Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
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Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume 1, Issue 3

1

WPBR: Weekly Prediction based Bandwidth Reservation Scheme for Macrocellular Wireless Networks in Urban
Areas

79

Adel Amani *, Hossein Pedram ** Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran2

Online Adaptive Fuzzy Logic Controller Using Genetic Algorithm and Neural Network for Networked Control
Systems88

Pooya Hajebi*, Seyed Mohammad Taghi AlModarresi**Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran

3

Improving Coverage and Capacity in Underwater Acoustic Cellular Networks

99

Amirmansour Nabavinejad *, Samar Shahabi Ghahfarokhi *** Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran, ** Faculty of Electrical Engineering and Information
Technology, Ilmenau University of Technology, Ilmenau, Germany

4

Research on the Scheme and Performance of Linear SA in CDMA Application

107

Jijiang Chen1, Jing Zhou2, Jianfeng Lu3
1.Nanjing University of Posts and Telecommunications, Nanjing P.R. China, 2.PAP Division of XX city, Nanjing P.R. China
3.The XX Regiment of The Air Force, Nanjing P.R. China

5

Point-to-Multipoint and Multipoint-to-Multipoint Services on PBB-TE System
Wonkyoung Lee*, Chang-Ho Choi*, Sun-Me Kim*
*Optical Internet Research Department, Electronics and Telecommunications Research Institute, 161 Gajeong-dong, Yuseong-gu, Daejeon, 305-350,
Korea

116

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

79

WPBR: Weekly Prediction based Bandwidth
Reservation Scheme for Macrocellular Wireless
Networks in Urban Areas
Adel Amani *, Hossein Pedram ** Department of Computer Engineering and Information Technology,
Amirkabir University of Technology, Tehran, Irana.amani64@gmail.com, pedram@aut.ac.ir

Abstract-—One of the major challenges in the cellular networks
is to guarantee the quality of service of the ongoing calls byprioritizing them over the new calls. An operative approach toprioritize the hand off calls over the new calls is by reserving
bandwidth for the ongoing calls of mobile stations in thepotential next cell that they may visit. Improving the predictionof potential next cell that a mobile station may visit will cause
better bandwidth utilization. In this paper, we propose weekly
prediction based bandwidth reservation scheme. The proposed
scheme, WPBR, improves prediction by means of storing weekly
movement probabilities of mobile station based on Markov
modeling techniques. In order to decreasing the storage spacethat is needed for storing the mobile station’s movement
probabilities, we adopted a dynamic hashing approach.
Simulation results show that the weekly prediction in the
proposed scheme, significantly improves bandwidth utilization,and the adopted dynamic hashing approach caused the overheadof storage space to be acceptable.
Index Terms—Macrocellular Wireless Networks, Bandwidth
Reservation, Mobility Prediction, Markov Modeling Techniques,
Dynamic Hashing.

I. INTRODUCTIONI

N recent years there has been a rapid development in mobile
and wireless cellular networks. One of the most major
challenges in the mobile and wireless cellular networks is toefficiently utilize the bandwidth while the quality of service(QoS) of the ongoing calls should be guaranteed. However,
due to user mobility, establishment and management ofconnections are very difficult.

A mobile and wireless cellular network consists of a lot ofcells that serve a large number of mobile users with various
mobility patterns and a number of applications. A cell involves
a base station (BS) and a number of mobile stations (MSs).
When an MS moves from the coverage of a cell into theManuscript received July 9, 2012.
Hossein Pedram is with Department of Computer Engineering andInformation Technology, Amirkabir University of Technology, Tehran, Iran
(Tel: +98-2164542701; email: pedram@aut.ac.ir).
Adel Amani was with Department of Computer Engineering and Information
Technology, Amirkabir University of Technology, Tehran, Iran (Tel: +989183733912; email: a_amani@aut.ac.ir).

coverage of another cell and there is an ongoing call, the callshould be handed over from the prior cell to the new cell. This
process is called hand off. If there is no available bandwidth toserve the hand off call in the new cell, then this call will notcomplete and will be dropped by the new BS. There are threeelementary QoS parameters in the cellular networks: the handoff call dropping probability (CDP), the new call blocking
probability (CBP) and the bandwidth utilization (BWU).
From the user point of view, dropping of an ongoing call ismore undesirable than blocking of a new call. In the other
word, ongoing calls must prioritized over new calls. In
literature, several approaches have been proposed to givepriorities to hand off calls [1]. One most common approach toprioritize hand off calls over new calls is by reserving channels
for hand off calls. Schemes that utilize mentioned approach areclassified as channel reservation schemes [1].
Reserving some bandwidth at each BS could only beutilized by hand off calls. Since any such reservation
inevitably increases the CBP, and decreases the system’s
BWU, it is very important to make reservations as sparingly as
possible. One astute way for the purpose of sparing reservation
is to reserve bandwidth for each MS based on its predicted
path through the cellular network. At first, the potential next
cell that an MS may visit during its lifetime is predicted basedon some information about the MS’s mobility, and then,bandwidth reservation will be made only in the potential next
cell that the mobile station may visit. In [1], schemes thatutilize mobility prediction approach to reserve bandwidth areclassified as predictive mobile-oriented channel reservation
schemes (PMOCR).
The information that employed in the mobility prediction
could be categorized into two types: current mobility
parameters of MSs and observation histories of MSs. The
schemes that using current mobility parameters like velocity,
direction, angle or distance in their prediction, typicallycompute the probability of events occurring, depending on thevalues of these parameters. In [1], it mentioned that the key
limitation of prediction by means of current mobility
parameters is the requirement of positioning hardware.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Another problem in these approaches is the overhead to obtain
mobility parameters. However, the serious problem with theseschemes is hidden behind the parameters and the mobility
models that used in their predictions. The majority of theseapproaches utilize mobility models like random waypoint or its
variants that fail to represent all or some of mobility
characteristics (temporal and spatial dependency of velocity
and geographic restrictions) that mentioned in [2]. But thepredictions are unrealistic without representing mobility
characteristics.
History-based schemes use historical mobility patterns of
MSs to calculate their potential next cell. These schemes are
based on a reasonable assumption that mobility of an MS
shows some regularity in urban areas. In [1], it mentioned thatthe key limitation with all history-based schemes is theoverhead to store and update observation histories for theMS’s mobility.
There are two common approaches to work withobservation histories: data mining techniques and markov
modeling techniques. In data mining approach, user mobility
patterns are mined from the history of mobile user trajectories.
The MS’s next movement is predicted by using mobility rules,
which are extracted from the user mobility patterns. These
trajectories are saved over time and the needed storage space
will grow over time too. In markov modeling approach,potential next cell for an MS predicted based on historicalmobility of the MS using Markov modeling. The Markovian
mobility model defines distinct probabilities for movementfrom a given cell to each of its neighbors. Thus the needed
space for storing movement probabilities in markov modelingapproaches does not exceed a particular limit.

Needed Space (k byte)

250

200

150

100
Markov Modeling Approach
Data Mining Approach

50

0
1

3

5

7

9

11 13 15 17 19 21 23 25 27
Day

Figure 1.Comparison of needed storage space by data mining approach andmarkov modeling approach

In Figure 1 needed storage space of the markov modelingapproach that suggested in [3], is compared to the data mining
approach that explained in [4]. The comparison is done in amacrocellular wireless network with 25 cells and 95 mobile
stations that move based on real time traces that have obtained
through the reality mining project [7]. Our simulation results

80

show that the needed space of data mining approach isextremely more than needed space of markov modelingapproach. Thus the data mining approaches are not rightchoices for mobility prediction in the macrocellular wirelessnetworks with a large number of mobile stations in urban area.
In this paper, we propose a predictive mobile-oriented
bandwidth reservation scheme that improves prediction bymeans of storing weekly movement probabilities of MSs basedon Markov modeling techniques. Also, we adopted a dynamic
hashing approach in order to decreasing the storage space thatis needed for storing the movement probabilities. Simulation
results show that the weekly prediction in this novel strategy
significantly improves bandwidth utilization, and the adopted
dynamic hashing approach fairly overcomes the overhead ofstorage space.
The remainder of this paper is organized as follows: Section
II gives an overview of related works. In section III the novel
weekly-prediction-based bandwidth reservation (WPBR)
strategy will be introduced. Section IV describes simulation
environment and provides simulation results and discussions.
Section V gives a plan for future works and concludes thepaper.
II. RELATED WORKS
Many history-based mobility prediction schemes have been
proposed to estimate the potential next cell that an MS may
visit. In [3], a dynamic location management scheme havebeen introduced, in which potential next cell for an MS
predicted based on historical mobility of the MS using Markov
modeling. The Markovian mobility model defines distinct
probabilities for movement from a given cell to each of its
neighbors. These probabilities are stored in a matrix that called
transition matrix. Each cell records movement probabilities
based on the frequency of hand offs to its adjacent cells foreach MS that is on a call inside the cell. Movement
probabilities are updated at specified refresh intervals.
Refreshing process may take place hourly, daily or more
infrequently. The proposed algorithm for obtaining the
movement probabilities is given as follows:
1) For every hand off to a neighbor cell, the hand off tallyfor that particular neighbor is incremented, or the cell isrecorded as a neighbor if not previously seen.
2) On a cell refresh, each individual hand off tally is divided
by the total number of hand offs to obtain the probabilityof moving to each cell.
3) Stored movement probabilities are smoothed using asimple exponentially weighted moving average as in (1).
4) Each hand off tally is reset to zero and the process is
continued.
prob(c,i) = w×prob(c,i−1) + (1−w)×newProb(c).

(1)

For each MS the probability of moving to cell c in theinterval i obtained from (1), in which w is the smoothing
factor. Each cell requires two hash maps, one to store the hand

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

off frequencies that resulted in newProb(c), and one to store
the smoothed movement probabilities that used to predict
potential next cell. MS’s mobility patterns attain a steady state
and are predictable after a series of repeated cell refreshes.
However, a single transition matrix for the whole day willcause unrealistic predictions [5]. In urban areas, a considerable
number of MSs are moving from home to work every
morning, and coming back from work to home in theafternoon. So, if an MS came back to home in the same paththat has gone to work and refresh interval was greater than orequal to a day, a single transition matrix will give equal
probability of occurrence for both of the movement paths (towork and to home) at any time of the day. In order toovercome this problem, the day is divided into eight fixed timeslots (00:00-6:00, 6:00-8:00, 8:00-10:00, 10:00-12:00, 12:0014:00, 14:00-16:00, 16:00-18:00, 18:00-24:00). Individualtransition matrixes are created and maintained for each of theeight time slots [5]. This approach will cause more realistic
mobility prediction, but on the other hand it increases thestorage space that is needed for storing the movement
probabilities.
III. WEEKLY PREDICTION BASED BANDWIDTH RESERVATIONSCHEMEIn this section, proposed radio resource management
scheme would be introduced. When MS i moves into thecoverage of cell c in time slot t of day d and there is an
ongoing call, a hand over request will be sent to call admission
control module of cell c. Then call admission control module
gets the available radio resources from radio resource
management module. If there was not enough resource, thecall request will be rejected. If there was enough resource, calladmission control module will allocate the resources to MS i.
Subsequently call admission control module will get thepotential next cell from prediction module and will send a
bandwidth reservation request to the resource reservation
module of potential next cell, and then resource reservation
module reserves requested bandwidth if possible. The structureof proposed radio resource management scheme has been
depicted in Figure 2.
Call Request

Movement
Probability

Prediction
ModuleFinding
Potential
Next Cell

Radio Resource
Management Module

Resource Reservation

TABLE I.

PSEUDO CODE DESCRIBING THE CALL ADMISSION CONTROLMODULE//method gets MS id,i , and required radio resource, rrr, as input
arr = available radio resource from radio resource management module
if(rrr<arr)
accept the request
nCell = get the potential next cell from prediction module
send reservation request to nCell
else
reject the request

A. Weekly Behavior Similarity
In [6], authors have tried to expand the comprehending ofpragmatic user behavior in macrocellular wireless networks,
utilizing real time traces. The traces have obtained through thereality mining project [7]. We use their traces in this paper too.
In [6], the parameter  defined as in (2):
 =



       

.

(2)

The ratio  indicates that to what extent the user mobility
behavior is predictable. The swing in value  from the day ito the day i+7 of each week is seen to follow a periodic
pattern. Comparable weekly patterns suggest repetitive
patterns in user movements. It seems that mobility pattern of
MSs is repetitive with a period equal to a week.
Now, we introduce another parameter to indicate to what
extent the user mobility behavior is similar to its mobility
behavior in the previous days. Behavior similarity of MS i in
the day n to its behavior in the day n-j that indicated by ,  −  defined as in (4):! "# ,  =!$%&' () *+%', *ℎ.* / + 0+,+*, "'11 2 + 3.4 .
 ,  −  =A,  −  =Available
Resources

Resource Allocation

In Table I, the main steps of the call admission control
module are described using pseudo code. In the rest of thissection, the structure and the action of prediction module
would be introduced.

∑<=> ?@@ 8 67 8 , ,7 8 ,9: ;∑<=> ?@@ 8 7 8 ,

.

