare the performance of the
GBi-ARQ scheme with that of the GBN-ARQ scheme
proposed in MBC. The difference in the packet constructions of the GBi-ARQ and the GBN-ARQ schemesis described in Figure 3.
Figure 4 shows throughput performances of the
GBi-ARQ and the GBN-ARQ schemes. The curvesindicate the maximum achievable average transmitted
bits per burst as a function of initial SNR of a meteor burst channel. The decay time constant for a meteor burst is assumed to be 0.15 [s]. The packet isassumed to be encoded by a convolutional code with
R=1/2,K=5,7 and BPSK modulated with symbol rate
2400[baud]. The parameters, U and d, in the GBi-ARQ
scheme and block length in the GBN-ARQ scheme are
optimized for each initial SNR. We can see from thefigure that the GBi-ARQ scheme exhibits better performance than the GBN-ARQ scheme. Since the GBiARQ scheme employs symbol-wise retransmission, it
can use MBC channel more effectively.
6. Conclusions
In this paper, we present GBi-ARQ scheme proposed by our laboratory. In the GBi-ARQ scheme,transmission data are sequentially encoded by a convolutional encoder and symbol-wise retransmission isachieved by evaluating the reliability of decoded datausing modified VA. The paper explains the modified
VA used in the GBi-ARQ scheme together with generalconvolutional codes and ordinary VA. The performanceof the GBi-ARQ scheme in MBC is compared withthat of the GBN-scheme. Then, it is shown that theGBi-ARQ scheme has better throughput performance
than the GBi-ARQ scheme. However, other than MBC,
there are many wireless communication systems whichsuffer from unexpected disconnections due to suddennoise increment, abrupt signal fading, interference andso on. Thus, in the future study, I am thinking to apply
the GBi-ARQ scheme to such communication systems
and evaluate the performances.
References[1] K.Mukumoto, N.Ngashima, and A.Fukuda : Centralized data collection systems via meteor burst,
IEEE Trans., vol.J67-B,no.5,pp.489-496,May 1984.
[2] J.Z.Schanker : Meteor Burst Communicatioins (injapanese), Tokyo:Corona Publishing Co., 1997.
[3] H.Yamamoto and K.Itoh, : Viterbi decoding algorithm for convolutional codes with repeat request, IEEE Trans.Inf.Theory, vol.26, no.5,pp.540547,Sep.1980

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

Parallel Transmission Wireless
Visible Light Communication Systems using Color Shift Keying
Haruki Furukawa† , Tadahiro Wada † , Kaiji Mukumoto‡†

Graduate School of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email: {f0330134,tetwada}@ipc.shizuoka.ac.jp

Abstract
In this paper, we show an eﬀect of Color-Shift Keying (CSK) on parallel-transmission wireless visible light
communication (PT-VLC) systems. The CSK scheme
can improve information transmission rate of PT-VLC
systems compared with that using mono-color. In thisstudy, we construct a prototype of a PT-VLC systemusing a liquid crystal display as a transmitter and experimentally evaluate an eﬀect of the CSK. We also
show an eﬀect of a forward error correction code for
PT-VLC.
1. Introduction
Visible light communication(VLC) is a communication system using the visible light as a carrier. VLC
has many advantages. It has little influence to humanbodies or precision instruments compared with otherwireless radio communications [1–3]. It can performsecure communication because it has a visible irradiation range. VLC allows to use a light source as not
only the illumination but also the transmitter. There
are many devices used for transmitter of VLC. Liquid
Crystal Display(LCD) is one of candidates . The LCD
is widely used on various situations, such as a computer, a television, a smart phone, and a tablet PC.
Furthermore the LCD can easily control strength of luminescence and use many colors.
For VLC systems, there are two standard devices
for receivers, photo-diodes (PD) and image-sensors(IS). PDs are popular devices and have quick electronic
response, hence they allow fast communications. However, PDs have wide field of view, therefore VLCs using
PDs are seriously aﬀected by optical disturbance. On
the other side, VLCs using ISs (ISC) restore information by image processing. This characteristic enables toclear optical disturbance. Moreover, since ISs are implemented as a digital camera in various devices suchas cellular phones today, systems employing ISC and
IC together (ISIC systems) are expected to be utilizedeasily.

‡Division of Technical Service,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email: tekmuku@ipc.shizuoka.ac.jp

Table 1: Product Specifications of the LCD
Name of product
FTD-G722AS2
Display size
17[inch]Resolution
1280×768
Input signal
analog RGB
Power consumption
23[W]One disadvantage of the ISIC systems is that popular ISs have very low frame rate such as 30-60[fps],
which limits a transmission rate. In order to overcome
this problem, parallel transmissions are quite eﬀective.
The parallel transmissions can be achieved by using the
LCD. At the receiving side, parallely transmitted data
can be obtained by image processing.
Color Shift Keying(CSK) is alternative technique
to increase transmission rate. If ON-OFF keying isused for VLC, one light source can transmit only 1 bit.
However, if we can use 3 colors, Red, Green, and Blue,one light source can transmit more information.
In this paper, we construct a prototype of a parallel transmission ISC system using an LCD and a USB
camera, and evaluate the performance of this system
by experiments. Especially, the eﬀectiveness of CSK isevaluate by experiments.
2. System Construction
This section describes the structure of the transmitter and receiver of our ISC system. Fig. 1 shows
the constructed ISC system. This ISC system transmits digital information by controlling brightness of
the LCD, then restores the information by capturing
the brightness of the LCD and image processing.
2.1. transmitterThis system uses an LCD shown in Fig. 2(a) as atransmitter. The specification of the LCD is describedin Table 1.

Transmitter
FEC
EncoderInterleaver
Start Code
Generator

Color Shift
KeyingLED
MatrixPosition Signal
Generator
ChannelReceiverHeader
Detection
Output
Data

FEC
Decoder

M[box]

Input
Data

Image
Processing

Image
SensorN[box]

Deinterleaver

Figure 3: An Example of a frame
Figure 1: Block Diagram of the PT-ISC System
Table 2: Specification of the USB Camera
Name of product
Lumenera Lu75C
Resolution of images
640 × 480
Gradation
8[bit]
Frame rate
60.0[fps](a) LCD(b) USB camera

Figure 2: Experimental Equipments
An example of the transmission pattern of information by an LCD is shown in Fig. 3. As shownin Fig. 3, the information is transmitted by M × N
boxes, and each box consists of some liquid crystal pixels and displays a color according to the information
bits. The area of M × N is called as a frame in thispaper. Here, two variables, m and n, indicate the coordinate of each box, where m and n are integers andsatisfy 0 ≤ m < M and 0 ≤ n < N , respectively. For
example, the BOX in the 3rd column at the 0th row isrepresented as BOX(0,3) .
The LCD is used by this research consists of three
primary colors of red, green, and blue on one liquidcrystal pixel. Three variables, r, g and b, denote lightintensity of the LCD and have a range from 0 to 255(8 bits). By using the three primary colors, we can
express various colors by mixing these three colors.
In this manuscript, we only use two colors, red andblue. Futhermore, we set the light intensity of twocolors, r and b, at 0 or 255. Therefore, each box isexpressed by 4 colors, black, blue, purple, and red.
The sets of light intensity, (r, b) are (0, 0), (0, 255),(255, 255) and (255, 0), respectively.
Moreover, M-sequence is used for the signal of theposition detection of the information area on the LCD[3]. Because M-sequence is expected for reliable posi-tion detection, it has the very excellent auto-correlation
characteristic. The signal of BOX(1,1) ，BOX(1,N ) ，
BOX(M,1) and BOX(M,N ) located in four corners of theframe is made into the signal for position detection.
2.2. ReceiverIn this ISC system, a USB camera (as an IS) shownin Fig. 2(b) is used a receiver. The specification ofthis camera is shown in Table 2. We adopt the camera whose specification is approximately the same as
popular cameras. The captured image is then fed to apersonal computer for image processing.
3. Restoration Algorithm
Fig. 4 shows the flowchart of the information
restoration algorithm at the receiving side of this system.
3.1. Capture
In the first step, the receiver captures an image withheight I (0) and width J (0) involving the LCD named
IM G(0) . Fig. 5(a) is an example of IM G(0) , whose
I (0) =480[pixel] and J (0) = 640[pixel], respectively. In
this paper, the pixel in the j th column of the ith row(α)
of IM G(α) is represented as Pi,j , where the variables
i and j satisfy 0 ≤ i < I (α) and 0 ≤ j < J (α) , re(0)spectively. Since IM G(0) is the color image, Pi,j has
3-color channels, RED, GREEN, and BLUE, of whichCapture
IMG(0)

IMG(0)

Matched Filtering
Median Filtering
Minimum Distance Detection
IMG(0)
Perspective Transformation

Position Detection

Normalization(a) Captured image(b) Median-filter image(IMG(1))
tl

tr

bl

P,P ,P ,P

br

IMG(2)
Color Estimation(c) Position detected image (d) Perspective transformed
image

Figure 4: Flowchart of the Restoration Algorithm
the received light intensities are represented as R(i,j) ,G(i,j) , and B(i,j) , respectively.
3.2. Position Detection

Figure 5: Sample Images on the Restoration Process

Next, the position of information area, which isframe, in the captured image should be determined.
As mentioned earlier, M-sequence is used for the detection of the information area. The detection algorithm
is almost the same as shown in Ref. [3].
3.3. Perspective Transformation
The shape, location and size of the information area
on IMG(0) depend on relative position of the LCD andthe USB camera, that is, the region of the information
area is not constant on IMG(0) . In order to easily detect transmitted information, the region of informationtransforms to a fixed size rectangle. Perspective Transformation is adopted for this transformation, as show inRef. [3]. The transformed image, IMG(2) , is described
as Fig. 5(d), whose size, I (2) × J (2) , is 640 × 480 in thisstudy.
3.4. Color Estimation
As the final process of the information restoration,
the transmitted symbols are estimated by detecting colors of the boxes. In order to identify all boxes, IMG(2)is evenly partitioned into M × N areas as shown in
Fig. 5(e). Each partitioned area is named a “cell”.
The height of a cell, ch , and the width of it, cw , aregiven by
ch =

J (2)
I (2)
and cw =,MN(e) Partitioned-image(1)

respectively. The cell of (m, n) is written as CELL(m,n) .
The transmitted light intensities, r and b, of boxes
can be estimated from the internal state of CELL(m,n) .
Here, we define received light intensities, R(m,n) andB(m,n) , which is derived as,
R(m,n)

c∑
hm

≡

cw n
∑

(2)

(2)

(2)

(3)

R(i,j) , andi=ch (m−1) j=cw (n−1)

B(m,n)

c∑
hm

≡

cw n
∑

B(i,j) ,i=ch (m−1) j=cw (n−1)
(2)

(2)

where R(i,j) and B(i,j) are light intensities of IMG(2) .
In order to perform the color estimation of eachsymbol, the Euclid distance d on a color space is defined. The Euclid distance on the color space is foundfrom Eq.(4). The receiver detects the color having thesmallest Euclid distance. The set which shows a color
is C = {′′ Black ′′ ,′′ Blue′′ ,′′ Red′′ ,′′ P urple′′ } and c isthe element c ∈ C.
√c )2 + (Bc2dc = (R(m,n) − Rave
(4)(m,n) − Bave )cc
Rave
and Bave
within Eq.(4) are called on reference
points. The reference points are initially determinedby using a frame negotiated by both the transmitterand the receiver. An example of sets of received lightTable 3: Details of Luminescence Patterns by the LCD
Display pattern
The number of colors

The number of boxes
per frame (M × N )The number of pixels
per box (ch × cw )The number of
total pixels

Figure 6: An Example of Cell State Values, R(m,n) andB(m,n)(a) 拭璽

1(b) Pattern2

Pattern 1
4(black, blue,
red, purple)
512(16×32)225(15×15)
115,200

Pattern 2
4(black, blue,
red, purple)
253(11×23)441(21×21)
111,573

Pattern 32(black,blue)
512(16×32)225(15×15)
115,200

Table 4: Parameters of the Turbo Code
Constraint length3
Generator matrix(7,5)
Coded rate
0.50
Word length
Information:1522[bit]Code:3048[bit]Decoding
10[time]In this experiment, since four corner boxes are used
for the position detection, the number of boxes which
can transmit information are M × N − 4. Because eachbox of Pattern 1 and 2 can transmit 2 bits, the amountof information bits per frame is 1016 [bit/frame] byPattern 1 and 498 [bit/frame] by Pattern 2, respectively. In Pattern 3, one frame can transmit 508[bit/frame] because this pattern uses only two colors.

(c) Pattern3

4.2. Introduction to FEC
Figure 7: Luminescence Patterns by the LCD
intensities, (B(m,n) , R(m,n) ), are shown in Fig.6. We
can found that the sets of intensities are concentrated
on four points corresponding to four colors.
4. Experiments
4.1. Luminescence Patterns of the Frame
The luminescence patterns of frame is assumed
three patterns shown in Fig. 7. The frame construction of each pattern is shown in Table 3. As shownin Table 3, these three patterns use the almost equivalent LCD pixels for information transmission. Because
the number of boxes of Pattern 1 is twice as many as
one of Pattern 2, Pattern 1 transmits information twice
as many as Pattern 2. On the other hand, since theboxes of Pattern 1 have half LCD pixels by comparing
with Pattern 2, Pattern 1 is more influenced from adjacent boxes. Moreover, because the luminescent colorsof Pattern 1 are twice as many as one of Pattern 3, Pattern 1 can transmit twice information bits comparing
with Pattern 3.

As well known, Forward Error Correction (FEC)
codes are useful for reliable communication. Although
FEC codes decrease a bit rate, reliability of communications increases. In this paper, we adopt a Turbo code
as a FEC code. The specification of the Turbo code isshown in Table 4.
4.3. Conditions of Experiments
Assumptions of this experiments are shown in Table
5. We set four experimental conditions namely ExA,
ExB, ExC and ExD, as shown in Table 6. ExB, ExC,
and ExD have an almost the equal information transmission rate. ExA can achieve a twice as much information transmission rate as ExB, ExC and ExD.
4.4. Experimental Results
Fig.8 shows the BER performance vs. communication distance which is the distance between the LCD
and the USB camera. The results of ExA and ExD exhibited almost the equal BER characteristic. It meansthat CSK have large capability because ExA can transmit information twice as many as ExD. Next, since ExB
utilized the turbo code, as compared with ExA, BER

Display Pattern
FEC
Bit rate
Symbol rate
Code Rate
Start code

Table 6: Experimental Conditions
ExA
ExB
ExC
Pattern1
Pattern1
Pattern2
UncodedTurbo encoded
Uncoded5080[bit]
5080[bit]2490[bit]2540[symbol]
2540[symbol]
1245[symbol]
1.00
0.50
1.00
508[symbol]
508[symbol]
506[symbol]

Table 5: Experimental Assumptions
Environment
indoors,830[Lux]
Communication distance
100-250[cm]
Resolution of the camera
640 × 480
Position detection code length, K
15[bit]Frame rate
5.0[fps]Number of trials
1000[time]

ExD
Pattern3
Uncoded2540[bit]2540[symbol]
1.00
508[symbol]

References[1] H. Sugiyama, S. Haruyama, and M. Nakagawa,“Experimental Investigation of Modulation method
for Visible-Light Communications,” IEICE Technical Report, OCS2005-19, May. 2005 (in Japanese).
[2] T. Yamazato, “What Visible Light Communication(VLC) can do, what VLC cannot do.”, IEICE Technical Report, RCS2011-49,
June. 2011 (in Japanese).
[3] J. Amano，T. Wada，K. Mukumoto, “ An Evaluation of a Parallel Transmission Visible Light Communication System employing an LDPC code, ”
Proc. International Symposium on Information
Theory and its Applications(ISITA 2012), pp. 566570, 2012

Figure 8: BER Performance using CSK
has been improved when the communication distance
was less than 200[cm]. We have can also find that the
BER characteristic of ExC was better than one of ExA.
It is because the area of boxes of ExC is larger thanthat of ExA and influence from adjacent boxes might
be reduced in ExC.
5. Conclusions
In this paper, we have constructed the prototype
of a parallel transmission ISC system using CSK. We
evaluated system performance by experiments. As aresult, we shown that CSK and FEC (Turbo code) werevery eﬀective for ISC.

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

A Study on Color Error for Color Shift Keying in Parallel Transmission Visible
Light Communications Systems
Tin Nguyenthuong† , Tadahiro Wada† , Kaiji Mukumoto‡
Graduate School of Engineering,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email: {f0330298,tetwada}@ipc.shizuoka.ac.jp†

Abstract
In this paper, we deal with Visible Light Communication(VLC) Systems. VLC is a communication systemusing visible light and have some advantages. Color
Shift Keying is a candidate technique to increase transmission rate of VLC. The paper provides evaluationof color error on VLC using CSK. The evaluation iscarried out from the view points of experiments andtheoretical analysis.
1. Introduction
Visible light communication (VLC) is a communication system using the visible light as a carrier. Rangeof wavelength of it is 380nm-800nm. VLC has many
advantages as follows:• Not be limited by radio law. The radio waves
are limited by the Radio Law, and it is definedas electromagnetic waves below 3[THz] in Japan.
However, the wavelength of visible light is 400800[THz] in frequency band, that it is outside
the control of the Radio Law.
• Little effect on precision equipments and humanbodies. Because VLC does not use the radio
waves, it can be used in environments that arestrictly limited to usage of radio waves such as
hospitals, trains and planes.
• High operability and security. Because we cansee the visible light by naked eyes, we are able tocontrol it easily. Therefore, VLC has the strong
security and high operability.
On the other hand, the VLC still exists some disadvantages:• Because wavelength of visible light is short, VLC
does not have effect on diffraction, scattering andreflection. Therefore, it can not transmit information when meeting a shield.

Division of Technical Service,
Shizuoka University
3-5-1 Johoku, Hamamatsu, 432-8561, JAPAN
Email: tekmuku@ipc.shizuoka.ac.jp‡• VLC is much affected by noise such that naturallights, artificial lights and so on.
With the light source which are used in the VLC,
the light emitting diode (LED) has been noticed. Comparing with a fluorescent lamp and an incandescentlamp, it has a lot of advantages, such as low power
consumption, long life, height of the visibility, small
size and lack of heat generation. Therefore, it is widely
applied in a variety of locations in traffic-light apparatus, electrical scoreboards, etc.
Moreover, PIN photodiode, avalanche photodiodeand image sensor are the typical light-receiving devices
in VLC. The PIN photodiode and the avalanche photodiode are the general light-receiving elements. They
are possible to detect the intensity-modulated signal toGHz. The image sensor has very high spatial resolution, which means that the number of pixels of the image sensor is the number of maximum channels. Therefore image sensor communication allows parallel communication. Moreover, this scheme has very strong
tolerance for disturbance by eliminating another lightsources from the received image. However, the frame
rate of the standard image sensor is 30-60[f ps], whichis too slow to use it as the receiver. Nevertheless, image
sensor communication has been developed more popularly than PIN photodiode, because image sensor communication has above-mentioned unique features andmost cellular phones are equipped with an image sensor as a digital camera.
Red, green and blue are the three primary colors in
the LED. They are also able to produce different colors
if we change the mixing ratio of the light from them.
In 1960, the red LED was born, and then the green
LED was invented. However, it was difficult to develop
the blue LED, because it needed a high technology. In
1990s, the blue LED was invented. After it was born,
the color LEDs are widely used in various applications.
For example, many of the huge screen on the wall surfaces of the tall building started to use LEDs.
In our laboratory, researches on VLC have been
done. In most of the researches, one color of LEDs

is considered. However, comparing with one color, theusage of three colors is expected to transmit more information. Therefore, we consider to use three colors
for transmissions, which is called as Color Shift Keying(CSK). We evaluated color error rate of CSK in viewof theoretical analysis and experimental results.
2. Experiments
2.1. The system configuration
In order to obtain experimental data, we configure
the systems shown in Figure 1.

2.2. Experiment results
In the transmitter, the numbers of samples of Red,
Green and Blue are 2070, 2090 and 3240, respectively,
as shown in Table 1. The distance between the USB
camera and the LED matrix is 200cm.
Table 1: The samples of transmitter
Color
Numbers
Blue
2070
Green
3090
Red
3240
Total
8400

At the receiver, even when sending one color, we obtained three colors of luminance intensity, Red, Greenand Blue. The luminance intensity ranges of them is0-255.
Figure 2 shows the method to determine the color
error rate when transmitting Blue. It is similar to thecases that we transmit Green and Red.

Figure 2: Detection of color error when transmitting
Blue
Table 2 shows the probability of color errors by obtaining from the experiments.

Figure 1: The system configuration
At first, from a micro computer driven by Peripheral Interface Controller (PIC), signal is fed to an LED
matrix. Next, the LED matrix displays lighting patterns according to the signal. Then, an image sensor
installed on an USB camera captures the display of the
LED matrix. Lastly, the captured image is fed to acomputer and image-processed to obtain the transmitted data.

Table 2: The probability of color error by experiments
Reception
Transmission
Blue
Green Red
Blue
―
0
0
Green
1.78×10−2―0Red
3.12×10−20―

From Table 2, when sending the Blue, no color error
occurs. However, when sending the Green and Red, we
can see that color errors to Blue occur.

3. The mathematical model of luminance intensity

Figure 3: State of the lights which arrive at pixel of thereceiver3.1. Way of thinking about luminance strength
Making a mathematical model of luminance intensity is important to recognize physical meaning of reception and to enable analysis easily. In figure 3, thereception of lights are roughly described when a “Blue”
color LED light is front of a pixel of the receiver.
Note that other LED lights, “Red”, “Green” and also“Blue”, exist in the neighborhood of the Blue light.
As mentioned earlier, even when we send a blue
color light, we obtain the three of luminance intensity,
Red, Green, and Blue at the receiver. In this case, thefront “Blue” color light is a direct light. And neighboring lights, Red, Green and also Blue, can be considered
as scattering lights. Furthermore, illumination lamps
are also affected and can be considered as an ambient
light.
From this figure, we can see that, at the pixel ofthe receiver, the Blue component consists of the direct light, the scattering lights, and the ambient lights.
However, the Green and Red components consist of
only the scattering and ambient lights. Therefore, we
use Nakagami-Rice distribution to approximate for the
luminance intensity of “Blue”, and Rayleigh distribution to approximate for that of “Green” and “Red”.
Both Nakagami-Rice and Rayleigh distributions aredescribed in Appendix.
3.2. Results of approximation

Figures 4, 5 and 6 show probability density function of luminance intensity when sending Blue, Green,
Red, respectively, and approximation results of them.
The horizontal axis shows the luminance intensity ofreception, and the vertical axis shows the probability
density function(PDF). The PDF of the luminance intensity can be obtained by the experiments shown in
the previous section.
In order to obtain estimates of the approximation,
ML estimation is used for Rayleigh distribution to statistically obtain average and variance from the experimental data. However, because it is difficult to obtain
the estimates for Nakagami-Rice distribution, simple
average and unbalanced variance of the experimental
data are calculated to obtain the estimates.
We can see that the shape of graphs of experimental
data and graphs expressed by approximation are nearly
similar. In figure 4, since the characteristic which represents the intensity of the Blue does not overlap with
the intensity of the others, the color error rate is lesswhen transmitting blue. On the other hand, in figures
5 and 6, the characteristic which represents the intensity of Green and Red overlap significantly with otherintensities. Therefore, when transmitting Green andRed, the color error rate tends to be high.
3.3. Probability of color error rate by theoretical analysis
By an appropriate approximation of the luminanceintensity, we can easily and mathematically estimate
color error rates. Here we derive equation of the error
rate by using Nakagami-Rice distribution and Rayleighdistribution.
As expressed in Appendix, probability density function of Nakagami-Rice distribution is as follow,
! "
2 +A2
r − r 2σrA
2
1 I
PN (r) = 2 e
.
(1)
0
σ1
σ12
Probability density function of Rayleigh distribution is as follow,
2

PR (r) =rr − 2σ
2
e 2.
2
σ2

(2)

Let a random variable r1 follow the NakagamiRice distribution and a random variable r2 follow theRayleigh distribution. Probability that r2 is greaterthan r1 can be expressed is as follow,
# ∞
# ∞
P =
PN (r1 )PR (r2 )dr2 dr1 .
0

=

r1

2
− A2
2σ1

1
e
σ12

2 2
σ12 σ22 − A2σσ22 (σ12 +σ22 )1e.
σ12 + σ22(3)

We use this equation to find the probability of color
error rate. The result is as shown in Table 3.

Table 3: The probability of color error by theoreticalanalysis
Transmitter
Blue
Green
Red

Blue
―
2.83×10−2
1.84×10−2

ReceiverGreen
2.96×10−3―
1.72×10−4Red
7.1×10−5
3.51×10−3―

By comparing the results between Table 2 and Table 3, we can find:• When sending the blue, the error rate is low.
• There is no significantly different between the results of the error rate by the experimental results and theoretical analysis. Therefore, it isreasonable when using Rayleigh distribution andNakagami-Rice distribution to approximate.
4. Conclusion
This research, we examined color error rate inparallel transmission visible light communication
when using CSK. Firstly, we found the probability of color error rate by experimental results.
Next, we determined the mathematical model ofthe luminance intensity of reception characteristic and found the probability of color error rateby theoretical analysis. Finally, we compared theresults of probability of color error rate which
are found by experimental results and theoretical analysis.
References[1] S.Stein, J.J.Jones, Modern Communication
Principles, McGRAW HILL, 1967.
A. Rayleigh distribution
Rayleigh distribution is the distribution of theamplitude x + jy whose coordinates x, y are independent, with identically distributed N (0, σ)[1].
Probability density function f (x) of the randomvariable x is given by the following equation,!
"
1
x2
f (x) = √exp − 2 .
(4)
2σ
2πσ
Similarly, the probability density function$ of 2the%y
1random variable y is f (y) = √2πσ
exp − 2σ.
2&Here, we have r = |x + jy| = x2 + y 2 . By setting x = r cos φ, y = r sin φ, and the probability
density function f (x, y) is given by the following
equation,''' ∂x ∂x '' ∂r ∂φ 'f (r, φ) = f (x, y) = f (x)f (y) ' ∂y ∂y '' ∂r ∂φ '
!
"
2
r
r
=
exp − 2 .
(5)
2πσ 2
2σ
By integrating over from 0 to 2π with relating
to φ, the probability density function f (r) withthe random variable r is given by the following
equation,
!
"
# 2π
r
r2
f (r) =f (r, φ)dφ = 2 exp − 2 . (6)σ2σ0B. Nakagami-Rice distribution
We give the probability density functions f (x)and f (y) as follow,!
"
1(x − A)2
f (x) = √
exp −
. (7)
2σ 2
2πσ
!
"
1
y2
f (y) = √exp − 2 .
(8)
2σ
2πσ
We focus on the magnitude& of r which is expressed as r = |x + jy| = x2 + y 2 . By settingx = r cos φ and y = r sin φ, probability density
function, f (r, φ), is given by the following equation,f (r, φ) =
=

'
' ∂x
' ∂r
fx fy ' ∂y
' ∂r

'

∂x '
∂φ '
∂y '
'
∂φ

!
"
r
A − 2Ar cos φ + r2
exp −
2πσ 2
2φ2
(9)

By integrating over from 0 to 2π with relating
to φ, the probability density function f (r) withthe random variable r is given by the following
equation,# 2π
f (r) =f (r, φ)dφ
0
!
" ! "
r
A2 + r2
rA
=
exp −
I0
.(10)
2
2
σ
2σ
σ2
Here, f (r) is defined as the probability density function called Nakagami-Rice distribution,

where Io () means Modified Bessel function of thefirst order,! 2π
1
I0 (z) =ez cos θ dθ.
(11)
2π 0

Figure 4: The probability density function of luminance intensity when sending Blue

Figure 5: The probability density function of luminance intensity when sending GreenFigure 6: The probability density function of luminance intensity when sending Red

Contempo: A Home Care Model to Enhance the Wellbeing ofElderly Pople
Kurnianingsih-IEEE Member, Lukito Edi Nugroho, Widyawan, Lutfan Lazuardi, Ridi Ferdiana, Selo

Abstract— Fast growing population of elderly people has
recently been a serious issue in many developing countries andit becomes a global concern in the world. Most elderly people
require assistance in their daily life, including in maintaining
their wellbeing, taking care of their health, or responding toemergency medical situations. The need for caring for elderly
people, particularly to maintain their wellbeing, has been
growing significantly. The objective of this research is to seek
improvements in quality of life that can enhance the wellbeingof elderly people through innovations in information
technology. This research is also expected to be a significant
contribution for dealing with ageing population as a common
issue in developing countries. This paper deals with a pervasive
health care technologies and ambient environment, which
propose a model of Connected Technologies for Home Care forElderly People (Contempo) to enhance the wellbeing of elderly
people. A model of wellbeing assessment is also proposed to
examine the proposed model of Contempo.

I. INTRODUCTION
The population of aging people has recently been gaining
attention from many countries. Due to the uniquedemography profile, developing countries tend to have moreelderly people compared to developed countries. United
Nations data predicted that by the year 2019 elderly
Indonesian’s ratio will reach about 7% of the population andwill rise from 7% to 14% in 2041 [1].
A major concern for elderly people is their health care.
Healthcare for elderly becomes a very crucial issue in many
developing countries, because if it is neglected, poor and sickold people will indeed become burdens to their families andthe government. Continuous urbanization in which people
from rural move to urban area has made most elderly people
will eventually live in urban areas. Elderly people are at highrisk for disease and disability. The problem of rush, bustle ofactivity, and crowded health services in urban areas are thefactors that sometimes make elderly people become difficult
to go out of their home to meet health professionals forroutine clinical check-up. Their disability to access publichealth services brings up a problem in their medical care.
Additionally, most elderly people are still unwilling to enter
nursing homes. They prefer to have the convenience andcomfort of staying at home. Contempo which implement theKurnianingsih is with the Dept. of Electrical Engineering andInformation Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
and also with Dept. of Electrical Engineering, Politeknik Negeri Semarang,
Indonesia (e-mail: kurnia.s3te13@mail.ugm.ac.id).
Lukito Edi Nugroho, Widyawan, Ridi Ferdiana, and Selo are with theDept. of Electrical Engineering and Information Technology, Universitas
Gadjah Mada, Yogyakarta, Indonesia (e-mail: lukito@ugm.ac.id;
widyawan@ugm.ac.id; ridi@ugm.ac.id; selo@ugm.ac.id).
Lutfan Lazuardi is also with the Dept. of Public Health, Universitas
Gadjah Mada(e-mail: lutfan.lazuardi@ugm.ac.id).

concepts of pervasive computing technologies and ambientenvironments is a solution to deal with this problem.
Contempo can be defined as a dwelling that connects theresidents, household appliances, environments, sensor andservices, medical care givers, family member and allow them
to be remotely monitored or accessed. Such connected homehealth care which equipped an automated stand-alone
surveillance method and fully integrated within theenvironment is a real and urgent need, particularly for elderly
people who likes living autonomously. The monitoring canbe done remotely by health service providers such as
hospitals. Having the capability of remote health careservices can reduce human resources and response time incritical situations and finally enhance the wellbeing of elderly
people.
Enhancing the wellbeing of elderly people in this research
project will be concerned on the technological perspective
through Contempo, including metrics and evaluation criteria
which can be tested by elderly people. In this sense, we aim
at proposing a Contempo model, which is equipped with anautomated stand-alone surveillance method and is fully
integrated with the environment. We also aim at proposing awellbeing assessment model as a tool to assess Contempo
model, which is a technology solutions for immediate andconvenient medical and health services for elderly
community in order to enhance the wellbeing of elderly
people. Finally, this research project is also expected to be asignificant contribution for dealing with ageing population as
a common issue in developing countries.
The structure of this paper is as follows. Section 2 givesa description about related research work. Section 3
describes a proposed model. Section 4 presents challenges.
Conclusion is discussed in Section 5.
II. RELATED RESEARCH WORKThis research project deals with elderly people relevants
to regional common issues of population ageing [1]. The
issue of rapidly aging population in many countries has been
published by JICA on a Japanese study report [2, 3]. In
response to the aging population, Indonesia as one ofdeveloping countries has adopted a National Action Plan [4],which focuses on establishing and ensuring necessary
support for the elderly.
Lots of health problems faced by elderly people such as
chronic desease, disability, and fall make elderly needasisstant living. Many inventions [5,6,7] have beendeveloped to overcome elder’s health problems. WirelessSensor Network (WSN) and Radio Frequency Identification(RFID) in health care system has become a tremendous
effort as communication tools in health care [8,9] However,improving quality of life is one of the main concern for the
pursuit of wellbeing of elderly people.

give resume of diagnostic as a feed back manually to thesystem to improve the elderly health status and treatment.