(3)
(4)

Also, behavior similarity of all MSs in the day n to their
behaviors in the day n-j that indicated by A,  −  defined
as mean of similarity ratios of all the MSs in the network as in(5):

Call Admission
Control Module

Required
Resources
Available
Resources

Accept/Reject
Request81

Reservation
Request toPotential
Next Cell

Figure 2. The structure of proposed radio resource management scheme∑<=> ?@@ 7 B7 ,9:


CB

.

(5)

A plot of A28,28 −  for 0≤ j<28 is given in Figure 3. It is
obvious that  ,  =1 and so A,  =1.

A,  −  could be used as a parameter to indicate that towhat extent the mobility behavior in the network is similar tothe previous days. Figure 3 illustrates that in the macrocellular
wireless networks, the mobility behavior in the day i is similar
to the mobility behavior in the day i-7. Therefore, it seems that

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

mobility prediction based on weekly observations is more
pragmatic than mobility prediction based on daily
observations.
1

Ŝ(28,28-j)

0.8

JF
JJJ, *;   , *H ∑<=> ?@@ 7 B7 ,;9:,I


CB

82

.

(7)

JJJ28,10; 28  , *H for 14≤ j<28 and 0≤t’≤10 is
A plot of JF
given in Figure 4. It is obvious that TSO n, t; n, t =1 and so
JJJTSn, t; n, t =1.
The time slot similarity ratio, JJJ
TSn, t; n  j, tH , could be
used as a parameter to indicate that to what extent the mobility
behavior in the network is similar to the previous time slots.
Figure 4 illustrates that in the macrocellular wireless networks,
the mobility behavior in the time slot t of day d is similar to themobility behavior in the time slot t of day d-(x*7) that x ϵ {1,2, 3, …}.

0.6
0.4
0.2
0
0

7

14
Day (28-j)

21

28

Figure 3. Behavior similarity of MSs in day 28 to their behavior in theprevious days

Similarity ratio could be defined for time slots either.
Assume ! "# , , * indicates the number of times that MS i
visits cell k in time slot t of day n. Behavior similarity of MS iin the time slot t of day n, to its behavior in the time slot t’ of
day n-j that indicated by F , *;   , *H defined as in (6):F , *;   , *H ∑<=> ?@@ 8 67 8 ,, ,7 8 ,9:,I ;∑<=> ?@@ 8 7 8 ,,.

(6)

Behavior similarity of all MSs in the time slot t of day n totheir behaviors in the time slot t’ of day n-j that indicated by
JJJJF, *;   , *H defined as mean of similarity ratios of all theMSs in the network as in (7):

B. Weekly Prediction
And now, we introduce our weekly predictive bandwidthreservation strategy. In order to predict movement of each MS,
the week is divided into 56 fixed time slots (each day is
divided into eight time slots as explained in [5]). Individualtransition matrixes are created and maintained for each of the
56 time slots during the week. For MS i the probability ofmoving to cell c in time slot t of the day d indicated by ", *, 3 which obtained through the frequency of hand offs
to cell c during time slot t of day d. Movement probabilities
will be updated using (8) at the end of each time slot. The
transition matrix for MS i in time slot t of day d indicated by/+, *, 3 .
S ", *, 3  T U S ", *, 3 V 1  T U 'TS(&" .

(8)

When MS i moves into the coverage of new cell in time slot
t of day d and there is an ongoing call, prediction module ofthe cell detects the corresponding transition matrix and then,searches the matrix for maximum transition probability

0.4
0.35

ŤŠ(28, 10, 28-j ,t')

0.3
0.25
0.2
0.15
0.1
0.05
0
time slot time slot time slot time slot time slot time slot time slot time slot time slot time slot time slot time slot time slot time slot time slot
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
day
day
day
day
day
day
day
day
day
day
day
day
day
day
day
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Time slot t' of day 28 - j
Figure 4. Behavior similarity of MSs in time slot 10 of day 28 to their behavior in the previous time slots

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012(W ).
W  %.X

Y :     Z ", *, 3 [.

(9)

As it mentioned, time slots are fixed and the MS behavior
fluctuates from day to day. Hence a movement might becaptured in the adjacent time slots which may result inincorrect predictions. It is difficult to use variable time slots
and this problem will not be solved even if variable time slots
be used.
In order to overcome this problem, the prediction could bemade with the aid of transition matrix of the adjacent timeslots. The mobility behavior of MSs in time slot t of day 3 ismoderately similar to their mobility behavior in time slot t1and t+1of day 3 of the previous weeks as it could be seen in
Figure 4. Thus, in order to resolve the problem of MS’s
behavior fluctuation, prediction module could makepredictions based on the transition matrixes /+, * − 1, 3 ,/+, *, 3 and/+, * + 1, 3 .
If W was greater than or equal to a minimum threshold , the bandwidth reservation request will be sent to the cellthat maximizes (9). Otherwise, if W was less than  , theprediction will be made based on AW that obtained from
(10). In the other word, if W was less than  , thebandwidth reservation request will be sent to the cell thatmaximizes (10).
AW =
9]
^

%.X

Y :    ×  ", * − 1, 3 +9]^Z\ ×  ", *, 3 +×  ", * + 1, 3 [.

The algorithm effectiveness affected by choosing
appropriate value for  . Choosing a high value for results in smoothed prediction, but also, increases the numberof accesses to the transition matrixes from 1 to 3. For eachadjacent cell c, the probability  ", *, 3 will be equal to 0,
and so, W will be equal to 0 until the first movement is
captured in the time slot t in the day d. After capturing the firstmovement in the time slot t of day d, ∑∀  ", *, 3 will beequal to 1. Hence, if the number of adjacent cells is n, theminimum value for W will be 1`. Thus,  should begreater than or equal to 1`.Assuming the number of adjacentcells to be 6,  should be greater than or equal to 0.16J. The
value of  was chosen to be 0.2 to make more accurate
predictions.
The weighting factor q in (10) indicates that to what extent

AW is affected by adjacent time slots. If any movement still

not captured, q should be equal to 0 in that time slot.
Otherwise, it should be greater than 0. So, q obtained from
(11):
\ = \′ × ∑Y:    ", *, 3 .

It seems that \′ should be greater than or equal to 0. 3J. If \′was less than 0. 3J, the current time slot movement probabilities
is less effective than adjacent time slot movement probabilities
in calculating AW , and it does not makes sense. The value of\′ must be proportionate to behavior similarity of MSs to their
behavior in time slots t, t-1 and t+1. Thus, there is a direct
JJJJ). The
correlation between \′ and time slot similarity ratio (Fparameter \′ could be obtained from (13) for each time slot t.
\ I : =JJJJ
B ,; 9:×d ,
.
JJJJ
JJJJ ,; 9:×d , eB
JJJJ ,; 9:×d ,e
B ,; 9:×d ,9 eB

\I =

(11)

(12)

ij` l

∑gmnk f]Ig h
i`dl

.

(13)

In (12) the parameter JJJJ
F is the time slot similarity ratio and\ I : indicates the correlation of mobility behavior in time slot
t to mobility behavior in time slot t of j weeks ago.\′ obtained
from averaging out the values of \ I : for j={1, 2, …, i`7l}.
The parameter \′ could be obtained at the beginning of eachtime slot or could be obtained for all time slots after an initialperiod (about a month). Based on reality mining traces thatused in this article, the value of \′ was chosen to be between
0.4 and 0.5 to make more accurate predictions.
TABLE II.

(10)

83

PSEUDO CODE DESCRIBING THE PREDICTION MODULE//method gets i, t and d as input
qrst = 0.2
σ=0
qruv
=0
s
wruv
= nulls
while(there is another adjacent cell)
c = next adjacent cellσ += qs w, x, y)if(qs w, x, y >qruvs
qruv= qs w, x, y
s
wruv
=c
s
>qrst )
if(qruvs
ruv
return ws
else
z ruv
q
= 0, { = 0.5∗σswhile(there is another adjacent cell)
c = next adjacent cell}9{p = { × qs w, x, y +6qs w, x − }, y + qs w, x + }, y ;
~
ruv
zs )
if(p>q
z ruv
q
=p
s
wruv
=c
s
z ruv
= =0)
if(q
s
 = 0 ruvq=0swhile(there is another adjacent cell)
c = next adjacent cell += qs w, x, y − } ruvif(qs w, x, y >q)sq ruv= qs w, x, y − }
s
wruv
=c
s
return wruvs

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

When a BS attaches to the cellular network, it has nobackground information about the mobility patterns. Henceany prediction and reservation will not be made in the initialperiod. The mobility pattern of MSs in day 3, is moderately
similar to the mobility pattern in day 3  1 and day 3 V 1 ofthe previous weeks as it could be seen in Figure 3. So, in orderto improve prediction in the initial period, prediction module
could make prediction based on the transition matrix for the
MS in time slot t of day 3  1, /+, *, 3  1 .
The prediction module of each cell gets i, t and d as inputs
that indicate the MS id, the time slot and the day of week
respectively. At the end, prediction module returns thepotential next cell that MS i may visit, and subsequently
bandwidth will be reserved in the predicted cell. In Table II,
the main steps of the prediction module are described using
pseudo code.
C. Storing Movement Probabilities
Weekly prediction needs to store and update transition
matrixes weekly that require more storage space. In weekly
prediction, for each MS, there are 56 time slots whereas indaily prediction 8 time slots should be traced for each MS. It
seems that required storage space for weekly prediction is 7
times the required storage space for daily prediction. However,in practice, some time slots will never capture any movements.
Simulation results show that in actual traces, required storage
space for weekly prediction is 3 to 4 times the required storage
space for daily prediction. This ratio could be decreased to 2.5
using dynamic hashing approach. Decreasing the required
storage space in macrocellular wireless networks is crucial due
to existence of a large number of MSs.
In order to storing movement probabilities, hashing has beensuggested in [3]. The hash function maps MS id into the stored

84

probabilities. When an MS, for the first time, moves into thecoverage of new cell, all the space that is needed formovement probabilities will be allocated to it. However, as it
mentioned, some of the time slots will never capture any
probabilities. In this work, a dynamic hashing approach has
been introduced to allocate just the required storage space toan MS.
Dynamic hashing promises the flexibility of needed storage
space while preserving the fast access times expected fromhashing [8]. Dynamic hashing is a type of hashing which treatsa hash as a bit string. The hash function -2 returns a uniquebinary number that its first b bits will be used to figure out
where they will go in the hash table. Additionally, b is thesmallest number such that the first b bits could be the same forutmost &W keys, that &W is the bucket size.
In this article, in order to simplify hashing, the binary formatof keys is used as the bit string. The transition matrix of MS i
in time slot t of day d indicated by /+, *, 3 . So the key may
consist of 3 fields, MS id, time slot and the day. Now thequestion is, how first b bits should be structured and what
fields must first b bits consist of?
The plot of time slot similarity that had been depicted in
Figure 4 has been shown again for the last week in Figure 5. In
this sample, similarity of last week time slots to the reference
point, time slot 10 of day 28, has been depicted. Square shapedata points indicate the most similar time slots of the mostdissimilar days to the reference point time slot in the last week.
As it mentioned, the most similar time slot in previous days toa reference point time slot is the same time slot in previous
days. Triangle shape data point indicates the most dissimilar
time slot of the most similar day to the reference point timeslot in the last week. As it mentioned, the most similar day inŤŠ(28, 10, 28-j ,t')

0.32

0.27

0.22
0.22

0.22

0.17

0.12

0.07

0.02
time slot
10
day
20

0.07

time slot
10
day
21

time slot
10
day
22

time slot
10
day
23

time slot
10
day
24

time slot
10
day
25

time slot
10
day
26

time slot
10
day
27

time slot
10
day
28

Time slot t' of day 28 - j
Figure 5. Behavior similarity of MSs in time slot 10 of day 28 to their behavior in the last week time slots

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012previous weeks to a reference point day is the same day inprevious weeks. As it could be seen in Figure 5, square shapedata points are more similar to the reference point time slot
than triangle shape data point. In the other word, mobility
behavior of MSs in different time slots of a day varies morethan mobility behavior of MSs in the same time slots ofdifferent days. Thus, the number of new entries that captured
in different time slots is more than new entries that captured indifferent days. Hence hash could be structured as following:
1) First b bits could consist of MS id, i.
2) First b bits could consist of some of the most significant
bits of time slot, t.
3) If first b bits were same for more than &W keys, first b
bits shall extend. The extension shall done to theremaining bits of time slot and then encompass the day
bits, d.
At first, the set of i and 2 most significant bits of t could be
used in construction of the hash table. For example, the entry/6,5,3 will go into the index 11010 of hash table. In somecells, this set could be adequate to construct the hash table. If
there was an entry that causes the bucket size exceeds &W ,the hash table size will be doubled.
⁞⁞

001,00001,01/1,5,3/1,4,1

001,10/1,2,2/1,3,2/1,0,0/1,1,2

001,11⁞/1,6,1/1,7,1⁞
2 most significant bits of t(a)

MS id (i)⁞/1,0,0⁞

001,000/1,2,2

001,001
001,010001,100/1,4,1/1,3,2

Simulation results show that for the majority of cells, theundesirable increase in the hash table size will persist to acertain extent, and the dynamic hashing approach moderately
overcomes the overhead of storage space. Moreover, dynamic
hashing causes storage space to increase imperceptibly. Also it
is possible to decrease the storage space by deleting transition
matrixes that will not be used any more. However, opportunity
to decrease storage space is not investigated here.
IV. SIMULATION AND RESULTS
In order to evaluate the performance of proposed weeklyprediction-based bandwidth reservation (WPBR) strategy, thedaily prediction based bandwidth reservation (DPBR) scheme
has been implemented and simulated for comparison. The
algorithms of daily prediction that described in section II andweekly prediction that described in section III have been
implemented using Java.
A. Simulation Model
The data set that have been used in this study, developed byresearchers at MIT Media Laboratory, and came from Nokia
6600 phones programmed to automatically run the ContextLog
application as a background process at all times [7]. These realtime mobility traces have been mapped into a macrocellular
network with 25 hexagonal cells that each cell has 6 adjacentcells. Table III summarizes the various simulation parameters
whose values were empirically chosen to represent the most
realistic scenario for the simulation. Each simulation was
carried out for 28 days of real time traces in the macrocellular
network. It is assumed that the arrival rate of new calls is a
Poisson distribution with a mean arrival value of 0.5.
TABLE III.