Many researchers have work in wellbeing projects [1013]. The four domains of the framework of wellbeing has
been categorized as an individual wellbeing,family
wellbeing. community wellbeing and societal wellbeing[10]. Indicators as ‘satisfaction’, ‘anxiety’ and ‘happiness’
are used by UK government to measure people'ʹs‘individual’ and ‘psychological’[11, 12].

We define alert system of Contempo into five categories:

Home is the best environment for elderly people to getconvenience healthcare. By utilizing WSN and RFID,
medical caregivers also can remotely monitor the elderly
activities and physiological parameters. Elderly’s disability
to access public health services and elderly’s preference tohave the convenience and comfort of staying at home are astrong justification for developing a Contempo. It can
provide constant monitoring of their health and activities
despite their incapabilities and preference, which in turn will
enhance their wellbeing.
III. THE PROPOSED MODEL
In this section, an architecture of a proposed model ofContempo to enhance the wellbeing of elderly people is
proposed, as shown in Figure 1. Contempo is an ambient andpervasive system which is equipped an automated standalone surveillance method and fully integrated within theenvironment. It can provide constant monitoring theactivities and physiological parameters of elderly person,
recognizes changes in their health status, and has thecapability to alert health professionals medical care andfamily members.
The proposed model of Contempo, as shown in Figure 2,
consists of three main system, namely Reflective Sensing
System, Historical Alert System, Diagnostic Support
System. This three main system of Contempo will work inharmony simultaneously in order to enhance the wellbeingof elderly people, particularly elderly people withdissabilities or/and elderly people with chronic deseases.
A. Reflective Sensing System (RSS)RSS will provide automatically reflection of sensing
gathered from vital sign sensors, activity daily sensors, falldetection and tracking sensors, and ambient environment
sensors. Sensor will send data of the elderly in real timeabout health status and environment surrounding the elderly
as inputs to RSS. Output from this system will be sent
automatically to home appliances or/and personal healthappliances (particularly for elderly with chronic desease).
B. Alert System (AS)AS will provide alert to medical care and family
members related to elderly health status in emergency
condition.
C. Diagnostic Support System (DSS)
DSS will provide medical diagnostic to doctors to bemonitored and analyzed. The result of analyzing, doctor will

• Category 1: Wellness Improvement Condition
Condition that elderly has significant improvement on
wellness. We will define wellness of elderly by indexing
based on physiology of elderly and activity of elderly.
• Category 2: Critical Activity Condition
Condition that elderly needs special attention because of
doing critical activity. Critical activity leads to possibility of
having accidents such as position in bathroom, going tooutside home.
• Category 3: Minor Condition
Condition that elderly needs more convenience fromenvironment and some asisstances.
• Category 4: Major Condition
Condition that elderly in critical condition and needssome medical asisstance, such as falls which not resulting
injury, elderly remains awake.
• Category 5: Emergency Condition
Condition that elderly in emergency condition and needasisstance from emergency units. Emergency conditions
such as fall in the elderly and resulting serious injury.
Alert condition category 1 till category 3 will be sent only
to care giver and family member. Alert condition category 4
will be sent to doctor, care giver, and family member. Alertcondition category 5 will be sent to emergency unit, doctor,
care giver, and family member.
Connected Technologies for Home Care for Elderly
People (Contempo)
Wearable
Body Sensors
Ambient
Environment
Sensora

Reflective
Sensing
Systemb

Home
AppliancesdPersonal
HealthSupport
AppliancesjEmergency
Unit

e
g

f
h

Databasec

Alert
SystemkiCare GiverlnMedical Record
Personal Record

o
p
q
r
s

m
Diagnostic
Support
Systemt

FamilyMemberDoctoru

Figure 1. A Proposed Model of Contempo

The flows in connected home health care, as described
below.

a. Wearable_Body_Sensor_Data
Wearable_Body_Sensor_Data consists of data from vital
sign sensors, activity daily monitoring sensors, fall detection
sensor, and tracking sensors. Vital sign will monitor
physiological function of elderly, which includes data ofblood pressure, heart rate, respiratory rate, and bodytemperature. Activity daily monitoring sensors will monitor
elderly’s activity, which includes sitting, lying, and standing.
We will classify the state of activity into two categories
namely active state and passive state. Active state is a state
where the elderly in a active circumstance such as sitting
position and standing position. Both sitting position andstanding position are states where elderly is doing activities.
Lying position will be defined as a passive circumstance
where elderly is not doing activities, in this case elderly is in
an idle activity. Falls often happen to elderly that can cause
injury and fatal damaged, even death. Fall detection will
have to be able to accurately predict a fall of elderly so thatsome fast preventive measures can be taken to prevent any
serious injury from occurring. Elderly tracking system will
be able to track the location of elderly. When something
happened to elderly in dangerous condition such as fall, thesystem can detect the position of the person and will givealert to emergency unit, doctor, care giver, and family
member.

h. Historical_Alertb. Ambient_Environment_Data

m. High_Alert

Awaring context of environment for connected homehealth care is a crucial matter in the design of solutions forinhouse safety. Context information related to elderly
wellbeing such as location and health status will be obtainedby sensor infrastructure such as lighting, temperature, anddensity. The information can then be further processed andcombined with wearable body sensor data to give
appropriate condition of elderly health status.

The output of Historical_Alert_System will also be sent
as an alert to doctor if elderly is in major condition andemergency condition.

c. Trigger_Data
The processing result of Reflective_Sensing_System willproduce Trigger _Data reflectively to Home Appliances to
do some action to home appliances.
d. Trigger_Data
The processing result of Reflective_Sensing_System
will produce Trigger_Data to Personal_Health_Appliances
to do some action reflectively to elderly’s themselves.
e. Trigger_Data
The processing result of Reflective_Sensing_System willproduce Trigger_Data to Historical_Alert_System to dosome action related to alert condition of elderly.
f. Sensing_Data
The processing result of Reflective_Sensing_System
will produce Sensing_Data, which will be saved in database.
g. Sensing_Parameter
Sensing_Parameter is a historical data taken fromdatabase to be processed in Reflective_Sensing_System totrigger Home_Appliances and Personal_Health_Appliances.

Historical_Alert is data as a result fromHistorical_Alert_System and will be saved in database.
i. Historical_Data
Historical_Data is data taken from processed database as
an input to Historical_Alert_System.
j. Emergency_Alert
The output of Historical_Alert_System will be sent as analert to emergency unit if elderly is in emergency condition
only.
k. Alert
The output of Historical_Alert_System will also be sent
as an alert to care giver if elderly is in wellness improvement
condition, critical activity condition, minor condition, major
condition, and emergency condition.
l. Alert
The output of Historical_Alert_System will also be sent
as an alert to family if elderly is in wellness improvement
condition, critical activity condition, minor condition, major
condition, and emergency condition.

n. Historical_Data
Historical_Data is data taken from processed database as
an input to Diagnostic_Decision_Support processing system.
o. Diagnostic_Data
Diagnostic_Dataistheresultofprocessed
Diagnostic_Decision_Support that will be saved in
Database.
There
are
three
inputstoDiagnostic_Decision_Support
processing
system:Medical_Record_Data, Personal_Health_Record_Data, andResume_of_Doctor_Diagnosis.
p-q Medical_Record_Data
Medical Record (MR) contains information aboutelderly/patient’s health compiled and maintained by each ofhealthcare providers. Medical_Record_Data consists ofelderly health information including personal identification;
anamnese; laboratory, radiological, and other test record;
medication record.
r-s Personal_Health_Record_Data
Personal Health Record (PHR) contains information
about elderly/patient’s health compiled and maintained byelderly themselves. Personal_Health_Record_Data consistsof medical record information, which comes from healthcare
providers, and patient/elderly’s additional information,
which comes from individual such as preferences.

t. Diagnosis_Data
The result of Diagnostic_Decision_Support will be sent to
the Doctor as a Diagnosis Data. Doctor then will monitor
and analyze it.
u. Resume_of_Doctor_Diagnosis
Doctor’s analysis manually will be sent as a
Resume_of_Doctor_Diagnosis to improve processing in
Diagnostic_Decision_Support. The information related toelderly’s health status will periodically update by system anddoctor.
This Contempo model will be tested and assessed tomeasure level of wellbeing. We also proposed a wellbeing
assessment model, as shown in Figure 2, to test a proposed
Contempo model. Wellbeing assessment can be measurequalitatively, including satisfaction, anxiety, happiness [11,12]. We propose three components to assess wellbeing,
namely physical health, behaviour, and environment.
A. Physical Health
There are four variables to measure physical healthperiodically such as body temperature, blood pressure, heartrate, and respiration. Body temperature will be measured
quantitively in Celcius degree. Bood pressure will bemeasured quantitively in mmHg. Heart rate will be measured
quantitively in pulse/minutes. Respiration will be measured
quantitively in breaths/minutes.
B. Behaviour
There are two variables to measure behaviour
periodically such as duration of active state and event of fall.
Active state will be defined as a sitting or standing position
while doing activities. We will measure and analyze theduration of active state done by elderly in a day. Event offall will be measured every day and will checked the historyof fall.
C. Environment
There are three variables to measure environment
periodically such as lighting, temperature, and density.

Figure 2. A Proposed Model of Wellbeing Assessment

IV. CHALLENGES
Many healthcare services projects have developed deals
with aspects of well being through the concepts of pervasive
computingtechnologies
andambient
intelligent.
Technological enhancements can be beneficial to promoting
people’s wellness. Since health care for elderly people isbecoming an important issue in developing countries due torapid growth of elderly people population, development ofContempo system to enhance the quality of health andwellbeing of elderly people is of significant importance.
Elderly’s disability to access public health services andelderly’s preference to have the convenience and comfort ofstaying at home are a strong justification for developing aContempo system. There are some challenges in promoting
wellbeing of elderly through Contempo systems, such as (1)how to integrate three main sub systems of Contempo tobecome an effective and comprehensive system, (2) how
determine parameters for wellbeing index, and (3) how todevelop knowledge base to determine the appropriate
clinical decision support system. Such Contempo system,
which equipped an automated stand-alone surveillancemethod and fully integrated within the environment is
expected to be able to provide constant monitoring of theirhealth and activities despite their incapabilities andpreference, which in turn will enhance their wellbeing.
REFERENCES[1]

World Health Organization and US National Institute of Aging,
Global Health and Aging, October 2011.
[2] K. Oizumi, H. Kajiwara, and N. Aratama, Facing up the Problem ofPopulation Aging in Developing Countries, JICA, December 2006
[3] JICA, Aging Population in Asia: Experience of Japan, Thailand, andChina, March 2007.
[4] United Nations, Permanent Mission of The Republic of Indonesia to
The United Nations New York, New York, 29 April 2011.
[5] D. Popescu, R. Dobrescu, A. Maciuca, and R. Marcu, “Smart Sensor
Network For Continuous Monitoring at Home of Elderly Population
with Chronic Desease”, IEEE 20th Telecommunication ForumTELFOR 2012, Serbia, Nov 20-22, 2012, pp. 603-606.
[6] R.A. Ramlee, D.H.Z. Tang, and M.M Ismail, “Smart Home System
for Disabled People Via Wireless Blutooth”, 2012 IEEE International
Conference on System Engineering and Technology, Bandung,
September 11-12, 2012.
[7] W. Putchana, S. Chivapreecha, and T. Limpiti, “Wireless Intelligent
Fall Detection and Movement Classification Using Fuzzy Logic”,
IEEE The 2012 Biomedical Engineering International Conference((BMEiCON-2012)[8] N. S. R. K. Prasad and A. Rajesh, “RFID-based hospital real timepatient management system,” International Journal of Computer
Trends and Technology, Vol. 3, Issue 3, 2012, pp. 509-517.
[9] P.Dillon and T. Znati, “Towards an architecture for mobile
healthcare,” 37th Annual IEEE Conference on Local Computer
Network, Florida, 22-25 Oct 2012, pp. 260-263.
[10] V.L. Placa, A. McKnaught, and A. Knight, “Discourse on wellbeing
in research and practise”, International Journal of Wellbeing, Vol 3,
2013, pp. 116-125.
[11] Stratton, A., (2010). Happiness index to gauge Britain’s national
mood. The Guardian, 15 November, p. 20.
[12] Office for National Statistics (ONS). (2012). Analysis of experimental
subjective well-being data from the annualpopulation survey, April to
September 2011. Office for National Statistics (ONS).
[13] Hsinchun Chen, “Smart Health and Wellbeing”, IEEE Journal onIntelligent System, 2011.

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

Development of Embedded Gateway for Wireless Sensor Network and Internet
Protocol Interoperability
Guntur Dharma Putra, Sigit Basuki Wibowo, Bimo Sunarfri Hantono, Widyawan
Department of Electrical Engineering and Information Technology,
Universitas Gadjah Mada
Jalan Grafika 2, Yogyakarta, INDONESIA
Email: guntur.dharma@mail.ugm.ac.id

Abstract
Wireless Sensor Network (WSN) usage for buildings
and household has been increasingly popular because
it offers many benefits, such as home automation andhome surveilliance. Therefore, in order to increase
WSN flexibility usage, remote controlling which enables administration is needed. In fact, generally WSN
is controlled by a coordinator (sink node) which is located near the WSN area itself. This research proposes
integration of WSN and Internet Protocol (IP), thatenables remote controlling and administration throughthe Internet Protocol stack. This research utilizes thewireless local area network or commonly known as
WiFi. One of main components on this WiFi network
is Access Point (AP) that acts as node coordinator.
Furthermore, AP also serves as a gateway that connects multiple devices, that is connected to the AP, tothe Internet. Therefore, this research will develop asoftware which is going to be implemented to the AP
so that the AP has a capability as a gateway for both
WiFi network and several WSN protocols to the Internet.
1. Introduction
Wireless Sensor Network (WSN) is a form of distributed autonomous node network which is used formonitoring physical or environment condition such astemperature, sound, vibration, and humidity. Furthermore, it is possible to add an additional function to
every node, for instance input/output port which allows actuator controlling that bounds with anotherelectronic or electric device.
WSN usage in a building or household has been
increasingly popular since it offers many benefits. A
popular example of WSN usage in a household is homesurveillance, which allows realtime home monitoringin every corner of the house. Thus, the householders should not be worried about their house security,
I would like to thank both of my thesis supervisors, S B
Wibowo and B S Hantono.

because the WSN has made the home security much
more secure and better. Another example is home automation, a process of automatization of any regularroutine activities at home. For instance, turning on
lamps when the environment gets dark and turning on
the air conditioner when the house owner is about togo back home.
Generally, WSN is controlled by a sink node near
the sensor’s network area. Thus, the problem arrises if
there is a need of remote controlling which is required
when the controller is far from WSN area. The possible
solution of this problem is the use of Internet Protocol(IP) because it offers wider area and it is accessible
anywhere.
However, most WSN does not use the IP stack, but
their own proprietary communication protocol, such as
ZigBee and IQRF. Therefore, there is a need of a gateway which enables the WSN from multiple vendors to
be remotely accessible from the internet.
1.1. Research Objective
This research objectives is to carry out a research
about development of a software which will be implemented in an household wireless Access Point to serve
as a gateway that is able to integrate WiFi network
and a couple of WSN protocols with the internet.
2. Tools and Materials
This research employs TP-LINK MR3020 with
OpenWRT installed as an OS for the WiFi Gateway.
However, the developed software is not device-specific.
It could run on any WiFi router or even desktop computer. This software requires LAMP stack, Python,and USB to serial driver in order to operate. OpenWRT OS has more capability than the default routerfirmware, it is actually based on Linux kernel [6] andoptimized for embedded device. OpenWRT can also
serves an web server with LAMP Stack installed. Furthermore, it needs Python with PySerial library andUSB to serial driver in order to communicate withWSNs.
Two kind of WSN will be utilized in this research.
The first WSN is IQRF technology. IQRF consist of[4] IQRF sensor nodes, development board for uploading the program, and evaluation board for testing anddeployment. In order to work, IQRF needs to be programmed, and the program will run on top of IQRF
OS. In this research, IQRF is used to measure environment temperature. However, IQRF sensor nodes
support many features and it is not only limited fortemperature measurement.
In addition to IQRF, this research will also utilize
ZigBee using XBee Series 1, Arduino Uno, and Relay
shield. This second part is used to simulate home automation and remote controlling. A coordinator will
be connected to the WiFi router using XBee Explorer
USB cable and the nodes are Arduino Uno with Relay
shield and XBee plugged in.