001,110/1,7,1/1,7,4

001,111⁞/1,5,3

3 bits of t(b)

Figure 6. An example of adopted dynamic hashing approach,(a) before entering }, ,  , (b) after entering }, , 

SIMULATION PARAMETERS

Parameter

Value

Description of parameter!

25

Number of cells simulated

95

Number of mobile stations in system

30MbpsMaximum bandwidth capacity of a cell

 ]

3Mbps

Average bandwidth requirement for a callA

0.5req/s

Average connection arrival rate

A

235 s

Average call duration

T

0.5

The weighting factor that used in (1)\0.5

The weighting factor that used in (8)



0.2

The threshold that indicates which time slots
should participate in prediction!

001,101

MS id (i)An example of dynamic hashing approach has been depicted
in Figure 6. It has been assumed that for this particular
example, the bucket size is 2. Before entering /1,7,4 , theset of i and 2 most significant bits of t is adequate to construct
the hash table as it has been shown in Figure 6(a). In order toenter /1,7,4 , the hash table should be doubled as in Figure6(b). Likewise, other bits of the key could be included in theset and the hash table size could increase.

/1,6,1

001,011⁞/1,1,285

aa. This number came from number of MSs that have been traced in reality mining project [7]Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Figure 8 compares the new call blocking probability of the
WPBR strategy with the DPBR scheme for average connection
arrival rate 0.5. March of progress in CBP is similar to what
expressed about bandwidth utilization before. For the WPBR
scheme, CBP increases for an initial period (about a week) andthen decreases with improvement in the prediction and cause
the WPBR scheme to gain lower CBP.

Figure 9 compares the hand off call dropping probability of
the WPBR strategy with the DPBR scheme for averageconnection arrival rate 0.5. For the DPBR scheme, CDP
decrease to an extent and after that leveled off at about
0.15 × 109^ . For the WPBR scheme, decrease in CDP for an
initial period is less than decrease in CDP for the DPBR. The
trend line of the WPBR scheme shows that CDP is dwindlingby the time. . As the WPBR scheme distinguishes between
days of the week, its CDP fluctuates in the course of a week.
The weekly fluctuation in the CDP of the WPBR schemeshows that the behavior of MSs is more predictable in somedays of the week. For example the MS behavior on workdays
could be more predictable than weekends.
0.02

Call DroppingProbability

B. Experimental Results
Figure 7 compares the bandwidth utilization of the WPBR
strategy with the DPBR scheme for 28 days of simulation andaverage connection arrival rate 0.5. There is no backgroundinformation about the mobility patterns in the first day, hence
no prediction will made and bandwidth utilization values arehigh for both schemes. As time passed, mobility information is
gradually gathered. Prediction and the respective bandwidthreservation will cause bandwidth utilization value to decrease.
For the DPBR scheme, bandwidth utilization values decrease
to an extent and after that leveled off at about 0.4. The
subsequent negligible fluctuation in the bandwidth utilization
values of the DPBR scheme has arisen from alteration in the
behavior of MSs. For the WPBR scheme, bandwidth
utilization values decrease for an initial period (about a week)
and then increase with improvement in the prediction. The
accurate and sparing prediction caused the WPBR scheme togain higher bandwidth utilization.

86

DPBR
WPBR
0.015
TrendLine(WPBR)

0.01

0.005

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Day
0.9

Figure 9.Comparison of hand off call dropping probability by the two
schemes for average connection arrival rate 0.5

Bandwidth Utilization

0.8
0.7
0.6
0.5
0.4
0.3
0.2

DPBR

0.1

WPBR

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Day
Figure 7. Comparison of bandwidth utilization by the two schemes foraverage connection arrival rate 0.5

Call Blocking Probability

1

From the MS’s point of view, the objective is to minimize
CBP and specially CDP in order to improve the performanceof the network. Another objective, from the service provider’s
perspective is to decrease the cost by increasing BWU of the
network. In order to make a fair balance between both user
satisfaction and service provider satisfaction, one should
consider the cost/performance ratio (Z) which is defined in [9]as (14) where r is the penalty factor used to reflect the effect of
the hand off dropping over the new call blocking. A penalty of
5–20 times is commonly recommended [9].The design goals ofa call admission control scheme are increasing theperformance and decreasing the cost, which means minimizing
Z.

DPBR

0.9

WPBR

0.8

=



 



=

×e


.

(14)

0.7

Figure 10 depicts Z versus r on the day 28 of simulation. Itshows that the proposed WPBR scheme, for penalty of 5–20
times, has a lower Z value than DPBR scheme.

0.6
0.5
0.4
0.3
0.2
0.1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Day
Figure 8. Comparison of new call blocking probability by the two schemes
for average connection arrival rate 0.5

Figure 11 compares the required storage space of the WPBR
strategy with the DPBR scheme for 28 days of simulation. It
seemed that required storage space for the WPBR was 7 times
the required storage space for the DPBR. But simulation
results show that in actual traces, required storage space for the
WPBR is approximately 2.5 times the required storage space

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

for the DPBR. So, the adopted dynamic hashing approachmoderately obviates the overhead of storage space.
1.6
1.4
1.2

Z

1

ACKNOWLEDGMENT
This work was supported by Iran Telecommunication
Research Center (ITRC). The authors would like to thank theanonymous reviewers and the associate editor for theirvaluable comments and time spent in handling this paper. And
also we would like to express our gratitude to Xalid Barmasida
for his care in helping with English editing of the manuscript.

0.8

REFERENCES

0.6

[1]

0.4
DPBR
0.2

WPBR

[2]

0
5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

r

[3]

Figure 10. Z versus r on the day 28 of simulation[4]

300000DPBR with Hashing
250000Storage Space (Word)

87

[5]

WPBR with Dynamic Hashing

200000[6]150000[7]
100000

[8]

50000

[9]

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Day

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prediction strategy using markov modeling for wireless networks,”inProceedings of the World Congress on Engineering and Computer
Science (WCECS), San Francisco, USA, 2008.
M. S. Sricharan and V. Vaidehi, “A pragmatic analysis of user mobility
patterns in macrocellular wireless networks,”Pervasive and Mobile
Computing, vol. 4, pp. 616-632, 2008.
N. Eagle, A. Pentland, and D. Lazer,“Inferring Social Network
Structure using Mobile Phone Data,”in Proceedings of the National
Academy of Sciences (PNAS), Vol 106 (36), pp.15274-15278, 2009.
R. J. Enbody and H. C. Du, “Dynamic Hashing Schemes,”ACM
Computing Surveys (CSUR), vol. 20, pp. 850-113, 1988.
I. Candan, M. Salamah, "Analytical modeling of a time-threshold based
bandwidth allocation scheme for cellular networks,"Computer
Communications, vol. 30, pp.1036-1043, 2007.

Figure 11. Comparison of used storage space by the two schemes

V. CONCLUSIONS
A weekly predictive mobile-oriented bandwidth reservation
strategy has been proposed that improves prediction by weekly
storing movement probabilities based on Markov modelingtechniques. And also, a dynamic hashing approach has been
adopted to decrease the required storage space. Simulation
results show that the weekly prediction in this novel strategy
significantly improves bandwidth utilization, and the adopted
dynamic hashing approach moderately overcomes theoverhead of storage space.
Unfortunately, the cell residence time of mobile stations is
not included in the bandwidth reservation strategy. When amobile station moves into the coverage of new cell and there isan ongoing call, bandwidth will be reserved in the potential
next cell and the cell residence time of the mobile station has
not been taken into account. This topic is an interesting areafor further researches.

Adel Amani received his BS degree from Kordestan
University and Allame Rafiee University in 2008 in
Computer Engineering. He received his MS degreefrom Amirkabir University of Technology in 2012 inInformation Technology. His research interests include
computer networks, distributed systems, wirelesscellular networks, mobility prediction in mobile
networks, multimedia networks, and wireless sensor
networks.

Hossein Pedram received his BS degree from Sharif
University in 1977 and an MS degree from Ohio State
University in 1980, both in Electrical Engineering. He
received his PhD degree from Washington State
University in 1992 in Computer Engineering. He hasserved as a faculty member in the Computer
Engineering Department of Amirkabir University ofTechnology since 1992. He teaches courses in
Computer architecture and distributed systems. His
research interests include innovative methods in computer architecture suchas asynchronous circuits, management of computer networks, distributed
systems, and robotics.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 201288

Online Adaptive Fuzzy Logic Controller Using
Genetic Algorithm and Neural Network for
Networked Control Systems
Pooya Hajebi*, Seyed Mohammad Taghi AlModarresi**Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran
Hajebi@stu.yazd.ac.ir, smta@yazd.ac.ir

Abstract—Networked Control Systems are used for controlling
remote plants via shared data communication networks such as
Ethernet. These systems have found many applications inindustrial, medical and space sciences fields. However there aresome drawbacks in these systems, which make them challenging
to design. One of the most common problems in these systems isthe stochastic time delay. Packet switching in internet brings
about the randomly varying time delay and consequently makesthese systems instable. Convenient controllers such as PID and PI
type controllers which are just matched with a constant time delay
could not be a solution for these systems. Fuzzy logic controllers
due to their nonlinear characteristic which is compatible withthese systems are potentially a wise option for their control
purpose. Fuzzy logic controller could become adaptive by meansof neural networks and beneficial to deal with the varying timedelay problem. Further, they do have more capabilities to tackle
packet dropouts and dynamically system variables. This paper
introduces a novel control method which addresses the varying
time delay problem effectively. This novel method suggests an
online adaptive fuzzy logic controller which has been controlled
and adapted through the neural network. This method takes theadvantage of the genetic algorithm to optimize the membershipfunctions for its fuzzy logic controller. This designed controller isapplied to an AC 400 W servo motor as a remote plant in order tocontrol its position via Ethernet. The measurement of round-trip
time (RTT) is used to estimate the online time delay as aparameter in online adaptive fuzzy logic controller. The
rule-based table of designed fuzzy logic controller rotates inrelation to this estimated time delay. The value of rotating is
obtained from a trained neural network. Comparison ofsimulation results for different controllers indicates that this novel
designed controller provides a better performance over thevarying time delay. The proposed method follows the input easily,
despite classical methods which result in an unstable systemespecially over the large time delays as large as 600 ms. Results geteven more improved when genetic algorithm is applied to fuzzy
logic controller.
Index Terms—Data Communication Networks, Genetic
Algorithm, Networked Control Systems, Neural Networks, Online
Manuscript received June 29, 2012. This work was supported in part by the
ICT Research Institute (ITRC), Iran, under Grant T-500-12192.
P. Hajebi is with the Department of Electrical and Computer Engineering,
Yazd University, Yazd, Iran (phone: +98-351-8122377; fax:
+98-351-8200144; e-mail: Hajebi@stu.yazd.ac.ir).
S.M.T. AlModarresi is with the Department of Electrical and Computer
Engineering, Yazd University, Yazd, Iran (e-mail: smta@yazd.ac.ir).

Adaptive Optimized Fuzzy Logic Controller, Rules-Table
Rotation.