Figure 1: WSN and IP architecture with single wirelessAccess Point.

3. Methodology
3.1. Architecture Design
The architecture design that will be adopted in thisresearch is illustrated in Figure 1, and 2 alleges architecture design in block diagram. The figure illustratesa system which contains two distinct WSN with its own
different protocols and a local wireless network (WiFi).
WSN protocols employed in this research are IQRF and
ZigBee. The research execution will be divided into
three Work Package (WP).
3.2. Working Plan
WP 1: Software Design
This part covers literature review which is mostly about
the Operating System for the desired embedded device.
The next step is the design of software that would be
implemented in the wireless Access Point (AP). This
implemented software has to be efficiently working toovercome the wireless AP’s computational limitation.
WP 2: Software Implementation
This Work Package carries out software implementation. Firstly, ensure that both WSNs are connectedto the internet as previously planned. Secondly, assure that WiFi network still runs smoothly after thedeveloped software has been implemented to the Access Point. Supplementary additional features can also
be added in this step.
This software utilizes Python programming language with PySerial library in order to read serial port
for communicating with the WSNs coordinator. For instance, the code listing for reading IQRF temperature
is listed in Listing 1.

Figure 2: System design in block diagram.

# Importing PySerial l i b r a r y ( s e r i a l )t o rea d s e r i a l p o r timport s e r i a l , s y s# xbee i s a v a r i a b l e that r e f e r s to
s e r i a l port :#path :/ dev / ttyUSB0#
baud r a t e :4800
xbee = s e r i a l . S e r i a l ( ’ / dev /ttyUSB0 ’ ,4800)# w r i t e c har ’ g ’ and g e t t h e r e t u r n e dtemperaturexbee . w r i t e ( ” g ” )print xbee . r e a d ( 1 0 )# close port
xbee . c l o s e ( )Listing 1: Python code to read IQRF temperature.
The Python source will be executed by the web application that is written using PHP programming lan-

guage. This web application will be called using AJAX
in order to get continous update from the IQRF temperature. The PHP code snippet is shown on Listing2.

Figure 4 shows the web application screenshot. The
web page allows relay controlling and temperaturemonitoring in a single page. User management andscheduling is also included in this web application.

<?php$command = ’ python / r o o t / i q r f . py ’ ;echo substr ( exec ( $command ) , 0 , 2 ) ;?>Listing 2: PHP code to execute Python script.
WP 3: Integration and Overall System TestingIf both WiFi and two proprietary WSN protocols areable to connect to the internet, this part will only coverentire system tesing. The testing process will also be
increased from laboratory scale to test bed scale. Testing in the test bed scale is carried out to ensure thatthe system will run according to the desired scheme.
4. ResultThis research produces a prototype of web application which enables users to control WSNs from several vendors. As seen in Figure 3, this research is conducted by using IQRF Wireless Sensor Network and
XBee. IQRF is used to measure the local environment
temperature, while XBee is connected to an Arduino
Relay for controlling the relay remotely with ZigBee
communication protocol.

Figure 4: Web application screenshot.

5. Conclusion
This research proposes an approach of software delopment that is intended for developing a software for
embedded home router to integrate Wireless Sensor
Network and Internet Protocol. This software will runon the top of OpenWRT operating system. By using
this software, users could remotely control WSN fromboth desktop or mobile web browser anywhere as longas there is a connection to the gateway.
The software developed from this research is notdevice-specific. It could run on any WiFi router oreven desktop computer. However, this software requires LAMP stack, Python, and USB to serial driverin order to operate.
Since this device consumes lesser energy than conventional dekstop computer does and supports task automation, this device is suitable to be implemented ina green building.
References[1] Spinar, R., et al., “Demo Abstract: Efficient Building Management with IP-based Wireless Sensor
Network”, , 6th European Conference on Wireless Sensor Networks. Cork, Ireland 11-13 February
2009.
[2] Adam Dunkels, Thiemo Voigt, Niclas Bergman,and Mats Jonsson “The Design and Implementation of an IP-based Sensor Network for Intrusion
Monitoring”, Swedish National Computer Networking Workshop, Sweden, 2004.

Figure 3: WiFi Router as a gateway is connected to
WSNs.

[3] Sigit B. Wibowo, and Widyawan, “Wireless Sensor Network and Internet Protocol Integration with
COTS”, 2013 AUN/SEED-Net Regional Conference in Electrical and Electronics Engineering,
Bangkok, Thailand, 2013.

[4] Online document, http://www.iqrf.org/, IQRF, accessed in March 2013.
[5] Widyawan, et al., “iHome: Low-Cost Domotic
for Residential Houses”, 5th AUN/SEED-Net Regional Conference on Information and Communications Technology (RCICT), Manila, The Philippines, 2012.
[6] Online document, https://openwrt.org/, accessedin March 2013.
[7] Online document, http://www.digi.com/technology/rfarticles/wireless-zigbe, accessed in March 2013.

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

The Impact of Nonlinear Fiber Propagation on the Integrated LTE (Long Term
Evolution) and RoF (Radio over Fiber) over Transmission Varying Span on High
Mobility Scenarios
Fakhriy Hario Partiansyah1,2 , Adhi Susanto1 , and I Wayan Mustika1
1

Department of Electrical and Information Engineering,
Faculty of Engineering Gadjah Mada University
Yogyakarta, Indonesia
Email: susanto@te.ugm.ac.id,wmustika@ugm.ac.id

Abstract
In the high way of metropolitan area, demands tilted
use communication voice and data mobility access onhigh mobility condition. The problem in providing
broadband communication (LTE) on high mobility access is not widespread due to the large inter- carrier interference, terrestrial obstacles eﬀect Doppler and multipath fading. Radio over- Fiber (RoF) techniques havebeen considered as an alternative for wireless interface
structure between eNB and RN in order to avoid severe
multipath fading. The aim of this research is propose
to investigation achieved condition throughput, degradation Error Vector Magnitude (EVM) LTE-RoF overvarying transmission span respectively 5-60 Km using
modulation QPSK, 16-QAM and 64-QAM by simulating. Research also investigation eﬀect of reducing theoverall system throughput for 30 Km/H and 120 Km/H
cases.
1. Introduction
In recent years, vehicular networks have attracted
a considerable attention from the research community
and the automotive industry aiming to provide not only
safety and eﬃciency in the transportation systems but
also leisure and infotainment to the driver and the passengers. However, these networks are challenging as
they have some speciﬁc characteristics, such as highvelocity of vehicles and the dynamic network topology,
that need to be taken into account in designing any
solution for Vehicle-to-Vehicle (V2V) and Vehicle-toInfrastructure (V2I) communications [1].
Though, as ubiquitous broadband internet accessbe-comes more and more essential, providing broadband wireless internet on high speed mobility in highway has not been widespread due to several obstacles. First, high speed of mobile station (MS) andhigh carrier frequency result in large Doppler frequency
I would like to thank my lab members, Professor YYY andProfessor ZZZ.

2

Department of Electrical Engineering,
Faculty of Engineering Brawijaya University
Malang, Indonesia
Email: fakhriy08@ub.ac.id

shift and severe inter carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems, which is the mainstream modulation techniquefor next-generation communication systems. Secondly,
all kinds of signal shadowing and blocking due to theterrestrials, such as hills, buildings and tunnels. [1],[2].
Future generation technology, the integration of microwave and optical networks is a solution for increasing capacity and mobility as well as decreasing costs inthe access network using RoF architecture.
Radio-over-Fiber technology is use of optical ﬁber
links to distribute RF signals from BS to Remote
Antenna Unit (RAU). In the communication system,
RF signal processing functions such as frequency upconversion, carrier modulation, and multiplexing, isperformed into the antenna. RoF makes it possible
to centralize the RF signal processing function in one
head end, then to use optical ﬁber, which oﬀers low
signal loss between 0.3 dB/km for 1550 nm and 0.5
dB/km for 1310 nm wavelength [3].
Distortion in the RoF system is nonlinearity distortion mostly caused by nonlinear character of laser
diode. There are chromatic dispersion of optical ﬁber
and noise awakened by transmitter device which still require to be considered. With the consideration, hence
writer feels important to do performance evaluation of
OFDM system which is integrated with radio over ﬁber
system. Laser are inherently nonlinear, the very basic
lasing operation involves several nonlinear mechanisms
such as the threshold current, stimulated emission andthe spontaneous emission. The stimulated emission isa self multiplying mechanism that occurs under positive optical feedback conditions. Spontaneously emitted photons lack coherency and therefore not desirable.
However, only when the driving currents is large than
the threshold current, the stimulated emission becomesdominant compared to the spontaneous emission process.
However, there have been several projects attempting to provide broadband wireless network on metro
area. An extended survey of broadband internet ac-

cess approaches on train was presented in [5]. The previous research is presents a comprehensive theoreticaland experimental analysis of the full system with a directly modulated LTE-RoF link [4], the system is theoretically and experimentally demonstrated as the highspeed interface between e NodeB and a relay node. The
LTE signals under test comprise three diﬀerent modulation schemes, namely, quaternary phase-shift keying(QPSK), 16- quadratic-amplitude modulation (QAM),
and 64-QAM, which are modulated on to orthogonalfrequency-division multiplexing (OFDM) at 2.6 GHz.
The aims to optimize the physical layer connectivity
for the third generation partnership programlong-term
evolution employing RoF technologies [5]. Additional
research on LTE-RoF [6], proposed LTE4V2X, a novel
framework for a centralized vehicular network organization using LTE. In the continuity to this work, this paper presents new performances evaluation of LTE4V2X
in highway scenarios in order to evaluate the impact ofhigh mobility. They are studied performances against
a decentralized organization protocol.
The main goal of this research is the impact of
nonlinear ﬁber propagation on RoF-LTE integrated toachieve high data rate / throughput and EVMs withvarying transmission span ﬁber on high mobility case.
The characteristics of the proposed architecture can besummarized as follows. Line-of-sight (LOS) deployment can greatly avoid ICI induced by Doppler frequency to overcome the signal blocking by the terrestrials.
2. Nonlinear Fiber
The terms linear and nonlinear in optics, mean intensity independent and intensity-dependent phenomena respectively. Nonlinear eﬀects in optical ﬁbers occur due to (1) change in the refractive index of themedium with optical intensity and, (2) inelasticscattering phenomenon. The power dependence of the refractive index is responsible for the Kerr-eﬀect. Depending
upon the type of input signal, the Kerr-nonlinearity
manifests itself in three diﬀerent eﬀects such as SelfPhase Modulation (SPM), Cross-Phase Modulation(CPM) and Four-Wave Mixing (FWM). At high power
level, the inelastic scattering phenomenon can induce stimulated eﬀects such as Stimulated BrillouinScattering (SBS) and Stimulated Raman-Scattering(SRS). The intensity of scattered light grows exponentially if the incident power exceeds a certain threshold value. The diﬀerence between Brillouin and Raman scattering is that the Brillouin generated phonons(acoustic) are coherent and give rise to a macroscopic
acoustic wave in the ﬁber, while in Raman scattering
the phonons (optical) are incoherent and no macroscopic wave is generated.
The nonlinear eﬀects depend on transmission

length. The longer the ﬁber link length, the more thelight interaction and greater the nonlinear eﬀect. As
the optical beam propagates along the link length, its
power decreases because of ﬁber attenuation. The effective length (Leﬀ ) is that length, up to which power
is assumed to be constant [6]. The optical power at a
distance z along link is given as
P (z) = Pin exp(−αZ)),(1)where Pin is the input power (power at z = 0)and iscoeﬃcient of attenuation. For a actual link length (L)eﬀective length is deﬁned as [6]:∫ L
Pin Lef f =p(Z)dz.
(2)
z=0

1 − exp(−αZ).
(3)α
Since communication ﬁbers are long enough so thatL ≥ α1 This results in Lef f ≈ α1
Lef f =

2.1. Self Phase Modulation (SPM)
The higher intensity portions of an optical pulse
encounter a higher refractive index of the ﬁber compared with the lower intensity portions while it travels
through the ﬁber. In fact time varying signal intensity
produces a time varying refractive index in a mediumthat has an intensity-dependant refractive index. The
phase (ϕ) introduced by a ﬁeld E over a ﬁber length L
is given by [6]:
2π
ηL,
(4)
(ϕ) =
λ
where λ is wavelength of optical pulse propagating inﬁber of refractive index η, and ηL is known as optical
path length. For a ﬁber containing high-transmitted
power n and L can be replaced by ηef f and Lef f respectively[6]
(ϕ) =

2π
ηef f Lef f .
λ(5)2.2. Cross Phase Modulation (CPM)
SPM is the major nonlinear limitation in a single
channel system. The intensity dependence of refractive
index leads to another nonlinear phenomenon known ascross-phase modulation (CPM). When two or more optical pulses propagate simultaneously, the cross-phasemodulation is always accompanied by SPM and occurs
because the nonlinear refractive index seen by an optical beam depends not only on the intensity of thatbeam but also on the intensity of the other copropagating beams [9]. The eﬀective refractive index of anonlinear medium can be expressed in terms of the input power (P ) and eﬀective core area (Aef f ) as [6]:

RoF
(Radio over Fiber)

Self Phasa Modulation( SPM )

Optical Kerr
EffectNonlinear EffectCross Phase Modulation( XPM )

Four Wave Mixing( FWM )

Stimulatted Scattering

Stimulated
Raman
Scattering

Stimulated
Brilloun
Scattering

Transmission characteristic

Varying Span of Fiber(5 Km, 10 Km, 60 Km)

LTE Systems
UE- High Mobility Scenario- Multipath FadingUE- Doppler EffectParameters Measurement- Throuhput- Delay- BER-EVM ( Error Vector Magnitude)

Figure 1: Block Diagram Research Integrated LTE-RoF scheme.

Pηef f = ηl |ηηl
.
(6)
Aef f
The nonlinear eﬀects depend on ratio of light power
to the crosssectional area of the ﬁber. If the ﬁrst-order
perturbation theory is applied to investigate how ﬁber
modes are aﬀected by the nonlinear refractive index, it
is found that the mode shape does not change but the
propagation constant becomes power dependent.
kef f = kl + knl P,(7)

where kl is the linear portion of the propagation constant and knl is nonlinear propagation constant. The
phase shift caused by nonlinear propagation constantin traveling a distance L inside ﬁber is given as [6]:
∫ L
ϕnl =
(kef f − kl )dz.
(8)0

Using equations (8) and (2) nonlinear phase shift
becomesϕnl = knl Pin Lef f .
(9)2.3. Four Wave Mixing (FWM)
The origin of FWM process lies in the nonlinear
response of bound electrons of a material to an applied optical ﬁeld. In fact, the polarization inducedin the medium contains not only linear terms but also
the nonlinear terms. The magnitude of these terms isgoverned by the nonlinear susceptibilities of diﬀerent
orders. The FWM process originates from third ordernonlinear susceptibility (X (3) ). If three optical ﬁelds
with carrier frequencies ω1 , ω2 and ω3 , copropagate inside the ﬁber simultaneously, (X (3) ) generates a fourthﬁeld with frequency [6]: ω4 , which is related to otherfrequencies by a relation
ω4 = ω1 ± ω2 ± ω3 .

(10)Energy : ω1 + ω2 = ω3 + ω4 .

(11)

M omentum : β1 + β2 = β3 + β4 .