I. INTRODUCTIONNETWORKED control systems (NCSs) are spatiallydistributed systems in which the communication between
sensors, actuators and controllers occurs through a sharedband-limited digital communication network [1], [2]. This
multipurpose shared network connecting, spatially distributed
elements, creates a flexible architecture which generally
reduces installation and maintenance costs. NCSs have beenfinding application in a broad range of areas such as mobile
sensor networks [3], remote surgery [4], haptics collaboration
over the Internet [5]–[7], and automated highway systems andunmanned aerial vehicles [8], [9]. Murry et al. in [10] haveidentified control over networks as one of the key future
directions for control. However, application of a shared
network versus several dedicated independent connections,introduces new challenges. Drops and variable delays in NCSs
are two major problematic issues that were addressed in [11],[12]. Packet dropouts and finite level quantization make NCSs
unstable [12]. When the delay time is less than the samplingtime of NCSs, results show that the time delay has insignificant
effect on control system. However, delay time greater than thesampling time degrade the performance of the NCSs [13].
Many controllers such as conventional PID and fuzzy logic
controllers are utilized to stabilize the NCS closed loop
feedback and to reduce the error. Classical Smith predictor is
one of the controllers which are efficient for time delay
processes [13], [14]. Lai and Hsu proposed an adaptive Smith
predictor as a controller for NCS in [14]. Despite showing
relatively a good performance, there are some drawbacks inthese controllers. For instance, the accuracy of the model
depends on plant transfer function estimation. Moreover, eachnew plant requires changing the controller design. Practicallyestimation of plant transfer function is not exact. Recently Pan
et al. in [15] and Zhao et al. in [16] have shown that fuzzy logic
controllers offer a better performance in tackling packet
dropouts and varying time delay, at the same time are morecompatible with nonlinear processes. W. Du and F. Du
proposed a Smith predictor integrated with fuzzy adaptive PID

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Actuator

Plant

SensorDelay t1

Data Network

Delay t2

89

e−t1s

G p (s )e −t2 sFig. 2. Fuzzy logic controller.
Controller

Fig. 1. A SISO networked control system structure.

controller for the NCSs in [17]. However they did not measure
the network delay online. They applied fuzzy logic controller
for tuning the coefficients of PID controller. This paper first,
suggests a fuzzy logic controller (without PID controller) tocontrol the position of an AC 400 W servo motor via Ethernet.
At the next step, it proposes a novel control method which is an
online adaptive fuzzy logic controller for the similar
application. This research provides the advantages of no PID
controllers application while offers an adaptive controller
which its fuzzy logic rules are rotating during the plant control.
The round-trip time (RTT) is measured online and this value isutilized as, tm, time delay parameter. Then, this time delay value
is mapped to an angle by means of trained neural network. This
neural network has been already trained by different timedelays in adaptive fuzzy logic controller. Results verify thebetter performance of this novel design which its fuzzy logic
controller rules-table rotates through a trained neural network.
The fact is that in communication networks time delay could
exceed 200 ms (vs. 400 ms, 600 ms). However, results from[17], [18] show that the response would be degraded in thesesystems for time delays over 200 ms despite the application ofdesigned offline controllers. This paper’s proposed method has
shown an improved response especially in the case of timedelays over 200 ms. Even with time delay of 600 ms, there is nodegradation in step response. Genetic algorithm could be
implied in order to optimize a suitable objective function fortuning the fuzzy logic controller membership functions. The
results indicate that this optimized controller shows better
performance in comparison with a non-optimized fuzzy logic
controller.
This paper includes the following sections; In Section II,
NCSs, stochastic time delay and packet dropouts are
introduced. Section III, first describes, designing a fuzzy logic
controller in order to control the position of an AC 400 W servo
motor and next introduces a novel adaptive fuzzy logic
controller with a rotating rules-table by means of trained neuralnetwork. Section IV introduces the genetic algorithm anddescribes its application in optimization of membershipfunctions of the designed fuzzy controller. Section V contains
the related simulations and equations. This paper ends withconclusion in section VI.
II. NETWORKED CONTROL SYSTEMS
Due to quantum leaps in communication systems, in recent
years, it has become more common to apply a sharedcommunication channel such as Ethernet or Controller Area
Network (CAN) bus etc. for transmission of the control signal
and the measured output. This method helps reducing thewiring costs as well as eliminates the necessity for maintaining
dedicated communication channels for each control parameter[15]. However, this type of networked control system is not a
perfect solution and own its various unsolved issues such astransmission delays and packet dropouts [12], [15] which candegrade control performance. The SISO (single input-single
output) NCS structure in the closed loop model is shown in Fig.
1. As illustrated in this figure, t1 and t2 indicate, time-delays
induced in the network structure for the controller-to-actuator
direction and the sensor-to-controller direction, respectively.
Basically, the induced network delay varies according to the
network load, scheduling policies, number of nodes, anddifferent protocols. Time-varying characteristic of these NCSs
makes the design and modeling of them more complicated. The
total time-delay can be categorized into three classes, based on
the parts where they occur, namely, the server node, thenetwork channel, and the client node [19]. In addition, theround-trip time (RTT) measurement is crucial as it provides ofaccurate delay measurements periodically [19]–[21]. RTT is
defined as the total time delay in SISO NCSs. Obviously thelonger distances increase, the time delay of a network sincemore nodes are involved and consequently results in a larger
RTT. In a classical Smith predictor design, the value of tm isconstant and usually equals to average approximation of timedelay between two nodes in the network. The value of RTT
could be applied to fuzzy logic controller for compensating ofvariable delay. Normally in a fuzzy logic controller, rule-based
table is constant during the control process action. In thispaper’s suggested method, RTT applied by neural network
mapping, generates a rotating rules-table.
III. FUZZY LOGIC CONTROLLER USING RULES-TABLE
ROTATION
As it has been already mentioned, an online adaptive fuzzy
logic controller could be a solution for tackling the stochastic
time delay problem in NCSs. However it controls with simple
PID or PI controllers which shows limited potentials, especiallyin nonlinearity processes. Recently it is proved that fuzzy logic
controller is the best option for controlling nonlinear processes
while make the system more robust against the varying timedelay [15], [16]. In the following parts, first a fuzzy logic
controller is designed then a classical Smith predictor would be
integrated with this designed fuzzy logic controller based on
our plant. Finally a novel rotating rules-table online adaptive
fuzzy logic controller is described.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

A. Designing Fuzzy Logic Controller
To implement a NCS controller, first the output of plant ismeasured and then it would be compared with a reference
signal. This comparison generates the error signal. The error
signal and its derivative are both inputs for the fuzzy logic
controller shown in Fig. 2. Here in this paper, the plant is an AC
400 W servo motor which its position as an output is measured
with an encoder with gain 104 P/R. The coefficients of theequivalent PI controller for this plant are Kp=0.0001 and Ki=
0.00000001, [14]. The open loop position control is obtained
from (1). Equations (2) and (3) represent the continuous-state
space form of transfer function described in (1). In Fig. 3, seventriangular membership functions have been devoted to either,
input (error and derivative of error) and output. In Fig. 3, thefuzzy linguistic variables (“NB”, “NM”, “NS”, “ZE”, “PS”,“PM”, “PB”) represent (Negative Big, Negative Medium,
Negative Small, Zero, Positive Small, Positive Medium andPositive Big) respectively. In position control, the output
follows the input. Therefore, at first they are assumed to have
similar membership functions. However in the following
section, output memberships would be optimized using an
objective function. Here are provided some design
specifications, applied in this fuzzy logic controller: 1) The
inference, used in this design is Mamdani-type [22], 2) Fuzzy
logic “and operator” was implemented by “min” method while
the fuzzy logic implication is based on the “min” method aswell and rules are aggregated using fuzzy “max” operator, 3)
The fuzzy logic output has been determined through the centerof gravity method by means of defuzzification, 4) Fuzzy rules
are opted based on Table I which contains 49 rules, 5) Due toG P (s) =10 4 (0.058 s + 3.221 )(1)s (0.0001 s 2 + 0.019 s + 1)⎡− 190 − 78.125 0⎤⎡2048⎤⎢⎥
00⎥ x + ⎢⎢ 0 ⎥⎥Ux = ⎢ 128⎢⎣ 0⎢⎣ 0 ⎥⎦
10⎥⎦y = [0 22.13 1228 .7] x

(2)
(3)

Membership functions for Input
1

NB

NM

NS

ZE

PS

PM

-2

-1

0

1

2

PB

0.5

0
-5

-4

-3

3

4

5

Membership functions for Output
1

NB

NM

NS

ZE

PS

PM

PB

0.5

0
-5

-4

-3

-2

-1

0

1

2

3

Fig. 3. Membership functions for input and output.

4

5

90

GSmith (s )G p (s )e −t1sGC

Gˆ p ( s )(1 − e −tm s )e −t2 sFig. 4. A control structure of Smith predictor.

high gain of encoder the scaling factor value selected for fuzzy
logic controller output is ,10-4.
B. Classical Smith Predictor with Fuzzy Logic Controller
Classical Smith predictor is one of the controllers which are
efficient for time delay process [13], [14]. Here a classicalSmith predictor is designed for comparing the results. In thisclassical Smith predictor which is shown in Fig. 4, GC is thedesigned fuzzy logic controller described in section III. A., GP
is the transfer function of the plant while ĜP is the estimationof plant transfer function. Usually tm is the approximation of
total time delay from controller to plant and plant to controller.
If tm is the appropriate estimation of overall time delay theperformance of system will be reasonable. tm is assumed 200
ms in the simulation. ĜP , is the estimation of GP, andpractically difference between these two transfer functions
results in instabilities and increases of the error of response.
This is the main problem for classical Smith predictor andonline adaptive Smith predictor. In this paper classical Smith
predictor with fuzzy logic controller was assumed ideal, thus
the GP and ĜP are equal in the simulations. The fuzzy rules are
selected based on Table I.
C. Designing Rules-Table Rotation of Online Adaptive
Fuzzy Logic Controller Using Neural Network
RTT is estimated in network [19], [20] and then thismeasurement would be applied to online fuzzy logic controller.
In this stage the designed fuzzy logic controller in part A would
be integrated with an online neural network. The measurementof round-trip time (RTT) is applied for estimating of online
time delay which in turn provides the value for rotation angle offuzzy rules-table. As already mentioned, the controller in thispaper has not included any PID or PI method.
TABLE IRULE BASE FOR ERROR, ERROR DERIVATIVE AND FLC OUTPUT(WITHOUT ROTATION).

e
de
NB
NM
NS
ZE
PS
PM
PB

NB

NM

NS

ZE

PS

PM

PB

NB
NB
NB
NB
NM
NS
ZE

NB
NB
NB
NM
NS
ZE
PS

NB
NB
NM
NS
ZE
PS
PM

NB
NM
NS
ZE
PS
PM
PB

NM
NS
ZE
PS
PM
PB
PB

NS
ZE
PS
PM
PB
PB
PB

ZE
PS
PM
PB
PB
PB
PB

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

e

NB

NM

NS

ZE

PS

PM

PB

PB

ZE

PS

PS

PM

PM

PB

PB

PM

NS

ZE

PS

PS

PM

PM

PB

PS

NS

NS

ZE

PS

PS

PM

PM

de

ZE

NM

NS

NS

ZE

PS

PS

PM

NS

NM

NM

NS

NS

ZE

PS

PS

NM

NB

NM

NM

NS

NS

ZE

PS

NB

NB

NB

NM

NM

NS

NS

ZE

e

NM

NS

ZE

PS

PM

PB

G p (s )

PB

ZE

PS

PS

PM

PM

PB

PB

PM

NS

ZE

PS

PS

PM

PM

PB

PS

NS

NS

ZE

PS

PS

PM

PM

ZE

NM

NS

NS

ZE

PS

PS

PM

NS

NM

NM

NS

NS

ZE

PS

PS

NM

NB

NM

NM

NS

NS

ZE

PS

NB

NB

NB

NM

NM

NS

NS

ZE

Φ

e −t2 sFig. 5. Structure of online adaptive fuzzy logic controller using neural network.

The nonlinear fuzzy logic controller does have the potential
to control the complicated and nonlinear processes while ismore robust against the dynamically system variables speciallyoccurs at the beginning of the process. In a control process withno delay, the error and the derivative of the error change
periodically. However, these changes suggests nonlinear
function pattern. During the control process and based on thetaken time, the error and derivative of the error move on the
fuzzy rules-table in a circular path. While delays caused theerror and derivative of error do not have desired time values,
application of this suggested rotation method could overcome
problem of delays. Thus, this paper has suggested a control
method which integrated fuzzy logic controller with a neuralnetwork. Fig. 5, shows the structure of this proposed controller.
Here in this figure, the value of RTT is mapped to an angle byneural network. The structure of neural network has two-layer
feedforward. First this neural network is trained by several set
point time delays. It means the value of rotation for several timedelays is obtained manually then these values will be applied
for training the neural network.
The value of angle for rotating rules-table in online adaptive
fuzzy logic controller, changes periodically based on the RTT
value. Fuzzy rules are opted based on Table II, but otherparameters (membership functions, fuzzy logic operators andfuzzy logic method) are similar to data in section III. A.
Equation (4) shows the mapping relation of the error andvariation of the error in new coordinate. Matrix A, in (5) is therotation transform matrix which rotates coordinates by the angle
TABLE II
RULE BASE FOR ERROR, ERROR DERIVATIVE AND FLC OUTPUT(ROTATION TABLE).

e
de
PB
PM
PS
ZE
NS
NM
NB

NB

de
e −t1s

91

NB

NM

NS

ZE

PS

PM

PB

ZE
NS
NS
NM
NM
NB
NB

PS
ZE
NS
NS
NM
NM
NB

PS
PS
ZE
NS
NS
NM
NM

PM
PS
PS
ZE
NS
NS
NM

PM
PM
PS
PS
ZE
NS
NS

PB
PM
PM
PS
PS
ZE
NS

PB
PB
PM
PM
PS
PS
ZE

Φ

Fig. 6. Rotating of fuzzy rules-table.

of φ radian. The rules-table rotation structure of fuzzy logic
controller and trend of rotation are shown in Fig. 6.
⎡e new ⎤⎡e ⎤(4)⎢e ⎥ = A⎢ ⎥⎣e ⎦⎣ new ⎦
⎡cos φA=⎢⎣ sin φ− sin φ ⎤⎥cos φ ⎦