(12)

2.4. Stimulated Brilloun Scattering (SBS)
Stimulated Brillouin scattering (SBS) is a nonlinear optical process when the input power exceeds thethreshold in a ﬁber. Since SBS was observed as the nonlinear phenomena in the 1964 for the ﬁrst time, it has
been extensively investigated. SBS is shown throughthe Stokes light of which wave frequency relative to theincident light down, the frequency shift is determinedby the nonlinear medium. Because of the low threshold of inherent power, the narrow line-width gain andhigh conversion eﬃciency characteristics of SBS, ﬁber
ampliﬁer (FBA) and lasers (FBL) based on SBS are important in many applications. Only can the long ﬁber
length and high pulse light input power satisfy the needof long distance and large scale structural monitoring.
The Brillouin of the stokes wave is given by [9]:
2n
ωA
=
,
2π
λp

VB =

(13)

where VA is refraqtive index of the ﬁber, WA is the freqof the acoustic wave and λp is the pump wave length.
Estimatis Brillouin threshold for long ﬁbers 20 km for
α=0.2 dB/Km at 1.55µm. For telecom ﬁbers Aef f =50 − 75µm2 . Threshold power Pth ∼ 1mW is relatively
small. The Brillouin for pump light is approximated
by
Pth =

21Aef f.
gBLef f

(14)

Brillouin gain gB = 5χ10− 11m/W is nearly independent of the pump wavelength.

Raman threshold is deﬁned as the input pump
power at which Stokes power becomes equal to thepump power at the ﬁber output[5]:Ps (L) = Pp LPo exp(−αL),(16)

for Po = Io Aef f Assuming a Lorentzian shape for
the Raman-gain spectrum, Raman threshold is reachedwhen (Smith, Appl. Opt. 11, 2489, 1972)
Pth =

16Aef f.
grLef f(17)

3. Conclusion
This research is propose to investigate the impactof performance integrated LTE-RoF system from nonlinear ﬁber propagation phenomena and eﬀect reducing the overall system. Goal of the measurement is toachieve value of throughput, BER, delay and EVM onhigh mobility and terrestrial condition.
References[1] Guillaume Remy, Sidi-Mohammed Senouci, Francois Jan, Yvon Gourhant, Impact of High Mobility in Highway Scenarios, Laboratory, University ofBourgogne, France, IEEE 2011.
[2] W. P. Ng, T. Kanesan, Z. Ghassemlooy C. Lu, Theoretical and Experimental Optimum System Designfor LTE-RoF Over Varying Transmission Span and
Identiﬁ cation of System Nonlinear Limit, Volume
4, Number 5, October 2012.

2.5. Stimulated Raman Scattering (SRS)
Stimulated Raman scattering (SRS) is important
as a phenomenon which can cause signal distortion inlong haul, low-loss telecommunications ﬁbres and as aneﬀect upon which wavelength shifting devices, known
as ﬁbre Raman lasers, are based . Narrow linewidth
pump laser light is converted to a broad Stokes spectral output. The conversion eﬃciency from pump to
Stokes wave- lengths is strongly inﬂuenced by the input power, the ﬁbre length, the intrinsic Raman gain
and the loss distribution over the range of wavelengths
corresponding to the Stokes output.
The Raman gain yi that is coupling the ﬁrst andthe channel can be expressed using triangular Ramangain as follows [5]:yi =i∆v Yp,1.5X1013(15)

For i∆v < 1.5x1013
Yp = Peak Raman gain coeﬃcient with a value
6x10−12 and ∆v = channel sampling frequency [5].

[3] havamaran Kanesan, Zabih Ghassemlooy, Optimization of Optical Modulator for LTE RoF in Nonlinear Fiber Propagation, IEEE Photonics Technology Latters Vol 24, No 7, April 2012.
[4] G. Remy, S.-M. Senouci, F. Jan, and Y. Gourhant,
Lte4v2x: Lte for a centralized vanet organization,
in Submitted to IEEE Globecom 2011,Houston,
Texas.
[5] Xiaorui Li, Huaping Gong *, Shuhua Li, Jianfeng Wang, ”Experimental Investigation on Pulse
Light Stirn ulated Brillouin Scattering in the Optical Fiber.” Institute of Optoelectronic Technology,
China Jiliang University, Hangzhou, China,2011.
[6] S. P. Singh and N. Singh,”Nonlinear Eﬀects
in Optical Fibers :Origin Management and Application,”Progress In Electromagnetics Research,
PIER 73, 249275, 2007,Department of Electronics and Communication University of Allahabad
Allahabad-211002, India.

[7] R.Herschel, C.G Schaﬀer, Radio over Fiber systems
for Next Generation Wireless Access, International
Scientist Workshop, Hamburg, Germany 2011.
[8] M. Cherif, S.-M. Senouci, and B. Ducourthial,
Vehicular network selforganizing architectures, in
Proc. IEEE GCC 2009.
[9] S. W. Peters, A. Y. Panah, K. T. Truong, and J.
R. W. Heath, BRelay Architectures for 3GPP LTEAdvanced,[EURASIP]. Wireless Commun. Netw.,
vol. 2009, pp. 618787-1618787-14, 2009.
[10] K. Dimou, W. Min, Y. Yu, M. Kazmi, A. Larmo,
J. Pettersson, W.Muller, and Y. Timner, BHandover within 3GPP LTE:Design principles and performance,[ in Proc. IEEE 70th VTC, 2009, pp. 1
4.
[11] M. Zhou, Principle, Technology and Challenge ofRadio over Fiber (RoF) based Broadband Accessfor Metro and Inter-city Trains, in Asia Communications and Photonics Conference and Exhibition,
2009, pp. 1 10.
[12] S. P. Singh and N. Singh nonlinear eﬀect in opticalﬁbers origin management and application, Progress
In Electromagnetics Research, PIER 73, 249275,
2007.
[13] Reza Abdole, Razali Ngah, Vida Vakilin, Tharek
A. Rahman, Application of Radio over Fiber in mobile communication, Asia Paciﬁc Conference, December 4-6, 2007

1

Controllability and Observability Analysis of theGain Scheduling Based Linearization for UAV
Quadrotor
Abstract—This paper presents the gain scheduling based
linearization of simplified quadrotor non-linear model aswell as the test for the system controllability and observability for various equilibrium point. The simplified nonlinear model of quadrotor is linearized at some equilibrium
points to get the linear state equation. Then, the controllability and observability test is done to the linearized model
for various equilibrium points. The system behavior is thentested by using the state feedback controller to confirm theresult.
Keywords—Quadcopter, non-linear control system, linearization, gain scheduling, LQR.

I.

Fig. 1.

Quadcopter axis

I NTRODUCTION

Quadcopter is one of many type of Unmanned Aerial
Vehicle that becoming popular and having much attentions from various group recently, especially the researchers and hobbiest in aeromodeling. Several factorsthat contribute to its popularity are its reliability inmaneuvering, its ability to be flown indoors, and easier
to model and control [1], [6].
One of the most important problem on the quadcopter
stems from the fact that quadcopter is essentially nota stable system, both in stabilization and trajectroy following. Therefore, a special considerations are needed
in designing the control system for the purpose of
estabilizing or maneuvering. Existing control theories
in controlling quadcopter are widely varied. The mostcommonly used is the conventional PID control, mainly
due to its simple structure are easy enough to be designed
and implemented in varied systems including quadcopter[1].
The drawback of PID Controller is it has the constantgain which may be optimum just for some specificcondition. In order to get better result, the controller has
to be adaptive so that it can adjust the controller gain toadapt to the position and attitude change of the quadrotor.
Many people have tried to design this adaptive control
such as Gaikwad [2] with auto-tuning PID Loop Shaping
and Liu [4] who design self-adaptive PID based on theleast-square method.
One of the challenge in designing controller for
quadrotor is that the system is non-linear so that we haveto linearize it first. The basic limitation of the controller
design via standard linearization is the fact that thecontrol is guaranteed to work only in the neighborhood ofa single equilibrium point. Gain scheduling is a technique
to design controller of non-linear system by linearize
the system at several equilibrium points, design thecontroller at each point, and implement the family of
linear controllers as a single controller with varying gainor parameter[3].
In this paper, we linearize the simplified model ofquadrotor based on Peter Corkes model [1] using theGain Scheduling linearization at some equilibrium point.
Then, we test the controllability and observability of thesystem for various equilibrium points. After that, using
the linearized state equation, we find the state feedback
controller and use it to control the altitude and attitudeof the quadrotor non-linear model.
II.

P ROJECT OVERVIEW

A. Notation Explanation
In this section, we explain the derivation of statespace equation of quadcopter system. The linearizationis performed in order to formulate the transfer functionof the quadcopter plant. Based on the Corke’s model[1], the model is shown at figure 1. We use 12 states
for this state-space model,
its position
in world frame
is denoted as 1 = x  y z , itsroll,pitch, yaw
due
angles denoted as 2 = φ θ ψ , its velocityto x-axis, y-axis, z-axis denoted as 3 = ẋ ẏ ż2, and its angular velocity
due to x-axis, y-axis, zaxis denoted as 4 = ṗ q̇ ṙ
. The state vari T
T,able and its system input is x = 1 T2 T3 T4
u=



ω12

ω22

T
ω42
. Hence,Tx = x1 , ..., x12Tu = u1 , ..., u4Then, equation 6 can be written as


 
 

τx
ṗ
Ix ṗ
I ω̇ =  τy  −  q̇  ×  Iy q̇ 
τz
ṙ
Iz ṙ

ω32

and
τx = db(ω42 − ω22 )τy = db(ω12 − ω32 ).

τz = k(ω12 − ω22 + ω32 − ω42 )T = b(ω12 + ω22 + ω32 + ω42 )B. Translational and Rotational Analysis
Based on Newton’s second law of translational motion[6], we have this equation:Thus, we have these equations for angular accelerationof quadcopter

(1)

p̈ =

Iz − Iydb 2(ω4 − ω22 ) −
q̇ ṙ
Ix
Ix

(7)

where ω = 4 and v = 3 .
From fig.1, we could get the forces which is workedon the quadcopter

q̈ =

db 2
Ix − Iz(ω − ω32 ) −
ṗṙ
Iy 1
Iy

(8)

k 2
Iy − Ix(ω1 − ω22 + ω32 − ω42 ) −
ṗq̇
Iz
Iz

(9)

F = mv̇ + (ω × mv)

F = F g − F thrust

(2)






0
0
F =  0  − W RB  0 
mg
T

We also derive the roll, pitch and yaw (RPY) rates
which is a function of angular velocity, using inverted
W ronskian matrix, denoted asTherefore, equation 2 can be expressed as


 
 

 
0
ṗ
ẋ
0
1  
v̇ =
g 0  − W RB  0  −  q̇  ×  ẏ 
m
T
ṙ
ż
1

where m is the mass of quadcopter, T is vertical thrust ofquadcopter against gravity and W RB is the rotation matrix from body-frame to world-frame or inertial-frame.
"
W

v̇ =

RB =

1
m

""

cθ cψ
cθ sψ
sθ

sφ sθ cψ − cφ sψ
sφ sθ sψ + cφ cψsφ cθ

−T (cφ sθ cψ + sφ sψ )−T (cφ sθ sψ − sφ cψ )mg − T (cφ cθ )cφ sθ cψ + sφ sψ
cφ sθ sψ − sφ cψ
cφ cθ

#

"
− W RBr̈ =#(q̇ ż − ṙẏ)(ṙẋ − ṗż)(ṗẏ − q̇ ẋ)1
T (cφ sθ cψ + sφ sψ )m1ÿ = − T (cφ sθ sψ − sφ cψ )m1z̈ = g − T (cφ cθ )
m


cφ sθ
−sφ cθ cφand the relation between RPY rates and angular velocity
expressed by matrix





φ̇
ṗ
 θ̇  = W −1  q̇ ṙψ̇φ̇ = ṗ + sφ tθ q̇ + cφ tθ ṙ

(10)θ̇ = cφ q̇ + sφ ṙ
sφ
cφ
ψ̇ =
q̇ + ṙ
cθ
cθ

(11)
(12)

(3)
(4)
(5)

Using rigid body rotational law, we have
Γ = I ω̇ + (ω × Iω)

sφ sθ
cφ cθ
sφ

So, the roll, pitch and yaw rates is##

Assume ṗ, q̇, ṙ, ẋ, ẏ, ż ≈ 0 , thenẍ = −c1  θ0W −1 =cθ
0

C. Linearization
The non-linear model of quadrotor will be linearizedat the equilibrium point in order to make the system moreamenable. In order to do the linearization, we have to findthe equilibrium point of the system or can be written as
0 = f (X(α, β, γ, δ)).

(6)

So, the equations can be modified as follow

where, I is moment of inertia of quadcopter


Ix
I= 0
0

0
Iy
0


0
0 .
Iz

ẋ1 = ẋ = x7 = 0(13)ẋ2 = ẏ = x8 = 0(14)ẋ3 = ż = x9 = 0

(15)

3

ẋ4 = φ̇ = x10 + sx4 tx5 x11 + cx4 tx5 x12 = 0(16)

And then, we getẋ5 = θ̇ = cx4 x11 + sx4 x12 = 0

(17)

sx4
cx
x11 + 4 x12 = 0
cx5
cx5

(18)

ẋ6 = ψ̇ =1ẋ7 = ẍ = − T (cx4 sx5 cx6 + sx4 sx6 ) = 0mẋ8 = ÿ = −ẋ10 = p̈ =ẋ11 = q̈ =N (2×2)I (6×6) I (6×6) O (2×1)O (4×6)

(25)

(19)

B (12×4) =1
T (cx4 sx5 sx6 − sx4 cx6 ) = 0mẋ9 = z̈ = g −O (6×6)
A(12×12) =  O (2×3)



(20)

1
T (cx4 cx5 ) = 0m"

N (2×2) =db
Ix − Iz(u1 − u3 ) −x10 x12 = 0
Iy
Iy

(22)

b
−m
 0




M (2×2) = (23)kIy − Ix(u1 − u2 + u3 − u4 ) −x10 x11 = 0
Iz
Iz
(24)
From equation 19 and 20, we getẋ12 = r̈ =



db
Iy
k
Iz

−gsδ −gcδ
gcδ −gsδb−m− Idbx
0
− Ikz

b
−m
0
− Idby

#

,
b
−m
db
Ix

0
− Ikz

k
Iz




.


Constants which are used is defined as
g = 9, 81 m /s2
ix = 0.0820 kg.m2
iy = 0.0845 kg.m2
iz = 0.1377 kg.m2
b = 1.2953 × 10−5 kg.m
d = 0.165 m
k = 1.0368 × 10−7 kg.m2
m = 4.34 kg

s2 x5 + t2 x4 = 0

The only solution for this equation is x4 = 0 and x5 = 0.
Combining the results with equation 16, 17, and 18
we get x10 = 0, x11 = 0 and x12 = 0. Lets assume theequilibrium point is located at some certain position in
Cartesian coordinate (x, y, z) and at some yaw angle
position defined by x = α, y = β , z = γ and ψ = δ . So,
the complete list of the state variables value in this equilibrium, X(α, β, γ, δ), can be written as x1 = α, x2 = β ,x3 = γ , x4 = 0, x5 = 0, x6 = δ , x7 = 0, x8 = 0, x9 = 0,x10 = 0, x11 = 0 and x12 = 0.
The state equation for the linearized model is given
by

(26)

where O is zero matrix and I is identity matrix. While
N and M can be defined as

(21)

db
Iz − Iy(u4 − u2 ) −x11 x12 = 0
Ix
Ix

O (8×4)M (4×4)The output of this quadrotor model is defined by theTvector y = [ x y z ψ ] , so the matrix C and D canbe written as"

C (4×12) =I (3×3) O (3×9)
O (1×3) L(1×3)

#

(27)

where
L(4×12) =h

0 0 1 0 0 0 0 0 0i.

So, the linearized model of this quadrotor isẋ = Ax + Buẋ = A12×12 x + B 12×4 u
y = C 12×1 x.

y = Cx + Du.

We can find the matrix A and B by using this equation


A=



B=

∂f1
∂x1 |X(α,β,γ,δ)

..
.

∂f12
∂x1 |X(α,β,γ,δ)∂f1∂u1 |X(α,β,γ,δ)

..
.