(5)

IV. OPTIMIZATION OF FUZZY LOGIC CONTROLLER USINGGENETIC ALGORITHM
To improve the proposed fuzzy logic controller, genetic
algorithm is used to find the optimal membership functions[15]. Here, firstly the genetic algorithm is explained, and thenthe optimization of fuzzy logic controller by means of genetic
algorithm is discussed.
A. Genetic Algorithm
Genetic algorithm was firstly introduced by John Hollandand developed by him, his student and colleagues [23]. Thesealgorithms are heuristic optimization process inspired bynatural evolution and could be used to minimize a suitableobjective function or fitness function for tuning the fuzzy logic
controller parameters. It is more effective at avoiding local
minima than differentiation based methods.
The genetic algorithm will generally include threefundamental genetic operations of selection, crossover andmutation. These operations are used to modify the chosen
solutions and select the most appropriate offspring tosucceeding generations [23].
In a genetic algorithm, a population of strings (called
chromosomes), which encode candidate solutions (called
individuals) to an optimization problem, evolves toward better
solutions. Traditionally, solutions represented in binary as
strings of “0”s and “1”s. The evolution usually starts from a
population of randomly generated individuals and happens ingenerations. In each generation, the fitness of every individualin the population is evaluated, multiple individuals arestochastically selected from current population (based on their
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

fitness), and modified (recombined and possibly randomly
mutated) to form a new population. The new population is thenused in the next iteration of the algorithm. Commonly thealgorithm terminates when either a maximum number ofgenerations has been produced, or satisfactory fitness level has
been reached for the population.
The basic iterations of genetic algorithm can be summarized
as follows [23]–[25]:1) Genetic representation: encoding the variables. Genetic
algorithm often encodes solutions as fixed length “bitstrings”(e.g. 101110, 111111, and 000101).
2) A method for generating the initial population: population
may be generated randomly or problem specific knowledge canbe used to construct the chromosomes with the populations.
3) An evaluation function, which assign a real number tomeasure the fitness of each chromosome.
4) A reproduction selection scheme, which is used to selectchromosome to be exposed to genetic operations. One of themost famous approaches is “roulette wheel”, which selects
chromosomes proportional to their fitness values.
5) Genetic operators: crossover and mutation are two main
operators in genetic algorithm. These operators are applied tomodify the chosen chromosomes (parents) and select the mostappropriate offspring to pass on to succeed generations. The
basic mechanism of genetic algorithm, crossover of the parental
and mutation illustrated in Fig. 7. Crossover is done byselecting two parents during reproduction and combining genes
to produce offspring. Two selected parents are combined withsome probability (crossover rate); therefore two new offspring
will be born. One gene or several genes of each offspring may
then change randomly (mutation) with some probability(mutation rate). Usually the crossover has high probability(typically values are between 0.8 and 0.95) and mutation has
small probability (typically values are between 0.1 and 0.001).
6) Termination: The cycle of genetic algorithm will be
continued until the genetic algorithm reaches to stopping
criteria. There are several approaches to terminate the genetic
algorithm. A common approach is to terminate genetic
algorithm when the number of generations reaches to specificvalue. The genetic algorithm process may also run just forlimited time duration. It is also possible to terminate a genetic
Evaluated
Offspring

Chromosome

StringFitness

A

10010011760

B

0111011456

Selection

C

1000010340(Roulette Wheel)D

0011000

220

E

001101025

Fitness
Functionalgorithm when the objective function of the best chromosome
has not improved in the several generations.
B. Using Genetic Algorithm for Optimizing of Fuzzy Logic
Controller Membership functions
In section III. A., input and output memberships of fuzzy
logic controller are assumed similar. Now, with taking toconsider the values from the last input membership functions,the output membership functions could be optimized. The
shapes of membership functions in fuzzy logic controller are
triangular. Therefore in the case of triangular fuzzy sets, threecharacteristic points (center and two widths) are used as theparameters should be optimized. Here, the number of triangularmembership functions is seven (“NB”, “NM”, “NS”, “ZE”,“PS”, “PM”, “PB”). To design symmetric controller for
positive and negative input pulses, the membership functions
are assumed symmetric to Y axis. Also the center of ZE isassumed zero. Therefore there are nine points or variables for
optimizing the output membership functions; two points for“NB”, three points for “NM”, three points for “NS” and one
point for “ZE”. The range of changes in these variables is
between 0.1 and 4. Moreover, there are constraints in
optimization which guarantee that the membership functions
areorderedaccordingtotheirvalues(e.g.
NB<NM<NS<ZE<PS<PM<PB); for instance, the center of“NB” must be less than the center of “NM”. These constraints
are considered to optimize the membership functions. The
number of initial population is assumed 100. Minimizing
integral of time-weighted absolute error (ITAE) is commonlyreferred to as good performance index in designing PID
controllers. Thus the ITAE assumed as objective function.
Equation (6) shows the mathematical formula of ITAE. Where t
is the time and e is the different between output and reference incontrol process.
ITAE =∞∫0 t e(t ) dt

(6)

Selection of parents is based on “roulette wheel”. The rate ofcrossover which applied to parents is 0.8. Crossover trends tomake the chromosomes within the population more similar,
whereas mutation trends them more divers and usually has low
rate. Here, because of existing constraints for membershipfunctions, the mutation is not applied. Also here the genetic
algorithm is terminated when 32 generations have been
produced.
V. SIMULATION RESULTS

Offspring 1

Crossover

Parent 1

0 0 1 1 0 1 1

0 1 1 1 0 1 1

0 1 1 1 0 0 0

0 0 1 1 0 0 0

Offspring 2

AlteredOffspring92

Mutated Offspring0 1 1 1 0 0 1

Parent 2

Mutation

Offspring0 1 1 1 0 0 0

Fig. 7. Mechanism of genetic algorithm.

A closed loop NCS unit in this paper includes these sections:
online adaptive fuzzy logic controller, neural network, plant,
data communication network. In order to analyse the whole unit
each section should be analysed separately. This paper hasapplied the state equations to plot the step response of this NCS.
At this first stage, transfer functions of plant and controller are
converted to state equations. Since the data transmitted over the
network is digital these equation states need to be discretizedstate space equation to be able to simulate the processes.
Equation (7) shows the discrete state-space form of process.
While A, B, C and D are the continuous state space matrices

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012(10)

Dd = D

(11)

-0.5
-1
-3

-2

Total Time Delay
700

600

500

400

300

200

100

0

5

10

15
Time (Second)20

25

Fig. 8. A sample of simulated varying time delay in NCS.

30

-1

0
1
2
n
(b) Neural Network Transfer Function for Layer 2

3

4

(9)

⎡0.0066 - 0.2973 0⎤⎡7.7924⎤⎢⎥(12)x[k + 1] = ⎢0.4870 0.7295 0⎥ x[k ] + ⎢⎢7.0909⎥⎥U [k ]⎢⎣0.0035 0.0089 1⎥⎦⎢⎣0.0277⎥⎦
y[k ] = [0 22 .13 1228 .7] x[k ](13)
In these simulations a random time delay is provided toanalyse the performance of NCS. The total of command delay,
t1, and feedback delay, t2, generates the total time delay (RTT)
which is shown in Fig. 8. After training of neural network, thebiases and weights values are obtained shown in Table III.
Neural network transfer functions for layer 1 (hidden layer) andlayer 2 (output layer) are “tansig” and “purelin” respectively.
Equations (14) and (15) show these two transfer functions inmathematical forms, while figures of these transfer functions
are plotted in Fig. 9.
2tansig (n) =(14)−1
1 + e − 2npurelin (n) = n(15)
The sampling time is assumed 0.01 second and the model of
NCS is based on the model described in [26]. Here in this paper
the simulations and comparisons of the step response areprovided among three controller types: 1) Online adaptive
fuzzy logic controller, 2) Classical Smith predictor with fuzzy
logic controller and 3) Pure fuzzy logic controller. The results
are illustrated in Fig. 12. Results show that the online adaptive
fuzzy logic controller offer a better performance compared toother two controllers.

Time Delay (ms)

0

(8)

T
Bd = ⎛⎜ ∫ e Aτ dτ ⎞⎟ B
⎝ 0⎠
Cd = C00.5

2
purelin (n)Ad = eAT(a) Neural Network Transfer Function for Layer 1
1tansig (n)then their equal discrete state space matrices (Ad, Bd, Cd, Dd)
would be obtained from (8), (9), (10), (11). By substituting the(2), (3) in to (7)-(11), discrete state space form of plant isobtained and represented in (12), (13).
⎧ x[k + 1] = Ad x[k ] + Bd u[k ](7)⎨⎩ y[k ] = Cd x[ k ] + Dd u[ k ]

93

0
-2
-4
-3

-2

-1

0
n

1

2

3

Fig. 9. Neural networks transfer functions; a) tansig; b) purelin.

As can be seen the output signal of online adaptive fuzzy
logic method does have small overshoot and fast response.
Therefore this controller is recommended for networked
control systems purposes. W. Du and F. Du have suggested aSmith predictor integrated with adaptive fuzzy-PID controller
for the NCSs in [17]. However they did not measure the
network delay online. They applied a fuzzy logic controller justfor tuning the coefficients of PID controller, which means that
their suggested controller works offline. They also havedesigned in [18] a RBF neural network control with Smith
predictor for NCSs which works offline as well. In
communication networks time delay could exceeds 200 ms (vs.
400 ms, 600 ms). The results from [17], [18] show that response
would degrade with time delays over 200 ms. In [17], [18], it
was assumed that the maximum of burst time delay is about 200
ms while this time delay was applied discretely. Our proposed
method has more improved response especially when the timedelay is over 200 ms even with the time delay of 600 ms there isno degradation in step response. In the spite of applying thislarge value of time delay continuously, results in Fig. 13 showthat this does not have any more effects on the step response as
well.
As described in section IV, This paper takes the advantage ofgenetic algorithm method to optimize the fuzzy controller
membership functions and consequently improve the overall
results. For this propose, it starts with assumption of a no delaysystem while optimizes the fuzzy logic controller. The applied
objective function type in this paper is ITAE. In this case,which is a position control example, the goal is that the output
follows the input more closely. The closer output follows the
input the more accurate is the performance. Tuning of an online
adaptive fuzzy logic controller reduces the error value evenwith the presence of time delay in the system. Hence genetic
algorithm is applied for some input pulses, and as a result, theoutput membership functions become tuned. The values ofobjective functions in each generation are shown in Fig. 10. As
could be perceived, the objective function values in eachgeneration become smaller compared to their previous
generation which is inherently a genetic algorithm
characteristic. Another point is that objective functions do not

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

show significant changes in last four generations, whichindicates that, this genetic algorithm roughly obtains its bestanswer. The best value of ITAE in 32th generation is 0.05292.
After applying the genetic algorithm to fuzzy logic controller,
tuned output membership function shapes could be illustrated
in Fig. 11. These membership functions are used for last three
types of controller. For online adaptive fuzzy logic controller,
the neural network is trained based on new optimized
membership functions. Obtained biases and weights values areshown in Table IV. Rotation values are obtained according todifferent delays in experiments. Then the neural network is
trained by these values. Experiments in Fig. 12 and Fig. 13 arerepeated and are shown in Fig. 14 and Fig. 15. Results of online
adaptive fuzzy logic controller are better compared to the othertwo controllers. Since the pure fuzzy logic controller is tuned in
conditions with no delay, changes in membership make it
unstable with the presence of time delay. Moreover, classicalSmith predictor as shown in Fig .15 becomes unstable for delay
times over 200 ms similar to pure fuzzy logic controller. It iseven worse when the time it is not tuned. Table V shows thecomparison of results. Two main evaluation indexes forcomparison of results are the rise time (Tr) and the Percent
Overshoot (P.O.). Rise time is that time taken for the output ofplant to rise from 10% to 90% of its final value when simulated
by step input. The Percent Overshoot is defined as (16). For aunit step input, where MPt, is the first peak value of the time
response, and fv is the final value of the response. Normally, fv
is the magnitude of the input.
M − fv
× 100 %
P.O. = Pt(16)
fv
Peak time (Tp) is the time that takes for output of plant toreaches the peak value. The smaller the P.O. and MPt values, thebetter is the controller performance. The results from Table V
show that our proposed method has no overshoot. Furthermore,when the membership functions are optimized with genetic
algorithm, the Tr is more smaller compared to the nonoptimized case. Since the Smith predictor controller is tuned for
200 ms delay, when optimization is applied, this controller
shows better results with the time delay of 200 ms. However,for other induced delays, its performance will decrease. Purefuzzy logic controller tuned with no time delay and shows poor
performance result compared to other two controllers.

94

Membership functions for Input
NB

1

NM

NS

ZE

PS

PM

-2

-1

0

1

2

PB

0.5

0
-5

-4

-3

3

4

5

Membership functions for Output
NBNM NS

1ZE

PS PMPB

0.5

0
-5

-4

-3

-2

-1

0

1

2

3

4

5

Fig. 11. Optimal fuzzy logic membership functions.