∂f12
∂u1 |X(α,β,γ,δ)

...
..
.
...

∂f1
∂x12 |X(α,β,γ,δ)

...
..
.
...

∂f1
∂u4 |X(α,β,γ,δ)

..
.

∂f12
∂x12 |X(α,β,γ,δ)

..
.

∂f12
∂u4 |X(α,β,γ,δ)







D. Controlability and Observability
Before designing any controller or observer any further, we have to check the controllability and observability of the system. Let us define the controllability matrix
as
Pc =




.

h

B AB A2 B · · ·An−1 Bi4With n defines the order of the system. In our case,the matrix can be written asPc(12×48) = O

hB AB A2 B · · ·(8×4)

Pc(12×48) =M (4×4)O (2×4)M (4×4)O (6×4)O (6×4)R(2×4)
O (4×4)

i

A11 B



R(2×4)O (10×4)

O (12×36)

III.
(28)

where
− gcIδydb− gsIδydb"

R(2×4) =gcδ dbIx− gcIδxdb

gcδ dbIy
gsδ dbIy− gcIδxdb#.

gcδ dbIx

This controllability matrix has unique form. From theconfiguration of the matrix element above, we can conclude that the rank of the controllability matrix will
always be the same with the order of the system (n=12)
regardless of the value of α, β, γ, δ, g, , λ, κ and ρ. We
can conclude that the system is controllable.
By definition, the matrix observability can be writtenasPo(48×12) =



C

2

CA

CA

11

···



CA

T



χ1(4×12) χ2(4×12) Po(48×12) =  χ3(4×12)  χ
4(4×12)O (32×12)

where
χ1(4×12) =χ2(4×12) =O (4×6)χ3(4×12) =χ4(4×12) =Z (4×1) =

O (4×2)Z (4×1)0

O (4×7)

N (2×2)O (2×2)O (4×1)



0

T

0

1

Linear quadratic regulator is a control method usingstate feedback law u = −Kx [5] to minimize the costfunction defined as
Z ∞

J(u)

xT Qx + uT Ru dt

(30)

0

where Q is weight matrix for state energy and R is weightmatrix for input energy. The matrix K can be derived
from equation(31)

AT S+SA−(SB+N )R−1 (B T S+N T )+Q = 0. (32)I (3×3)O (1×3)and

A. Linear Quadratic Regulator

While the matrix S is solution for the Riccati equationO (2×9)We are now able to design controller of quadrotor
using the matrix A, B , and C we have found. Let us
verify the controllability of this linearized model by usingstate feedback regulator.

(29)

O (3×9)L(1×9)

N (2×2)O (2×2)C ONTROL S YSTEM D ESIGNK = R−1 (B T S + N T ).

I (3×3)O (1×3)O (4×3)the controllability and observability of the system. For
gain scheduling, the controller matrix K will depend on
the value of yaw angle ψ and will be both controllable
and observable for all value of possible ψ .

In order to solve for matrix K, we have to give numericalvalue for so that we get all numerical values of matrix
A and B. For simplicity, lets choose the weight matrix
Q and R as follow
Q = qI12×12
R = rI12×12 .

This observability matrix has unique form. From the configuration of the matrix element above, we can conclude
that the rank of the observability matrix will always
be the same with the order of the system (n = 12)
regardless of the value α, β, γ, δ, g, , λ, κ and ρ. We canconclude that the system is observable.
What makes this result interesting is the fact thatthe controllability and observability dont depend on theequilibrium point we have chosen in the beginning,which is at x = α, y = β , z = γ , and ψ = δ . Even,
the property of matrix A and B depend only on theyaw angle value. So, we can conclude that the system
can be controlled by more advance adaptive controller
technique such as gain scheduling without concerning

Where q = 10000000000 and r = 0.0000000001. We
choose small value for weight matrix R because we dont
want to minimize the energy of input signal. In orderto make the quadrotor able to maintain its altitude, wehave to give great amount of energy for input signal. We
choose the value for δ = 0.5. The matrix K we get fromthe LQR is
K(4×12) = ξ ×

h

U (4×4) V (4×4) W (4×4)

i

(33)

where ξ is a constant, its value is 1.0 × 1010 and−0.6205 −0.3390
 0.3390 −0.6250

U (4×4) = 
0.62050.3390−0.3390 0.6250

3.83920.5000 0.0000 −0.5000
V (4×6) =  −3.8392 0.5000
0.0000 −0.5000


0.0000−3.8392 
,
0.0000 
3.8392
−0.9007 −0.4921
0.4921 −0.9007 ,
0.90070.4921 −0.4921 0.9007−0.5000−0.5000−0.5000−0.5000

5

−0.5000 −0.5000
W (4×6) =  −0.5000−0.5000

0.0000−0.70710.00000.7071
0.70710.50000.0000 −0.5000 .
−0.7071 0.5000 
0.0000 −0.5000

B. Simulation
We use matrix which has been mentioned above to
produce input signal based on the state feedback law u =−Kx. Here, we present the result for quadrotor attitude
and altitude control using state feedback controller weget by using the linearized model property of quadrotor.
In figure 2, we get the graph of quadrotor altitudewith initial condition z = 0 to the steady state conditionz = −5. While, in figure 3, we have the graph ofquadrotor yaw angle with initial condition ψ = 0 andsteady state condition ψ = 0.5. We can see that the state
feedback controller we get from the linearized model ofthe quadrotor works well. We dont see any overshoot andthe system dont need much time to reach the stability.
Changing the value of variable δ = 1, we get the resultin figure 4 and figure 5.

Fig. 2.

Altitude Z, for δ = 0.5

IV.

C ONCLUSION

In this paper, we get the linearized model of quadrotor
simplified model and show that the system is controllable
and observable regardless of the value of reference position in Cartesian coordinate as well as the reference yaw
angle. We use the simplified model of quadrotor based on
Peter Corkes model then linearize it to get the linearizedstate equation at some equilibrium point. Then, we test
the controllability and observability of the linearized
system and show that the system is controllable andobservable regardless of the value of the equilibrium
point we choose. We then test the system behavior by

Fig. 3.

Attitude Yaw, for δ = 0.5

Fig. 4.

Altitude Z, for δ = 1

using the state feedback controller and series of experiment confirm our results. The result we get from thispaper show that the simplified model of quadrotor can becontrolled by more advance adaptive controller techniquesuch as gain scheduling and will be still controllable andobservable for all possible equilibrium point.
ACKNOWLEDGEMENT
The authors would like to thanks to Japan International
for Cooperation Agency (JICA) for their kind supportin providing the Collaborative Grant for Researches
Alumnae.
R EFERENCES[1]

P. I. Corke, Robotics, vision and control: fundamental algorithms in MATLAB, ser. Springer tracts in advanced robotics.
Berlin: Springer, 2011, no. v. 73.
[2] S.
Gaikwad,
S.
Dash,
and
G.
Stein,“Autotuning
PID
using
loop-shaping
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IEEE,
1999,
p.
589593.
[Online].
Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=806712

6Fig. 5.

[3]

Attitude Yaw, for δ = 1

H. K. Khalil, Nonlinear systems, 2nd ed. Upper Saddle River,
NJ: Prentice Hall, 1996.
[4] X. Liu, T. Huang, X. Tang, and H. Xin, “Designof self-adaptive PID controller based on least square
method.” IEEE, Oct. 2009, p. 527529. [Online]. Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5402780[5] K. Ogata, Modern control engineering, 5th ed., ser. PrenticeHall electrical engineering series. Instrumentation and controls
series. Boston: Prentice-Hall, 2010.
[6] P. Pounds, R. Mahony, and P. Corke, “Modelling and control
of a quad-rotor robot,” in Australasian Conference on Robotics
and Automation 2006. Auckland, New Zealand: Australian
Robotics and Automation Association Inc., 2006. [Online].
Available: http://eprints.qut.edu.au/33767/

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

Particle Swarm Optimization Algorithm for Optimal Deployment of Wireless Sensor
Network by Taking Into Account of Barrier Position and Attenuation
Masjudin, I Wayan Mustika, Widyawan
Graduate School of Engineering,
Gadjah Mada University
Jl. Graﬁka No. 2, Kampus UGM, Sleman, Yogyakarta Indonesia
Email: masjudin s2te 11@mail.ugm.ac.id, {wmustika, widyawan}ugm.ac.id

Abstract
Connectivity is an important part in the deployment ofWireless Sensor Network (WSN). Proper placement of
the sensor nodes can reduce the complexity problems
in WSN such as routing, data fusion, communication
between nodes and etc. This paper studies the optimal deployment of sensor nodes by taking into accountthe position of barriers and damping eﬀect based onparticle swarm optimization algorithm. The researchuses two scenarios, the ﬁrst scenario uses single barrier
and the second one uses two barriers. The results showthat the sensor node deployment in indoor environment
without barrier has the best connectivity compared tothat of with barrier. The position of the obstacle also
aﬀects the connectivity of the sensor nodes.
1. Introduction
Connectivity is an important part in the deployment of Wireless Sensor Network (WSN). Proper placement of the sensor nodes can reduce the complexity
problems in WSNs as, for example, routing, data fusion, communication, etc [1]. In addition, optimal
placement of the sensor nodes can extend the node
life time and maintain the connectivity among nodes.
The existence of wall material in indoor environment
could change the routing path and thus aﬀect the datatransmission delay between nodes or from node to sink.
Propagation attenuation is inﬂuenced by the conditionof the track around. Attenuation decreases signal quality at the receiver. Study of the radio wave propagationin mobile radio communication shows that the magnitude of the damping rates of propagation is dominatedby a percentage of the concrete walls in indoor environment. This suggests a strong correlation between
propagation attenuation with propagation constant ofa space [2].
There are several algorithms used by researchers to
get a good spread of sensor nodes. Obstacle-Resistant

Deployment algorithm (ORRD) is used for the deployment of sensor nodes in the area with obstacles. This
study involves the robot moving direction and deploy
sensors to limit certain areas, and be able to handle thepresence of the barrier with regular shape or not. Thisalgorithm is able to deploy sensor nodes using minimal
number of sensors with the full area coverage [3].
Subsequent research using Particle swarm optimization algorithm to new mobility control algorithm is tomaximize the wireless nano sensor node lifetime andimprove the network performance. Simulation results
show that the proposed optimization algorithm improves the network coverage by better utilization ofneighbor nodes. The results also demonstrate thatthe algorithm increases nano sensor lifetime [4]. Implementation of the PSO algorithm is also applied tothe deployment of sensors to provide fault tolerance in
WSN in order prolong its lifetime, to reduce its complexity and to increase the quality of service (QoS) for
WSNs applications. Simulation results show that, theproposed algorithm outperforms random deployment
in terms of coverage [5].
Particle swarm optimization algorithm is also suitable to be applied on the deployment of sensor nodes
without any obstructions in the path (Line Of Sight)
which shows the results of the sensor nodes to form a
network with good connectivity [6].
This paper will discuss the spread of simulated sensor nodes to see the inﬂuence of the barrier position andattenuation against sensor node connectivity in wirelesssensor network based algorithm Particle Swarm Optimization (PSO). PSO algorithm is chosen because it
has several advantages, such as easy to implement andhave fewer functions and operating parameters to be
determined [7].
The structure of this paper is as follows: Section
II describes the model of wave propagation, Part III
particle swarm optimization algorithm is then followed
by a test scenario in Section IV and the test results inSection V. Finally, it is concluded in Section VI.
2. Radio Wave Propagation ModelRadio wave propagation for wireless applications inthe space that has a barrier or bulkhead as in the building have diﬀerent propagation models with space without barriers (Line Of Sight). Attenuation will occur for
propagation with barrier. Attenuation eﬀect decreases
signal quality at the receiver. Power received in a roomwith a barrier no longer aﬀected by the distance asin space without obstructions (LOS), but also by theattenuation barrier. On testing wave propagation in
space by a barrier, assuming the barrier has a certain
attenuation value, and the transmit power received by
the base (there are no reﬂections or wave interference)
the attenuation in decibels equation can be formulated
as the following equation:
L = 32.44 + 20 log f + 20 log d + (ΣX),(1)

where :
L : damping of spacef : frequency (MHz)d : distance between transmitter and receiver (Km)X : damping value of obstacle / barrier.
In this study, distance (d) is the distance between
the sensor nodes in the network, at get the Euclidean
distance formula :√d = (x1 − x2 )2 + (y1 − y2 )2(2)Strong magnitude of the received signal at the receiver by calculating power loss can be calculated by
the following formula :P r = P t+Gt+Gr −(32.44+20 log f +20 log d+(ΣX))(3)
with :
Pt : power transmit
Gt : gain of transmitter antenna
Gr : gain of receiver antenna
3. Particle Swarm Optimization
Particle Swarm Optimization is one of the optimization methods inspired by the behavior of herd animals
such as ﬁsh movements (school of ﬁsh), animal herbivor(herd), and birds (ﬂock) of each object are then reduced
to a particle animals. This algorithm was introducedby Eberhart and Kennedy in 1995 [8]. The distinctive
feature of PSO is the particle velocity settings heuristic

and probabilistic. If a particle has a constant speed so
if the position of a particle is visualized trail will form astraight line. With the external factors that distort theline and then move the particles in the search space is
expected to lead particles, approached, and eventuallyreach the optimum point. External factors in question
include the best position ever visited by a particle, thewhole particle best position (assumed to know the best
position of each particle every other particle), as wellas to explore the creativity factor.
The standard procedure for applying PSO algorithm is as follows:• Initialize a population of particles with random
positions and velocities in a search space dimension.
• Evaluate the desired optimization ﬁtness functionin the variable d on each particle.
• Comparing the ﬁtness evaluation with its particle
Pbest . If there is a better value than the value its
Pbest , then Pbest is set equal to the value andlocation of the i-th particle at the location of theexisting particles (Xi) in d-dimensional space.
• Identiﬁcation of particles in the environment withthe best results so far.
• Update the particle velocity and new position.
• Return to step b until the criteria are met, usually
stopping at a pretty good ﬁtness value or until the
maximum number of iterations.
Other parameters are known in PSO algorithm isconstriction factor, this parameter was introduced by
Clerc in order to guarantee a search in PSO algorithm
to converge faster [9]. Value constriction factor (K)obtained from the following equation :
K=

2
√
,
|2 − φ − φ2 − 4φ|

φ = φ1 + φ2 , φ>4 (4)The equation for update the particle velocity andnew position as in point (e) by inserting the following
constriction factor:
k+1
k
k
id
k
vid
= K.vid+c1 .r1 ((pbest )id
k −xid )+c2 .r2 ((pbest )k −xid )
(5)
k+1
xk+1
= xkid + vid
idwith the provisions of :
 k
k+1
k+1
<xmax
xmin <xkid + vid
 xid + vid
k+1
k+1
xid =
xmax
xkid + vid >xmax ,

k+1
xmin
xkid + vid<xmin ,

(6)