VI. CONCLUSION
NCSs have found widely application in various fields
recently. However there are some drawbacks in their structures
such as varying time delay and packet dropouts, which makes
the control design of these systems challenging. Conventional
PID and fuzzy logic controllers are mostly designed to address
the instability problems in NCSs. Fuzzy logic controllers withthe great potential in tackling the nonlinear processes andmaking NCSs more robust against the dynamically variable
parameters could be a more reasonable option for NCSs.
Moreover, a rotating rules-table fuzzy logic controller is a greatsolution for stochastic time delay. Thus according to above
mentioned characteristic of both fuzzy logic controller andrules-table rotation, this paper has proposed a novel controller
for NCSs. This novel design has integrated a rotating
rules-table fuzzy logic controller with a neural network tocontrol the position of an AC 400 W servo motor as a remoteplant via Ethernet. Simulation results and their comparison forthree different methods of controlling over this plant verified
that this novel controller design is more beneficial especiallyover the big value of delay times as large as 600 ms. The
proposed method shows better performance when the fuzzy
logic membership functions are optimized by means of genetic
algorithm.

Best: 0.052915
0.2
0.18

Fitness value

0.16
0.14

TABLE IIIWEIGHTS AND BIASES VALUES FOR NEURAL NETWORK.

0.12
0.1
0.08

⎡− 0.0057 ⎤IW1,1 = ⎢⎥⎣− 0.2547 ⎦LW2,1 = [0.3566 0.8011]⎡ 1.1243 ⎤b1 = ⎢⎥⎣24.6125⎦b 2 = 0.5684

0.06
0.04

0

5

10

15
Generation

20

25

Fig. 10. Objective functions values for different generations of genetic
algorithm.

30

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

TABLE IV
WEIGHTS AND BIASES VALUES FOR NEURAL NETWORK.
(FOR OPTIMIZED FUZZY LOGIC CONTROLLER)⎡0.0036⎤
IW1,1 = ⎢⎥⎣0.0154⎦

LW2,1 = [− 2.9954 − 15.2386]⎡ 1.286 ⎤b1 = ⎢⎥⎣1.4904⎦b 2 = 17.5069

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quantization,” Syst. & Control Lett., vol. 60, no. 5, pp. 325–332, May
2011.
[13] C. W. Cheng, C. L. Lai, B. C. Wang, and P. L. Hsu, “The time-delay
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1593–1602.

Pooya Hajebi (S’10) was born in Isfahan, Iran, in
1981. He received the B.S. degree in electricalengineering (Electronics) and the M.S. degree inelectrical engineering (Communication Systems),
both from Yazd University, Yazd, Iran, in 2005 and2009, respectively. Currently, He is working toward
the Ph.D. degree in the Department of Electrical andComputer Engineering, Yazd University, Yazd, Iran.
His research interests include Networked Control
Systems, Fuzzy Systems, Neural Networks, Data Communication Networks,
Digital Signal Processing, Biological Signal Processing, Digital Image
Processing, Cellular Networks, Optimizations, Time Delay Systems and RealTime Systems.

Seyed Mohammad Taghi AlModarresi obtained
his B.S. degree in Electronics Engineering and M.S.
degree in Communication Systems, both from the
Isfahan University of Technology, Isfahan, Iran. He
also holds Ph.D. in Electronics (Intelligent Signal
Processing) from University of Southampton, UK(Department of Electrical and Computer Science:
ECS). He works at the Department of Electrical andComputer Engineering in Yazd University where hepursues his research interests in: (i) Networked Control Systems (NCS) (ii)
Neuro-Fuzzy Networks (iii) Wireless Networks.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 201296(a) Total Time Delay
500

0

0

5

10

15
(b) Input (Reference)

0

5

0

5

0

5

10

15
(e) Fuzzy Logic Controller

0

5

10

15
Time (Second)

20

25

30

10
15
20
(c) Online Adaptive Fuzzy Logic Controller Using Rules-Tabale Rotation Method

25

30

10
15
20
(d) Classical Smith Predictor with Fuzzy Logic Controller , tm= 0.2 Second

25

30

20

25

30

20

25

30

4
2
0

4
2
0

4
2
0

5

0

Fig. 12. Simulation results (The maximum time delay is about 400 ms); a) Time delay; b) Reference signal; c) Position (revolution of the motor shaft) for online
adaptive fuzzy logic controller using rules-table rotation; d) Position (revolution of the motor shaft) for classical Smith predictor with fuzzy logic controller; e)Position (revolution of the motor shaft) for fuzzy logic controller.

(a) Total Time Delay
600
400
200
0

0

5

10

15

20

25

30

20

25

30

25

30

25

30

(b) Input (Reference)
4

2

0

0

5

10

15

(c) Online Adaptive Fuzzy Logic Controller Using Rules-Tabale Rotation Method
4

2

0

0

5

10

15

20

(d) Classical Smith Predictor with Fuzzy Logic Controller , tm= 0.2 Second
4

2

0

0

5

10

15
Time (Second)20

Fig. 13. Simulation results (The maximum time delay is about 600 ms); a) Time delay; b) Reference signal; c) Position (revolution of the motor shaft) for online
adaptive fuzzy logic controller using rules-table rotation; d) Position (revolution of the motor shaft) for classical Smith predictor with fuzzy logic controller.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 201297(a) Total Time Delay
500

0

0

5

10

15
(b) Input (Reference)

0

5

0

5

0

5

10

15
(e) Fuzzy Logic Controller

0

5

10

15
Time (Second)

20

25

30

10
15
20
(c) Online Adaptive Fuzzy Logic Controller Using Rules-Tabale Rotation Method

25

30

10
15
20
(d) Classical Smith Predictor with Fuzzy Logic Controller , tm= 0.2 Second

25

30

20

25

30

20

25

30

4
2
0

4
2
0

5

0

10
0
-10

Fig. 14. Simulation results using genetic algorithm (The maximum time delay is about 400 ms); a) Time delay; b) Reference signal; c) Position (revolution of the
motor shaft) for online adaptive optimized fuzzy logic controller using rules-table rotation; d) Position (revolution of the motor shaft) for Classical Smith predictor
with optimized fuzzy logic controller; e) Position (revolution of the motor shaft) for optimized fuzzy logic controller.

(a) Total Time Delay
500

0

0

5

10

15
(b) Input (Reference)

0

5

0

5

0

5

10

15
(e) Fuzzy Logic Controller

0

5

10

15
Time (Second)

20

25

30

10
15
20
(c) Online Adaptive Fuzzy Logic Controller Using Rules-Tabale Rotation Method

25

30

10
15
20
(d) Classical Smith Predictor with Fuzzy Logic Controller , tm= 0.2 Second

25

30

20

25

30

20

25

30

4
2
0

4
2
0

10
0
-10

20
0
-20

Fig. 15. Simulation results using genetic algorithm (The maximum time delay is about 600 ms); a) Time delay; b) Reference signal; c) Position (revolution of the
motor shaft) for online adaptive optimized fuzzy logic controller using rules-table rotation; d) Position (revolution of the motor shaft) for classical Smith predictor
with optimized fuzzy logic controller. e) Position (revolution of the motor shaft) for optimized fuzzy logic controller.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 201298

TABLE VCOMPARISON OF RESULTS

Approximate
value of delay

20 ms

MethodOptimization

Tr (s)

P.O. (%)

ITAE

Tp (s)

OnlineAdaptive
FLC
Classical
Smith
Predictor

Optimized

0.199000.0429-Non-Optimized

0.828900.4959-Optimized

0.686300.2858-

Non-Optimized

1.086900.7152-

Optimized
Non-Optimized
Optimized

0.20750.58990.50620000.04360.23550.3034-

Non-Optimized

0.787600.5937-

Optimized

0.21952.04

0.09720.5600Non-Optimized

0.602600.3355-

Optimized
Non-Optimized
Optimized

0.24390.2432
1.0146

24.45
21.77
0

4.78790.3322
1.28630.54000.6300-Non-Optimized

1.239401.6309-Optimized

0.1388144.383.81890.8600Non-Optimized

0.353826.91

1.0856

1

Optimized
Non-Optimized
Optimized

0.1506
0.29411.2878202.6652.25
010.60896.44262.29860.84000.9100-Non-Optimized

1.520202.6264-

Optimized

0.3932

36.03

11.26481.1600Non-Optimized

0.2371144.275.03261.3000Optimized
Non-Optimized

0.1379-343.67-

16.8155-

1.1400-FLC

200 msOnlineAdaptive
FLC
Classical
Smith
Predictor
FLC

400 msOnlineAdaptive
FLC
Classical
Smith
Predictor
FLC

600 msOnlineAdaptive
FLC
Classical
Smith
Predictor
FLC

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 201299

Improving Coverage and Capacity in
Underwater Acoustic Cellular Networks
Amirmansour Nabavinejad *, Samar Shahabi Ghahfarokhi *** Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran** Faculty of Electrical Engineering and Information Technology, Ilmenau University of Technology, Ilmenau,
Germany
dc.nabavinejad@gmail.com , samar.shahabi-ghahfarokhi@tu-ilmenau.de

Abstract— Underwater acoustic communications systems arechallenged by the characteristics of acoustic propagation throughthe underwater environment. There are a wide range of physicalprocesses that impact underwater acoustic communications andthe relative importance of these processes are different indifferent environments. Acoustic propagation is characterized bythree major factors: time-varying multipath propagation, low
speed of sound and attenuation that increases with signal
frequency. Limited bandwidth in these systems is of paramountobstacles. To overcome this problem the idea of frequency reuse
pattern seems to be useful. The key characteristic of a cellular
network is the ability to re-use frequencies to increase bothcoverage and capacity. One element that determines frequency
reuse is the reuse distance depending on the cell radius and thenumber of cells per cluster. Analysis of frequency reuse between
adjacent clusters and optimal cell-radius selection criteria has
been carried out recently. In other recent woks, the parameters of
the cellular networks designing have been calculated based on arough approximation of the attenuation and propagation model.
In our work, after driving the ratio of signal to interference forunderwater acoustic channels with more accuracy, the constraints
for the cell radius are determined. One of the most importantresults of this contribution is that, for special parameters likebandwidth, it may be impossible to provide the required signal tointerference ratio and bandwidth for the network users.
Furthermore, in this paper, the number of supportable users,
per-user bandwidth, and the user capacity for a cellular
underwater network are determined.
Index Terms— Cell Radius, Frequency Reuse, Signal toInterference Ratio, Underwater Cellular Networks, User Capacity

T

I. INTRODUCTION

HE surface of the earth covered 70% by ocean and most ofwhich are unexplored. There is a lot of potential
applications in underwater environment such as oceanographic
monitoring, scientific exploration, disaster monitoring and also
especially for oil/gas field exploration. The underwater channel
Manuscript received Jun 29, 2012.
A. Nabavinejad is with the Najafabad Branch, Islamic Azad University,
Isfahan, Iran (phone: +98-311-6250695; e-mail: dc.nabavinejad@gmail.com).
S. Shahabi Ghahfarokhi is with the Faculty of Electrical Engineering andInformation Technology, Ilmenau University of Technology, Ilmenau,
Germany (e-mail: samar.shahabi-ghahfarokhi@tu-ilmenau.de).

remains one of the most challenging communication
environments. Designing a reliable communication systemremains an active area of research. The underwater is a time andspatiallyvarying
propagation
environment
whosecharacteristics pose significant challenges to the developmentof effective underwater wireless communications systems. For
several decades, electromagnetic waves, especially radio waves,
have seen extensive use in long range communications. Whenthe propagating medium is water, however, electromagnetic
waves find limited use. The high rate of absorption ofelectromagnetic signals in underwater has limited thedevelopment of electromagnetic communications systems.
Similarly, optical signals are also rapidly absorbed inunderwater and have the added disadvantage of scattering bysuspended particles and high levels of ambient light in theupper part of the water column. As a result, the development ofunderwater optical communications systems has also been
limited to few applications. Thus, acoustic signalling is primary
form of wireless underwater communication. Despite its
favourable characteristics relative to electromagnetic andoptical propagation in the underwater environment, the physics
of acoustic propagation pose significant challenge tounderwater acoustic communications systems [1]. Acoustic
propagation is best supported at low frequencies, and the
bandwidth available for communication is extremely limited.
An underwater acoustic channel is different from aground-based radio channel from many aspects, including[2]-[7].
1) Bandwidth is extremely limited. Lack of available
bandwidth is the biggest issue for underwater acoustic
communication /network. The attenuation of acoustic signal
increases with frequency and range. Consequently, theavailable bandwidth of underwater acoustic channels is limited
and dramatically depends on both transmission range andfrequency.
2) Propagation delay is long. Sound propagates through watermuch more slowly than electromagnetic waves through air.
Sound speed underwater depends on several factors, such aspressure, salinity, and temperature, but has a nominal value
around 1500m/s [4]. The slower acoustic propagation speed,
five orders of magnitude below the speed of light, greatly
impacts communication protocols. Low sound speed combined
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

with the large distances common in underwater networks
makes propagation delays on the order of several seconds
common. Long propagation delays in validate or decrease theusefulness of many techniques used in RF networks, such as
carrier sensing and handshaking protocols.
3) The channel impulse response is not only spatially varied but
also temporarily varied. In fact, the ocean is a dynamic andcomplex environment. Water movements are never-ceasing,and conditions are always changing with the time of day andweather. It is generally very difficult to predict. The fluctuation
nature of the channel causes the received signals easily
distorted. Acoustic signals travelling through it are distorted bya variety of factors. The major contributors are absorption,refraction and reflection (reverberation). Through these threefactors, the signals picked up by receivers are duplicated formsof the original, of varying levels of strength and distorted by acertain degree of spreading or compression.
4) Probability of bit error is much higher and temporary loss ofconnectivity (shadow zone) sometimes occurs, due to theextreme characteristics of the channel. Underwater acoustic
communication channels are affected by many factors such as
path loss, noise, multi-path, and doppler spread. All thesefactors cause high bit-error and delay variance.
5) In RF networks, the capacity of a channel depends primarilyon the bandwidth utilized and the SNR at the receiver. The
familiar equation from Shannon describes this relationship as
C = B log (1 + SNR) where C equals the channel capacity and2