(7)

with :
xmin = lower limit problem
xmax = upper limit problem
In this study, PSO parameters used are :1. Swarm
Swarm is a collection of particles that make up
the population. The recommended size of theswarm range 20-60. Swarm size is too small does
not give you many options and solutions particles
tend to get stuck at a local optimum solution.
While the size of the swarm are too large to provide a global optimum solution, but slows down
the process of computing. In this study swarm
size used was 30.
2. Particle
Particle is a member or an individual part of theswarm. A particle will represent a solution. In
this study, the position of the particle is a solution of the sensor nodes are randomly generated
in a room with a size of 500 x 500 m2 . Coordinate the generation constrained with an upper
limit of 500 problems and lower limit 0 in orderthat the solutions obtained are in the scope ofthe distribution area. Visualize the particles in
2D coordinates to represent the position of thesensor nodes (x,y)
3. Fitness Function
The ﬁtness function is an important part of the
PSO algorithm. The ﬁtness function is determined according to the research objectives. The
focus of this research is to develop a system todesign the sensor node deployment in space bytaking into account the connectivity barrier ismaintained between the sensor nodes on the network. Fitness function in the design of sensor
node deployment is determined based on the received power and the number of connections or
the number of neighboring nodes are detected ata sensor node with the following provisions :• Power received that would otherwise require
to connect at -110 dB, greater than the limitof acceptability in the datasheet IQRF -110
dBm or -140 dB. This is because the area of
distribution of the measured area is only 500
meters while IQRF transmit power range of
700 meters (1.2 kbps)• The deployment is designed to form a network with full connection limits (full mesh)but the connection part (partial mesh) is

also allowed because the sensor nodes areused multihoop, because it inﬂuences thenumber of connections into account in theﬁtness function. In the event of a full connection in the network (full mesh) then thenumber of connections by n · (n − 1) with n
the number of sensor nodes
Based on the foregoing, the ﬁtness function canbe mathematically formulated in equation :
FXi =

j−1
∑

Prk (xid ) −∑2n(id)−1 · C(8)0with :F (Xi)= ﬁtness function of particle Xi
Prk = power receive connected of particle i nodedn = number of neighboring nodes detected onparticle i node d
C = constant (30)
4. Learning Rate
Learning rate used in this study φ1 =1,3 and
φ2 =2,8 in accordance with the general limitations
which are used in the constriction factor φ>4, tobalance the cognitive part and the social part ofPSO
4. Testing Scenario
The process of testing in this study observe the following :• Transmit power is included varies according totransmit power range IQRF TR 52B i.e -25 dB,-28 dB, -31 dB and -34 dB with the number of
sensor nodes 10, the area of 500 × 500m2 , andused band frequency 868 MHz• The distribution area is divided by a barrier into
1, 2 and 3 space with the location of the barrier
can be set so that the change of the space distribution area of sensor nodes can also be changed• The value of damping (attenuation) is used in theform of a barrier wall with 6 dB attenuation values, 0.5 inch glass barrier with the attenuation of
2 dB and a wooden barrier with the attenuationof 2.35 dB
Parameters tested at this stage is :• Testing the inﬂuence of the position and numberof obstructions in the distribution area to area
coverage and network connectivity

(a) without barrier(b) brick wall(a) without barrier(b) brick wall(c) wall of wood(d) wall of glass

Figure 1: Comparison of the results of deployment
without barrier with single barrier (barrier coordinates
x = 100)• Testing the eﬀect of diﬀerent barrier against area
coverage and network connectivity• Assessment of the eﬀect of transmit power to thearea coverage and network connectivity
5. Test Result• The scenario with single Barrier
Deployment of sensor nodes in the area tested
with a single barrier with barrier location coordinates on the x-axis and shifted by a certain
amount. Location coordinates barrier removal isintended to look at the inﬂuence of placement
or position of the barrier on PSO algorithm insensor nodes deployement. Coordinates barrier
placed sequentially on the x-axis = 100, 250, and400. While the transmit power used is -25 dB, -28
dB, -31 dB, and -34 dB. The test results showed
the sensor nodes deployment more sensor nodes
spread over a wider area than a smaller area.
Large emittance will cover a wider area than thesmaller emittance. Insulation material existence
which gives the damping has a signiﬁcant inﬂuence on connectivity and sensor coverage area.
Diﬀerent types of barriers also aﬀect the connectivity of the network. This is because the value ofthe diﬀerent attenuation of the barrier like a wallof wood with brick walls have diﬀerent attenuation values that inﬂuence the emitted transmitter
signal attenuation is also diﬀerent.
Fig.1 shows the diﬀerences in the results of the deployment of sensor nodes with no barriers and obstacles
given area using -25 dB transmit power. Both networks
can form a full network (full mesh), but the area coverage in the area of sensor nodes with single barrier
narrower. For scenarios with the location of the bar-

Figure 2: Comparison of the results with the deployment location of the barrier and the diﬀerent types ofbarrierrier on the x-axis = 100 area with a brick barrier wall,
wood and glass still showed results similar deployment.
Fig.2 illustrates the results of the deployment ofsensor nodes with the transmit power and the same
type of barrier to the scenario in Fig.1, but the location of the barrier in the slide to the x-axis = 250.
Diﬀerent barrier locations will provide solutions of different sensor node deployment, although with the same
power and the same initial coordinates. This shows the
position of the sensor node deployment barriers also affect and Noteworthy. Deployment of sensor nodes in aroom with a barrier wall shows the coverage area issmall and there are 3 (three) nodes that are not connected in full are marked with gray lines. This indicatesthe received power is less than -110 dB, but the node isstill connected via another node. While in the area of
distribution of a given barrier walls of wood and glass,the network can be formed in full (full mesh), but thearea coverage in a sensor network with a wooden barrier narrower when compared to sensor networks in the
distribution area with a glass barrier. This is due tothe damping value is smaller than the value of the glass
timber damping. Most of the wall attenuation values
resulting in greater attenuation of the transmit power
range resulting in a network coverage area into small.
Fig.3 shows the results of the deployment of sensor
nodes with -28 dB transmit power, smaller than theemittance of such a scenario in Fig.1 and Fig.2 that

(a) without barrier(b) brick wall(a) without barrier(b) brick wall(c) wall of wood(d) wall of glass(c) wall of wood(d) wall of glass

Figure 3: comparison of the results with the deployment of smaller emittance

Figure 4: Comparison of the results of the deployment
of sensor nodes with two barrier

use transmit power -25 dB.
Area coverage of sensors to be smaller and morenodes are not full connected form. Areas with a glass
barrier can form a full connections (full mesh) but
the coverage area is smaller when compared to scenarios that use transmit power -25 dB. Smaller transmit power will result in wireless sensor networks withsmaller coverage areas and fewer connections. From thedeployment results of which are shown, sensor nodes
deployment is aﬀected by the transmit power, the location of the barrier and the barrier types that havediﬀerent attenuation values.

two barriers, each of which is placed on the axis x1 =100 and x2 = 400. Deployment area with no barrier
to form a network with full connection (full mesh) andarea coverage, while the distribution area of the barrier brick wall to form a partial mesh networks withmany nodes that are not connected in full. Area distribution of the barrier wall of wood forming tissues
with narrower scope area and there are nodes that are
not connected in full. Area distribution of the barrier
wall of glass to form a network with full connection
but narrower than the distribution area without barrier. If we compare it with the distribution area of thesingle barrier as shown in Fig 1-3, the number of barriers also aﬀect the results of deployment. In the areawith two barrier with the same type of barrier, nodes
are connected in full amount less. Suppose the deployment results in the distribution area of the barrier wallof wood that in scenario single barrier can form a fullmesh network, in a scenario with two barriers can notform a full mesh network, although the transmit power
and position coordinates of the beginning of each sensor node.
Results of sensor node deployment such as in Fig.
5 shows the results of deployment with diﬀerent barrier positions namely x1 = 225 and x2 = 275. Diﬀerent
barrier positions when compared to the scenario in Fig.
4 shows the inﬂuence of the position of the barrier tothe results of the sensor node deployment. Deployment
results with the same power and the same initial koordi-• Scenarios with Two Barrier
Testing scenarios using two barrier diﬀerent transmit
power is -25 dB, -28 dB, -31 dB, and -34 dB. To simplify the calculation, using the restriction barrier type
scenario is similar to the same space (barrier equal tox1 and x2 , e.g. wall of wood and wall of wood or brickwalls and brick walls) so that the total value of attenuation barrier is a multiple of the value of the attenuation
barrier. At best deployment testing results obtained at-25 dB transmit power usage. Just as in the scenarioof the barrier, the results of deployment may include
a wider area or the maximum number of connections
compared to the use of smaller emittance.
Fig.4 shows the results of the deployment of sensor nodes transmit power and -25 dB in the area with

wood, as well as the application has not shown that the
shortest path can taken at the time of data transmission
References(a) without barrier(b) brick wall

[1] W. Y. Poe, J. Schmitt, Node deployment in large
wireless sensor networks: Coverage, energy consumption, and worst-case delay, ACM,2009.
[2] Indrawati, Amir D, Analisa korelasi konstanta
propagasi terhadap redaman propagasi gelombang
radio dalam ruang pada komunikasi bergerak, jurnal litek volume 6 nomor 1, hal 11-14, 2009.
[3] C.Y. Chang, C.C. Tsun, C.Y. Chieh, C. H. Ruey,
Obstacle resistant deployment algorithm for wireless sensor networks, IEEE vehicular technology
58:6:2925-2941, 2009.

(c) wall of wood(d) wall of glass

Figure 5: comparison of the results of the deployment
of sensor nodes with diﬀerent barrier positions

nant position would be diﬀerent if the barrier positionchanged. Diﬀerent types of barriers will also provide
diﬀerent solutions. Deployment best results obtainedon deployment with the highest transmit power and 25 dB in the area without barrier. While the results
are less good deployment in get on deployment withsmall emittance (-34 dB) and the area with the type ofbarrier brick wall. In the sensor node deployment, deployment results will be aﬀected by the transmit power,
the location of the barrier, barrier types and numberof barrier.

[4] M. Abbasi, M.S.A Latiﬀ, A. Modirkhazeni, M.H.
Anisi, Optimization of wireless sensor network coverage based on evolutionary algorithm, IJCCN International Journal of Computer Communications
and Networks, Volume 1, 2011.
[5] M. Abbasi, M.S.A. Latiﬀ, Mobility control to improve nanosensor network lifetime based on particle
swarm optimization, International Journal of Computer Applications (0975-8887), Volume 30: No.4,
2011.
[6] Z. Saharuna, Widyawan, S. Sumaryono, Deployment jaringan sensor nirkabel berdasarkan algoritma particle swarm optimization, Universitas
Gadjah Mada, 2012.
[7] R.L. Haupt, S.E. Haupt, Practical genetic algorithm, 2nd Ed. Hoboken, New Jersey: John Wiley& Sons, Inc. 2004.

6. Conclusion
Deployment of sensor nodes in a room with a barrier based on PSO algorithm can form a network withgood connectivity. Deployment results show the eﬀectof transmit power, position and attenuation barrier aswell as the number of barriers to coverage area and theconnectivity of sensor nodes in the network. The existence of a barrier that provides the damping eﬀect of
the transmit power make WSN coverage area becomesnarrower and connections that occur are not as goodas in areas without barrier. System design developed
sensor node deployment has several limitations such as
uneven sensor deployment, the lack of variation of thebarrier in the same room for two-barrier example, using the ﬁrst barrier and the second barrier walls using[8] R.C. Eberhart, Y.H. Shi, Comparison between genetic algorithm and particle swarm optimization,
Evolutionary programming VII: Proc. 1998.
[9] M. Clerc, J. Kennedy, The particle swarm explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evolutionary
Computation, 6(1):58-73, 2002.

Gadjah Mada University – Shizuoka University Joint Workshop on Telecommunications
Gadjah Mada University, Yogyakarta, Indonesia, December 2–7, 2013

A Game-Theoretic Framework with Utility Improvement for Joint Base Station andResource Selection in LTE Heterogeneous Networks
Agus Nurcahyo† , Wayan Mustika† , Widyawan† , and Koji Yamamoto†††

Department of Electrical Engineering and Information Technology
Universitas Gadjah Mada, Indonesia††
Graduate School of Informatics, Kyoto University, Japan
Email: † agus s2te 11@mail.ugm.ac.id, {wmustika, widyawan}@ugm.ac.id

Abstract
In the present paper, a game theoretic frameworkwith utility improvement in an orthogonal frequencydivision multiple access (OFDMA) cellular network is
proposed. Inspired by the cognitive radio technology,
each mobile node selects the most appropriate base station and resource blocks for uplink transmission in adecentralized manner in order to improve the throughput performance and manage the interference. A utility
function is deﬁned for each mobile node, which takes
into account the cross- and co-tier interference, and incentive to choose the base station based on the linkquality. Such a self-organization scheme can be modeled as a potential game, which is guaranteed to converge to a Nash equilibrium. The simulation results
show that the proposed utility improves system capacity in LTE heterogeneous networks.
1. Introduction
Picocells have been proposed as a promising solution for increasing the coverage and capacity of nextgeneration cellular systems, which will beneﬁt both
end-users and network operators. Pico base stations,which are known as pico eNBs (PeNBs) in 3GPP
Long-Term Evolution (LTE) terminology, are costeﬃcient, low-power, and short-range cellular base stations. They are designed to serve a small coverage area,
and they can reuse the licensed spectrum in indoor oroutdoor environments [1].
By embedding low-power cellular base stations inside a macrocell coverage, a heterogeneous network(HetNet) is constructed, and thus the beneﬁt in ofﬂoading the macrocell traﬃc and enhancing the system capacity can be obtained [2]. Recently, the concept of HetNet has been extensively discussed for application in LTE and LTE-Advanced [3]. In co-channel
operation of HetNet, the spectrum resources can beeﬀectively reused, thereby improving the spectral eﬃciency. However, densely deployed picocells in an existing macrocell network could introduce excessive uplinkinterference from macro user equipments (MUEs) topico eNBs. Therefore, mitigating the cross- and co-tierinterference is an indispensable task in HetNet deployment.
In [4] and [5], the feasibility of co-channel operation and interference coordination strategies in HetNets
were investigated. In the study reported in [6–8], theauthors showed that the interference in HetNets canalso be managed by resource partitioning, range expansion and power control techniques. In addition, theauthors of [9] proposed a cognitive radio resource management scheme for an OFDMA network. This scheme
can be used to mitigate the interference while providing
quality-of-service (QoS) guarantee in HetNets.
The other techniques for mitigating the interference
and enhancing the system capacity in co-channel operation of HetNets are self-organization and access control mechanisms. Self-organization refers to the abilityof an autonomous entity to join to the network, learn
the surrounding environment, and allocate the resource
accordingly, without the assistance of a network operator. Access control refers to the connectivity rightsof the users for using a low-power base station. Two
approaches of access control are identiﬁed: closed access and open access. These techniques are mainly discussed in [10–12]. Our previous work in [13] showedthat network convergence can be guaranted in the proposed scheme.
As a contribution to this work, a game theoretic
framework with utility improvement in an orthogonalfrequency-division multiple access (OFDMA) cellular
network is proposed. Inspired by the cognitive radio
technology, each UE acts as an autonomous entity andattempts to choose the most appropriate strategy combination of eNB and resource blocks (RBs) to maximize
its respective utility. The performance of the proposed
scheme is analyzed using a game-theoretic approach by
deﬁning a utility function for each UE, which takes into
account the cross- and co-tier interference, and incentive to choose eNB based on the link quality. In general, the best response strategy, which is deﬁned as thebest of strategy of a player in response to the current

Transmission signal

MUE

Frequency

...

PUE

macro
eNB

...