B equals the channel bandwidth. Acoustic channels, however,involve a more complicated relationship between capacity anddevice distance, as we will discuss in more detail later. Since
the available bandwidth in underwater is severely limited,frequency reuse and cellular networks concepts are very
important. The key characteristic of a cellular network is theability to re-use frequencies to increase both coverage andcapacity. Adjacent cells must utilize different frequencies;
however there is no problem with two cells sufficiently far
apart operating on the same frequency. Since bandwidth is atpremium in an acoustic channel, the concept of frequency reuseis an appealing one and, given the immense Success andpracticality of terrestrial cellular systems, the question naturally
arises as to how the cellular concept applies to an underwater
acoustic environment. The first task in the design of a cellular
network is the selection of network topology, i.e. determinationof the cell size (radius) and the reuse pattern. In other words, thepractical questions that one wants to answer are the following:
For a given distribution of users and the desired information
throughput, what should the coverage area of one base station
be? How far should another base station operating in the same
frequency band be? According to what pattern should thefrequency bands be reused? In a terrestrial radio environment,
this problem has a very simple solution, but in an underwater
acoustic environment, the solution is complicated by the factthat the path loss does not consist only of the spreading lossk(which grows with distances r , where k is usually a number
between 1 and 2), But the absorption loss as well (which growsrwith distance as a , and depends on the frequency through the100

factor a) [8]. In other recent woks, the parameters of the cellular
networks designing have been calculated based on a roughapproximation of the attenuation and propagation model. In
section II, after driving the ratio of signal to interference forunderwater acoustic channels with more accuracy, theconstraints for the cell radius are determined. In section III, we
determine the cell size and the frequency reuse pattern needed
to support a desired number of users operating over a given area
within a given system bandwidth. In section IV we define thecapacity of an underwater acoustic cellular networks and the
maximal user density that can be supported within a given
bandwidth. Conclusions are summarized in Section V.
II. UNDERWATER CELLULAR NETWORKS PARAMETERS
The concept of frequency reuse is employed often in wirelesscommunication technologies, cellular systems are widely used
today and cellular technology needs to offer very efficient useof the available frequency spectrum. The basic principle offrequency reuse establishes its foundations over the idea ofutilizing a limited number of available (or assigned) frequency
channels in different and distant geographical locations.
Actually, a cellular network is usually divided into small
geographical segments or "cells" contained within a larger set
called "cluster," and each cell has its own set of uplink anddownlink carrier frequencies. In this manner, all theneighboring cells have different sets of career frequencies, andthis difference is kept in order to avoid interference between
their transmission and reception. This is in accord with a basic
law of wireless communication, which states that similarfrequencies cannot be used repetitively in a limited
geographical area, as doing so results in their cancellation or
creation of interference, which results in interrupting theprocess of communication. Analysis of frequency reuse
between adjacent clusters and optimal cell-radius selection
criteria has been carried out recently [8]. In this section, we try
to obtain underwater cellular network parameters with attention
to limitations of underwater acoustic communication.
A. Design Constraints
In underwater cellular network design, a design constraint
refers to some limitation on the conditions under which acellular network is developed, or on the requirements of the
network. The available bandwidth for an acoustic network
typically is not more than 15 KHz [9]. This bandwidth must be
shared among all users. In this section, the design of a cellular
network begins with selection of the frequency reuse pattern
and the cell radius. The total bandwidth B is divided among N
adjacent cells, which forms a cluster. The clusters are thenreplicated over an arbitrarily large area. In doing so, it isrequired that the co-channel signal-to-interference ratio begreater than some minimum, and that the bandwidth per user be
W ≥ W0 .
B. Underwater Acoustic Transmission
The ocean is a dynamic and complex environment. Water
movement is never-ceasing and conditions are always changing
with the time of day and weather. It is generally very difficult to
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

C. Propagation Loss
Propagation loss causes a sound wave’s amplitude togradually diminish as it travels. There are two main
contributors to this loss. The first is spreading loss and thesecond cause of transmission loss is due to attenuation loss
includes losses due to absorption, leakage out of ducts,
scattering and diffraction from the actual water medium.
Absorption describes those effects in the ocean in which a
portion of the sound intensity is lost through convention of heat[10]. Spreading loss does not represent a loss of energy, butrefers to fact that the propagation of the acoustic pulse is suchthat the energy is simply spread over a progressively larger
surface area, thus reducing its density. Spreading loss is notfrequency dependent. Underwater communications are
currently characterized by low data rates and long propagation
delays. The long propagation delays are a result of the speed ofsound being slow, and the low data rates are primarily a resultof utilizing low-frequency acoustic signals to minimize
absorption, which can be very high as shown in Fig. 1. Ifshorter range of communication is used then higher operating
frequencies can be used.
As mentioned before, an important difference between thedesign of underwater acoustic cellular systems and the RF
terrestrial cellular systems is the fact that underwaterpropagation shows a frequency dependent absorption, in
addition to distance-dependent path loss. Attenuation or pathloss that occurs in an underwater acoustic channel over a
distance r for a signal of frequency f is given by [11].
A(r, f ) = r k a(f ) rabsorption rate of acoustic energy in underwater

10

absorption(db/km )predict. Acoustic signals travelling through it are distorted by avariety a factors. The major contributors are absorption,refraction and reflection. Through these three factors, thesignals picked up by receivers are duplicated forms of theoriginal, of varying levels of strength and distorted by a certain
degree of spreading or compression.

101

10

10

10

2

0

-2

-5

10

-2

10

-1

0

10

1

10
Frequency

10

2

10

3

10

4

Fig.1. Absorption of underwater acoustic signal

10 log a(f ) = 0.11

f2
1+ f 2+ 44

2f4100 + f(3)+2.75.10 −4 f 2 + 0.003

where the quantity 10log a(f) has unit of dB/km and f has unitof kHz. Without loss of generality, we will assume in the
numerical examples a practical spreading factor k=1.5.

(1)Where k is the spreading factor, and a(f) is the absorption
coefficient. The acoustic attenuation, expressed in dB, is givenby
10 log A(r, f ) = k.10 log r + r.10 log a(f )(2)Fig. 2. Spherically spreading of sound wave

The first term in the above equation indicates the spreading loss
and the second term shows the absorption loss. Spreading loss
consists of cylindrical spreading and spherical spreading. Fig. 2
and Fig. 3 illustrate these two types of spreading loss.
Cylindrical spreading usually occurs in shallow water andspherical spreading in deep water environment. In low
frequency (100-3KHz), the absorption coefficient is given by[12].

Fig. 3. Cylindrically spreading of sound wave

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Using the acoustic path loss (2), the power of the signal
received at a distance d from the transmitter is given by
P(r) == stf min + W∫fmin

f min + W

∫f

min

−r

(4)

df

P(r) = s t .rwhich we assume to be constant and the integration is carriedout over the frequency band occupied by the signal, starting at
some fmin and extending over a bandwidth w.
By substituting a(f) from (2) into (4), we have:P(r) = s t .r

∫f minexp( −(0.011

f

2

1+ f 2+ 4.4

f

f min + W

∫f minexp( −(A

f2
1+ f

2

+B

f2

4100 + f

C = 2.75 × 10

−5

D = 3 × 10

−4

−k

K
E

f min + W

∫f

.K.
.

π
2

2

exp(− Ef )df

min

(7)

[ erf (fmin + W) − erf (fmin ) ]By substituting E and K into (7) and introducing the parameterγ+ Cf + D))df

1− (k + )2

P(r) = s t .γ .exp(−0.974r).r(8)

1−(k + )
2

(9)

Now by using (9) and the assumption that all transmitterstransmit the same power spectral density s t , we can calculate
the signal to interference ratio (SIR) by the following
expression

B = 4.4r ln 10
r ln 10(6)the power of the signal received at a distance r from thetransmitter is given by

where the coefficients A, B, C and D are given by
A = 0.01ln 10.

24100 + f

20

γ = 17.6exp(−0.974r).r+2.75.10 f + 0.0003).r.ln10df−k= s t .r

−k

2

−4 2

= s t .rx

∫ exp(−λ )dλπ

we obtain

where s t is the power spectral density of the transmitted signal− k f min + W

2

erf (X) =s t A −1 ( r, f ) df

r −k a (f )102

r ln 10In order to evaluate the integrals, we make the following
approximations,f min + W

∫f s t A ( r, f ) df
P(r)
SIR =
=
∑ Ii ∑ ∫ f + W st A−1 ( Xi , f ) df
f
i
−1

min

min

f2
1+ f 2

f2
f2≈
4100 + f 4100

≈1 ,

i

(5)
=

Since the signal frequency in underwater acoustic
communication is about several KHz, the above approximation
is valid. Then P(r) can be calculated as follows
f min + W∫f

P(r) = s t .r − k .

exp( −(A

min

f

2

1+ f

2

+B

f

2

4100 + fmin⎛ 1⎞−⎜ k + ⎟s t γ .exp(−0.974r).r ⎝ 2 ⎠⎛iwhere Xi is the interference user distance to the desiredreceiver. Therefore signal to interference ratio for underwater
acoustic channel is equal to2+ Cf + D))df
f min + W∫f

≈ st r−k .
= st r−kmin

f min + W∫fK.

exp(−(A + B

f

2

4100

+ Cf 2 + D))df2

exp( − Ef )df

where
E=

B
4100

+C

SIR =

exp( −0.974r).r∑ exp (imin

K = exp ( −(A + D) )Now by using the definition of the error function,

1⎞∑ s t .γ .exp ( −0.974 Xi ) . Xi −⎜⎝ k + 2 ⎟⎠⎛ 1⎞−⎜ k + ⎟
⎝ 2⎠

⎛ 1⎞
− k+
−0.974 Xi . Xi ⎜⎝ 2 ⎟⎠

)

(10)

As this equation shows, under the approximations given by (5),the signal to interference ratio is independent from frequency
and thus, this ratio is usable for cellules with different
frequencies. In the next sections we may apply (10) to obtainunderwater cellular network designing parameters.
III. CELL RADIUS AND THE REUSE NUMBER
The design of a cellular underwater network is addressedfrom the view point of determining the cell size and thefrequency reuse pattern needed to support a desired number of
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012103

users operating over a given area within a given system
bandwidth.
A. Calculation of Cell Radius
The signal bandwidth depends on the multiple-accesstechnique used. If time-division multiple-access (TDMA) isused, it equals the bandwidth allocated to one cell, B0 =B
N

where N is the reuse number. N is given by form
N = i 2 + ij + j2 in which i and j are integers. If
frequency–division multiple-access (FDMA) is used, it equals
the width of a frequency channel allocated to one of U users
sharing a cell, B0 =B
[13]. Let us assume (without loss ofNU

generality) TDMA is used. Assuming hexagonal cell geometry,
as illustrated in Fig. 4, the SIR is dominated by the six nearest
co-channel cells, and the worst SIR, which occurs at the celledge, is given by
SIR ≈P (R )(11)

6P ( D )where R is the cell radius and D the distance to the nearest
co-channel cell. In a two-dimensional system geometry, thetwo distances are related by D =QR , where Q = 3N and N is
the reuse number [8]. By using (9), the SIR condition can be
written in terms of the cell radius R and the reuse factor Q as

SIR ≈

P(R)
6P(D)

=

⎛ 1⎞
−⎜ k + ⎟2⎠

exp( −0.974R).R ⎝⎛ 1⎞−⎜ k + ⎟6 exp(−0.974D).D ⎝ 2 ⎠≈6⎛ k+ 1 ⎞⎜⎟exp(−0.974R(1 − Q)).Q⎝ 2 ⎠(12)

Fig. 5 shows the SIR as a function of cell radius for different
values of the reuse number. As is seen, the SIR increases withincreasing both R and N.
Since SIR has more than one minimum value, according to thesignal to interference ratio which was obtained before forunderwater channel, we have,
SIR ≥ SIR 0⎛ 1⎞−⎜ K + ⎟2⎠)⇒ −0.974R(1 − Q) ≥ ln(6SIR 0 Q ⎝

R≥

⎛ 1⎞
−⎜ K + ⎟ln(6SIR 0 Q ⎝ 2 ⎠ )−0.974R(1 − Q)

= R 0 (N)(13)

In other words, in order that the SIR to be greater than thedesign value SIR 0 , the cell radius should be greater than theminimum value of R 0 (N) . The minimal cell radius depends on
the reuse number N, and also on the required SIR and thesystem bandwidth.
The second system requirement is that W is greater than orequal to W ( W ≥ W0 ). For a given density of users ρ, thenumber of users per cell is ρα R 2 , where α =≈ exp( −0.974 ( R − D ) ) . ⎜ ⎟6⎝D⎠1

Thus to satisfy the condition SIR ≥ SIR 0 we should have

0

⎛ 1⎞
− K+
⎛ R ⎞ ⎜⎝ 2 ⎟⎠1

Fig. 4. Concept of cellular networks

3 3
for the2

hexagonal cell geometry (α = π if the cells are modelled as
circular). The bandwidth allocated to one cell is B/N, therefore,
the bandwidth per user should satisfyW=

B N

ρα R 2(14)≥ W0

The above inequality shows that the cell radius should be lessthan some maximum value,
R ≤ R1 ( N ) =

1

B

αρ

NW0

(15)

Finally, the number of users in a cell should be greater than 1,otherwise the cellular concept is meaningless. This fact yields
an additional condition

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012104

SIR 0 causes R 0 to increase, and increasing W0 causes R1 to

SIR for diffrent parameters

decrease.
B. Number of Users and Bandwidth per User
Once the reuse number is fixed, the cell radius R can be chosen
as any value between the minimum and the maximum. The
choice depends on the system optimization criterion. A natural
criterion is maximization of the number of users supported per
Hz of occupied bandwidth [8].