: used RB: unused RB

pico eNB
Time

Co-tier interference
Cross-tier interference

Figure 1: System model for uplink transmission of HetNet with cross- and co-tier interference scenario.
strategies of the other players, may not converge to aNash equilibrium (NE). However, when the joint basestation and resource selection can be formulated as apotential game, the best response strategy of players isguaranteed to converge in a ﬁnite number of steps to
the NE [14]. The NE, which is the most widely-used solution concept in a non-cooperative game, corresponds
to a steady-state condition that can be used in analyzing the performance of the proposed scheme. Thus, inorder to improvement performance utility, we consider
weighting value in cros and co-tier interference. Finaly,
a weight value in the utility function is adjusted.
The remainder of this paper is structured as follows. The system model of a LTE HetNet is describedin Section 2. Section 3 describes the proposed selforganization scheme for joint base station and resource
block selection based on potential game approach. The
evaluation of the eﬀectiveness of the proposed scheme
is presented in Section 5. Finally, the conclusion ispresented in Section 6.
2. System Model
We consider the uplink transmission in an HetNet with cross- and co-tier interference, as shown in
Fig. 1. Cross-tier interference refers to the interference between diﬀerent network tiers, i.e., interference
between picocell tier and macrocell tier or vice versa.
In addition, interference may occur between neighboring picocells, which is known as co-tier interference. In
the LTE OFDMA system, the system bandwidth W
is divided into K RBs, in which an RB is deﬁned as
the smallest time-frequency resource unit that can beallocated to a UE. P outdoor pico eNBs are randomly
deployed in each sector of macro eNBs. There are UM
macro UEs (MUEs) and UP pico UEs (PUEs) that are
randomly distributed in each sector of macro eNBs andin each picocell, respectively. Thus, the total numberof UEs in three sectors of the macrocell site can becalculated as N = 3(UM + P UP ).
The co-channel operation of macro- and picocells is

Figure 2: Frame structure of the resources in an LTE
OFDMA system. A UE utilizes multiple RBs for uplink transmission; the used RBs are represented by theshaded blocks.
considered such that interference occurs when the UEs
in diﬀerent cells transmit using the same RB. Each sector of the macro eNBs can reuse all the spectrum resources in order to improve the spectrum utilization.
As a consequence, each MUE also experiences significant interference from the other MUEs and PUEs in
the nearby sectors. Furthermore, perfect synchronization of OFDMA network is assumed.
3. Potential Game Approach for Joint Base
Station and Resource Selection

3.1. Game Theoretic Framework
We model the self-organization scheme as a strategic non-cooperative game that has three major components: players, strategies, and utilities. The players are UEs (MUEs and PUEs), which are assumed
to have cognitive radio capabilities. The strategy of
UE i is the combination of eNB and subset of RBs,si = (bi , Ri ) ∈ Si , where bi ∈ B is the eNB that isselected by UE i from the set of eNBs B. The set of
eNBs consists of the sectors of macro eNB and pico(l)eNBs. ki ∈ Ri , 1 ≤ l ≤ L, is an element of the subset
of RBs Ri selected by UE i and |Ri | = L, ∀i ∈ N , isthe number of elements in the subset of RBs Ri . It is
assumed that the number of elements in Ri (L) is predeﬁned at the initial assignment. As shown in Fig. 2,
a UE utilizes L = 5 RBs for transmission. The utilityof UE i is a function of cross- and co-tier interference,and incentive to choose eNB based on the link quality.
In general, the strategic{ form game can be mathe}
matically deﬁned as Γ = N , {Si }i∈N , {ui }i∈N . The
ﬁnite set of players and the set of strategies associated with player i are denoted as N and Si , respectively. The utility function, ui : S → R, maps eachpossible∏ combination of the strategies of all players,
S = i∈N Si , to a real value R. The utility function, which can be used by the players to measure
their respective utility, represents the degree of satisfaction of player i as a function of the strategy chosen

by player i, si , and the strategy of the other players,s−i = (s1 , . . . , si−1 , si+1 , . . . , sN ).

Start
Initial assignment

3.2. Proposed Joint Base Station and Resource
Selection based on Potential Game Approach
The most widely-used solution concept in a noncooperative game is the Nash equilibrium, which represents a steady-state condition resulting when noneof the players deviate from their respective strategies
because if they deviate from their strategies, their respective utility would not increase [15]. A set of purestrategy proﬁles of all players, s⋆ = (s⋆i , s⋆−i ) ∈ S, is aNash equilibrium if and only if the following conditionis satisﬁed:

TuningSensing

Adjust transmission
parameters

Channel measurements
Interference estimation

Learning
Game-theoretic
approach

Figure 3:
scheme.

Self-organization cycle of the proposedui (s⋆i, s⋆−i ) ≥ ui (s′i , s⋆−i ), ∀s′i ̸= s⋆i , ∀s′i ∈ Si , ∀i ∈ N. (1)A potential game [16] is a type of non-cooperative
game where the convergence to a pure strategy Nashequilibrium can be guaranteed as long as sequential
play based on the best response strategy is adopted.
The best response of player i to the strategy proﬁle s−i(t+1)
at time t + 1, si(s−i ), is the strategy that satisﬁes
the following condition [14]
(t+1)

si
(t)

(s−i ) ∈ arg max ui (s′i , s−i ),(t)s′i ∈Si(2)(t)

where (si , s−i ) ∈ S represents the strategy proﬁle attime t.
A strategic game is called an exact potential gameif there exists a potential function P : S → R with thefollowing properties
P (s′i , s−i ) − P (si , s−i ) = ui (s′i , s−i ) − ui (si , s−i ), (3)where s′i ∈ Si , ∀i ∈ N . From (3), it can be inferredthat the information related to the improvement pathof a game can be modeled using the potential function
if the improvement in the utility of a player as a resultof deviating from the player’s strategy is equal to theimprovement in the potential function. In general, thebest response strategy does not always converge to aNash equilibrium. However, when a game can be modeled as a potential game with a ﬁnite set of strategies,
the best response strategy of all players will terminate
in a ﬁnite number of steps to a Nash equilibrium, regardless of the initial condition of the game and theorder of the sequential play [14].
In this paper, UEs, which are assumed to have cognitive radio capability, are able to dynamically sense
the available spectrum, measure the interference received and estimate their interference contributions on
their neighbors, and intelligently tune their transmission parameters by selecting the most appropriate combination of eNB and the subset of RBs in order to improve the throughput performance. As shown in Fig. 3,

the proposed scheme consists of three main phases that
resemble the cognitive cycle [17]: sensing phase, learning phase, and tuning phase.
In the sensing phase, the UE i observes its surrounding environment and obtains the information required for the learning purpose, such as the channel
gain to the neighboring eNBs. The control messages
are transmitted on a common control channel where
the information of the channel gains of the UEs is exchanged through this channel. In order to reduce thesignaling overhead during this phase, a local information exchange based on interference power threshold isconsidered. Thus, UE i exchanges the control message
to the neighboring eNBs within a radius of informationexchange, where the radius is determined by the interference power threshold Ith . We note that the amountof information exchange during this phase depends on
the interference power threshold Ith . Decreasing Ithwill allow UEs to learn more potential interferers since
the radius of information exchange increases. However,since the radius of information exchange increases, thecomplexity of the proposed system will increase due toan increase of information exchange during this phase.
In the learning phase, a game theoretic approachis used to model and analyze the interactions amongUEs, in which the outcome of these interactions canbe predicted and the set of strategies that satisﬁes theutilities of all UEs can be identiﬁed. Using this approach, each UE tries to evaluate its utility for every
strategy combination of eNB and the subset of RBs,
given the current strategies of the other UEs. The proposed utility function for each UE in the LTE HetNetcan be represented as follows:ui (si , s−i ) =
(
L ∑
L
N
∑
∑
−
Gbi ,j pj hbi ,j δk(x) k(y)x=1 y=1−N∑ij=1,j̸=ij)

Gbj ,i pi hbj ,i δk(y) k(x) + αGbi ,i pi hbi ,i .
jj=1,j̸=i(4)iwhere Gbi ,j denotes the link gain between the UE j
and eNB that is selected by the UE i, pj denotes thetransmit power of UE j in each selected RB, x and y
are the index of elements of subset of RBs selected by
UE i and j, and δk(x) k(y) is the interference functionijthat indicates whether or not the elements of the setof selected RBs by the UE i and j are the same: if(x)(y)
ki = kj , δk(x) k(y) = 1; otherwise, δk(x) k(y) = 0. Asi

j

i

j

suming that the total transmit power of UE i is divided
equally among the selected RBs, a radius function from
UE i to eNB that is selected by UE j, denoted by hbj ,i ,indicates whether or not UE i and eNB that is selectedby UE j are on the same radius of information exchange:{
1, Gbj ,i pi ≥ Ith ,hbj ,i =(5)0, Gbj ,i pi < Ith ,where pi denotes the transmit power of UE i in eachselected RB and Gbj ,i denotes the link gain between
UE i and eNB that is selected by UE j.
The ﬁrst two terms in the proposed utility function
take into account the total interference received by eNB
that is selected by UE i and the interference that is
potentially generated by UE i. The last term depends
only on the strategy chosen by UE i, which captures
the incentive to make a handoﬀ to eNB based on thelink quality with an incentive factor α.
Given the proposed utility function ui , we formulate
the potential function as
P (si , s−i )[ L L (
N ∑
N
∑
∑
1 ∑
=
−
Gbi ,j pj hbi ,j δk(x) k(y)ij2i=1 x=1 y=1
j=1,j̸=i
)
]
N
1 ∑
−
Gbj ,i pi hbj ,i δk(y) k(x) + αGbi ,i pi hbi ,i . (6)ji2j=1,j̸=i

The proof to show that the proposed utility function uand potential function P are an exact potential gameis given in the Appendix.
The last part of the learning phase is the best response strategy as explained in (2). In the best response strategy, the UE selects the most appropriate
strategy combination of eNB and RBs in response to
the strategies of the other UEs.

In the tuning phase, the UE adjusts its transmission
parameters such as eNB and the subset of RBs according the result of the learning phase. This can be doneby updating its strategy combination of eNB and thesubset of RBs, and informs all neighboring UEs about
the selected strategy by sending the control message
on the common control channel. The selected strategy
is the strategy that maximizes the utility of the corresponding UE, given the current strategies of the otherUEs.
4. Utility Improvement
Utility improvements is obtained by multiplying aweight value with the potential interference create andinterference received in the proposed function. so inorder to seek improved system performance, the utility improvement function is proposed to evaluate theutility function.
ui (si , s−i ) =
(
L ∑
L
N
∑
∑
−nr
Gbi ,j pj hbi ,j δk(x) k(y)x=1 y=1− ncij=1,j̸=iN∑

j)

Gbj ,i pi hbj ,i δk(y) k(x) + αGbi ,i pi hbi ,i . (7)j=1,j̸=iji

Evaluation of the utility is done by running thegame until the nash equilibrium conditions and take thelast value of the game. the game will be repeated witha combination of values of diﬀerent nc andnr . Eachgame is complete will take the last system throughput
value, and compare these values to get the highest value
would indicate an improvement in the utility function
5. Performance Evaluation

5.1. Simulation Model
We consider a network with 19 cell sites, each ofwhich has three hexagonal sectors as shown in Fig. 4.
The inter-site distance (ISD) is 500 m, which is in accordance with the 3GPP simulation assumption case1 [2]. Tri-sector antenna with 120◦ beamwidth at eachsector of macro eNBs is assumed [18]. In each sectorof macro eNBs, 10 MUEs are randomly and uniformly
distributed. Thus, 30 MUEs exist in a macrocell site
during network setup. Outdoor pico eNBs are randomly deployed in each sector of macro eNBs, which
are equipped with omnidirectional antennas. 2 PUEs
are randomly distributed inside the picocell coveragearea. The performance statistics of three central sectors are observed, while the other sectors of diﬀerent

: eNB: MUE: PeNB: PUE

1200

Table 1: Simulation parameters
Parameters
Values

900
600

Macro eNBXHexagonal grid, 19 cell sites
3 sectors per site
Circular cell, 1 sector per cell

0−300

288.68 m (ISD = 500 m), 40 m
128.1 + 37.6 log10 (dm [km]) dB[2]140.7 + 36.7 log10 (dp [km]) dB[2]8 dB (macro), 10 dB (pico)[ ()2 ]AH (θ) = − min 12 θ3θdB , Am
θ3 dB = 70◦ and Am = 20 dB

standard deviation
Pico eNB antenna
Antenna gain eNB
Antenna gain UE
UE power class
Thermal noise
density
Number of picoeNBs (PeNBs)
Number of UEs
Min. distancemacro eNB-MUE
System/RB
bandwidth
bandwidth
Number of RBs
Carrier frequency
Incentive factor αTraﬃc model
Number of
topologies

300
y (m)

Cellular layoutof macrocell
Cellular layoutof picocell
Macrocell
picocell radius
Macro path loss
Pico path loss
Shadowingstandard deviation

Omnidirectional
14 dBi (macro), 5 dBi (pico)
0 dBi
23 dBm−174 dBm/Hz
4 PeNBs/sector

−600
−900
−1200
−1200 −900 −600 −300

0 300 600 900 1200
x (m)Figure 4: Simulation model of HetNet with 19 macrocell sites.
spect to the strategy space, each UE utilize 5 RBs(L = 5) for uplink transmission. At the initial assignment, these RBs are randomly selected under thecondition that interference between MUEs in the same
sector of macro eNB, which is known as intra-tier interference, is avoided. For the sake of simplicity, we assume that the eﬀect of fast fading is averaged out. Furthermore, it is assumed that the total transmit power
is divided equally among the selected RBs. The otherrelevant parameters used in the simulation are summarized in Table 1.

10 MUEs/sector, 2 PUEs/pico eNB
5.2. Simulation Results
35 m
10 MHz (System), 180 kHz (RB)
50
2 GHz
1.0
Full buﬀer
500

cell sites are considered only as the interference contributors.
To enhance the spectrum utilization, co-channel operation of HetNet is considered; and thus, all spectrum
resources can be reused in every sector of macrocells
and in each picocell. We consider 10 MHz system bandwidth, which constitutes a total of 50 RBs. With re-

Figure 5 shows the evaluation of the utility function
with system throughput at the end of each game with
diﬀerent combinations of nc and nr . The highest valueof throughput system showed the best combination ofnc and nr . As show ﬁgure 5 the best performance isachieved when nc and nr are set to 2 and 6. Thus, theoptimal utility function can be formulate asui (si , s−i ) =
(
L ∑
L
N
∑
∑
−6
Gbi ,j pj hbi ,j δk(x) k(y)x=1 y=1−2i

j=1,j̸=iN∑

j)

Gbj ,i pi hbj ,i δk(y) k(x) + αGbi ,i pi hbi ,i . (8)j=1,j̸=ijiFigure 6 and 7 shows the impact of the interference
power threshold Ith during sensing phase on the convergence of system throughput. The system throughput represents the sum of the throughput of all UEs

System throughput (Mbps)

System throughput (Mbps)

270
260
250
240
230
220
210
200
190
180
170
160

Global sensing
Ith = -80 dBm
Ith = -70 dBm
Ith = -60 dBm
00

10

20

30

40

50

60

70

80

270
260
250
240
230
220
210
200
190
180
170
160
150
140

90

Global sensing
Ith=-80dBm
Ith=-70dBm

Ith = -60 dBm

0100 200 300 400 500 600 700 800 900 1000
Iteration t

Combination (nc,nr)(MUEs and PUEs) in the central macrocell site andthe throughput of UE i that utilizes L RBs can be calculated as
Ti = WRBL∑
l=1

log2 (1 + γk(l) ),(9)iwhere WRB is the RB bandwidth and γk(l) is the rei
ceived SINR on RB l. Starting with the initial assignment (t = 0) where each UE connects with its
initial eNB (either macro eNB or pico eNB) and randomly selects the subset of RBs for transmission, each
UE sequentially goes through the phases of the selforganization cycle shown in Fig. 3. In the ﬁnal phase,
the UE updates its strategy so as to maximize its
own utility, and this eventually improves the systemthroughput. After some iteration steps, the steadystate condition, which also indicates the convergence
to a Nash equilibrium, is reached. At this point, noUE can improve its own utility by choosing anotherstrategy combination of eNB and subset of RBs that is
diﬀerent than its best one.
From Figure 6 and 7 we also see that the systemthroughput at the convergence state decreases as Ith
increases. In other words, there is a trade-oﬀ between
the system throughput at the convergence state and theradius of information exchange, which is determined by
the value of Ith . Using the global information during
the sensing phase, which is referred to as global sensing, the upper bound of the system throughput at theconvergence state is achieved. However, this is impractical in a large network since the UE has to exchange
the information to all eNBs in the network.
Figure 7 shows the system throughput improved
when compared with ﬁgure 6. This proves the utility
evaluation to obtain the optimal utility function can
improve the capacity system.

Figure 6: Impact of parameter Ith on the convergenceof system throughput.

System throughput (Mbps)

Figure 5: System throughput in each game with combination nc and nr .

270
260
250
240
230
220
210
200
190
180
170
160
150
140

Global sensing
Ith=-80dBm

Ith=-70dBm

Ith = -60 dBm

0100 200 300 400 500 600 700 800 900 1000
Iteration t

Figure 7: Impact of parameter Ith on the convergenceof system throughput using optimal utility function.

6. Conclusion
A game theoretic framework with utility improvement in an orthogonal frequency-division multiple access (OFDMA) cellular network is proposed in this paper. Inspired by the cognitive radio technology, each
mobile node selects the most appropriate base station
and resource blocks for uplink transmission in a decentralized manner in order to improve the throughput performance and manage the interference. The
proposed a game theoretic framework with utility improvement can be modeled as a potential game, which
is demonstrated to converge to a Nash equilibrium aslong as distributed sequential play based on the best
response strategy is adopted. The simulation results
show that the proposed scheme improves the system
capacity.

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