300

SIR

200

100

0

C=

-100
6

ρα R 2

(18)

B N

50

4

40
2

20
0

Cell Radius (R)To maximize this quantity for a given value of N, under theconstraints SIR ≥ SIR 0 and W ≥ W0 , the maximum value of the

30
10
0

Reuse Number(N)cell radius should be chosen, R = R ( N ) . With this choice we
1

have,

Fig. 5. Acoustic SIR for different Values of N and R

R≥

1

C max =

(16)

αρ

⎧⎪
1 ⎫⎪
R 0 ( N ) = max ⎨R 0 (N),⎬ ≤ R ≤ R1 (N)
αρ ⎪⎭
⎩⎪

(17)

illustrates the admissible region for a system with ρ =1user/km 2 ,B = 15kHz , SIR 0 = 20dB , and W0 =0.14 kHz . This region is

1

αρ

1

αρ

R = R ( N) ,1

Wmax ( N ) =,B = 60kHz

,

SIR

0

= 23dB,and

W = 3KHz . Stricter performance requirements cause the0admissible

regionbecomes=, determines the maximal

to a situation in which the range of solution becomes narrows,
and also to situation in which there is no solution. Thesesituations are illustrated in Fig. 7 for a system with
ρ = 0.35user / km

morenarrow.

the SIR will equal some SIR max (N) .

Alternatively, it may be desired that the per user bandwidth W
be maximized. In that case, the smallest cell radius should bechosen, R = R 0 ( N ) . This selection results in the maximum valueof W which is given by.

reuse number. For each admissible value of N, there is a range
of cell radii that can be chosen to design the system. In practice,
it is desirable to use a small value of N, because it facilitates thefrequency allocation process and minimizes the loss incurredby insertion of necessary guard bands. However, we may want
to choose somewhat greater than minimum to ensure a margin
for the selection of the cell radius.
A slight change in the system requirement ( SIR 0 , W0 ) may lead

2

(19)

Whenever the cell radius is chosen greater than R 0 (N) , theWe observe that R1 (N) decays faster than R 0 (N) . The point
at which R 0 (N) falls below

2⎞⎟
NW0 ⎟⎠
1
=
W0
B N
B

SIR will be greater than the design value SIR 0 ; whenThis expression defines the admissible region of R and N.
Only those values of R and N that belong to this region
constitute a valid design of underwater cellular network. Fig. 6

bounded by R 0 (N) , R1 (N) and the straight line

=

B N

⎛ 1
⎜ αρ
⎝

ρα ⎜

Combining the conditions (13), (15) and (16), we find that thecell radius should satisfy

ρα R12 ( N )Increasing

B N
2ρα 0 R 0 ( N )
B N

⎛
⎜
⎝

⎪⎧

ρα 0 ⎜ max ⎨ R 0 (N),

⎩⎪

(20)
1 ⎪⎫ ⎞⎬⎟αρ ⎭⎪ ⎟⎠

2

Hence, depending upon the chosen value of N , the associatednumber of users per cell U = ρα R 2 , depends on the cellradius. Fig. 8 illustrates the bounds on the quantity U for a2

system with ρ = 0.35user / km , B = 60kHz , SIR 0 = 20dB , andW = 900Hz0

IV. CAPACITY ANALYSISThe fact that the range of admissible network topologies isdetermined by the desired user density (among otherparameters) gives rise to the question of user capacity.
Acoustic communication channel capacity determines the
maximum data rate that can be supported (theoretically) by an

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

V. CONCLUSION

9R0
8Since the available bandwidth in underwater is severely limited,frequency reuse and cellular networks concepts are very
important. Simple rules of cellular radio system design do not
apply. The acoustic path loss depends both on the transmission
distance and the signal frequency and thus it imposes a boundon the range of admissible cell radius and frequency reuse
numbers N that can be used to meet the system requirements
specified by the co-channel SIR and per-user bandwidth( SIR 0 , W0 ). Reuse number N and cell radius R must satisfy aR11/√(αρ)7

R 0 (N) and R 1 (N)

6
5
4
3
2
1
0

105

0

5

10

15

20
25
30
35
40
Reuse Number N
Fig. 6. Region of acceptable values (R,N)

45

50

acoustic channel for a given source power and source/receiverconfiguration. We define the capacity of an underwater
acoustic cellular network as the maximal user density that canbe supported within a given bandwidth. To assess the user
capacity, we turn to the design condition (17). This condition
implies that in order for a valid design to exist, the following
inequality must holdR 0 ( N ) ≤ R1 ( N )set of constraints in order to constitute an admissible solution
for a cellular network topology. The range of solutions depends
on performance requirements and system parameters ρ and B.
Relationships between various system parameters arecomplicate, but design rules are (relatively) simple. This
constraint in turn implies a limitation on the overall user
capacity, which is defined as the maximal density of users thatcan be supported within a given bandwidth B. Capacity
increases as the operational bandwidth moves toward higher
frequencies. We showed that there exists a a set of constraints
that the cell radius and the reuse pattern R and N must satisfy inorder to constitute an admissible solution for the cellular system
topology. The constraints that acoustic propagation imposes on
the system design, and consequently on its capacity, serve as amotivation for further research into the spatial reuse methods
that will enable effective interference mitigation whileremaining efficient in terms of both bandwidth and powerusage [14].

(21)
9

Substituting for R 0 ( N ) and R1 ( N ) this condition can beR0
8

expressed in terms of the user density and the system
bandwidth. In this manner, we observe that for any given, thereR 0 ( N ) ≤ R1 ( N )
or
are
two
possibilities:
(a)

1

αρ

≤ R1 ( N ) . If (a) is true, then in order for a solution toexist, condition (18) implies that the user density has tobe ρ ≤ ( B W0 ) α NR 02 ( N ) . If (b) is true, then in order for asolution to exist, the bandwidth should be B ≥ NW0 . Combining
the two cases (a) and (b), we obtain the maximal user densitythat can be supported for a given N,⎧⎪( B W0 ) α NR 02 ( N )B W0 ≥ N⎪⎩0

B W0 < Nρmax ( N ) = ⎨(22)

ρmax ( N ) = ρ max ( N )

6
5
4
3
2
1
0

We now define the system capacity as the maximum ofconditional capacities ρmax ( N ) taken over the reuse number N

1/√(αρ)7

R0 (N) and R 1(N)

(b)

R1

0

5

10

15

20
25
30
Reuse Number N

35

40

45

50

Fig. 7. Region of acceptable values (R, N) by changing the design parameters(SIR 0 , W0 )

(23)

N

Therefore the maximal user density depends explicitly on the
bandwidth of the system and it also depends on the system
requirements ( SIR 0 , W0 ). Fig. 9 illustrates the maximum valueof bandwidth per user.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

[7]60
UPCmin
UPCmax50

Users per cell

40

30

20

10

0

0

5

10

15

20
25
30
Reuse Number N

35

40

45

50

Fig. 8. Bounds on the number of users per cell U as a function of the reuse
number N

Wmax
1800
W max
1600

W0

1400
Bandwidth per User (Hz)

106

1200

D. pompili, T. Melodia, and l. F, Akyildiz, “ Deployment Analysis In
Underwater Acoustic Wireless Sensor Network,” in Proc. of ACM
International
Workshopon
Underwater
Networks, Los Angeles, CA, USA, Sept. 2006 .
[8] M. Stojanovic, “On the Design of Underwater Acoustic Cellular
Systems,” in Proc. of IEEE oceans 2007-Europe, pp. 1-6, Jun. 2007.
[9] M. Cardei, “Energy-efficient scheduling and hybrid communicationarchitecture for underwater littoral surveillance,” Computercommunication, vol. 29, no. 17, pp. 3354-3365, Jan. 2006.
[10] Paul C.Etter, Underwater Acoustic Modeling Principle, Techniques And
Applicat ions. Elsevier Science Publishers, 1991.
[11] M. Stojanovic, “On the relationship between capacity and distance in anunderwater acoustic channel,” in Proc. first ACM international
workshop on Underwater Networks, 2007.
[12] M. C. Domingo, R. Prior, “Energy analysis of routing protocol forunderwater wireless sensor networks,” Computer communication, vol.
31, no. 6, pp. 1227-1238, Apr. 2008[13] T. S. Rappaport, Wireless communications principles an practice,Englewood Cliffs, NJ: Prentice-Hall, 1996.
[14] B. Sirnivasan, V. Rodoplu, “Capacity of Underwater Acoustic OFDM
cellular Networks,” in proc. of IEEE oceans, pp. 1-10, 2010.

Amirmansour Nabavinejad (S’12) was born inIsfahan, Iran, in 1978. He received B.S. and M.S.
degrees in electrical engineering from Islamic Azad
University of Iran, in 2003 and 2006, respectively.
Currently he is a lecturer at Mohajer Technical
University of Isfahan and Sepahan Institute of Higher
Education, Isfahan, Iran. His research interests are in
DigitalCommunications,
Signal
Processing
Applications,
Information
Theory,Coding,Underwater Data Transmission, Acoustic Channel
Modeling, Acoustic Imaging and Cellular Networks.

1000
800
600
400
200
0

0

50

100

150

Reuse Number N

Fig. 9. Maximum of bandwidth per user with the reuse number

Samar Shahabi Ghahfarokhi was born in Isfahan,
Iran, in 1984. She received the B.S. degree inelectrical engineering (Electronics) from Yazd
University, Yazd, Iran, in 2007. Currently, she isworking toward the M.S. degree in the Faculty ofElectrical Engineering and Information Technology,
Ilmenau University of Technology, Ilmenau,
Germany.
Her
research
interests
includeTelecommunication Systems, Information Theory,
Data Communication Networks, Cellular Networks,
Digital Signal Processing, and Digital Image Processing.

REFERENCES
[1]

[2]
[3]

[4]

[5]
[6]

J. Preisig, “Acoustic propagation considerations for underwater acoustic
communications network development,” ACM SIGMOBILE Mobile
Computing and Communications Review, vol. 11, no. 4, pp. 2-10, Oct.
2007.
Z. Jiang, “Underwater Acoustic Networks – Issues and Solutions,” inProc. IEEE Int. Conf. on Systems, Man and Cybernetics, 2003.
J. H. Cui, J. Kong, M. Gerla, and S. Zhou, “Challenges: Building
scalable mobile underwater wireless sensor networks for aquaticapplications,” IEEE Network, Special Issue on Wireless Sensor
Networking, vol. 20, no. 3, pp. 12-18, May/Jun. 2006.
D. Pompili, T. Melodia, I.F. Akyildiz, “routing algorithm fordelay-insensitive and delay-sensitive application in underwater sensor
networks,” in Proc. of ACM Conference on Mobile Computing and
Networking (MobiCom), Los Angeles, CA, September 2006.
M. C. Domingo, R. Prior, “Energy Analysis of Routing Protocol For
Underwater Wireless Sensor Networks,” Computer Communications,vol. 31, no. 6, pp. 1227-1238, Apr. 2007.
I. F. Akyildiz, D. Pompili, and T. Melodia, “Challenges for Efficient
Communication in Underwater Acoustic Sensor Networks.” ACM
Sigbed Review, vol. 1, no. 2, Jul. 2004.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012107

Research on the Scheme and Performance ofLinear SA in CDMA Application
Jijiang Chen1, Jing Zhou2, Jianfeng Lu3
1.Nanjing University of Posts and Telecommunications, Nanjing P.R. China
2.PAP Division of XX city, Nanjing P.R. China
3.The XX Regiment of The Air Force, Nanjing P.R. China
chenjijiang060703@126.com, angelazj928@vip.qq.com, air_jerry@yahoo.cn
Abstract—Firstly, this paper researches on the scheme of
amplitude weighting(AW) for smart antenna(SA) beamforming in linear array. With the proposed mathematicalmodel in this paper, QPSK base-band AW is an effective
method for linear SA to realize multi-user and multi-direction
transmission, by which the perfect directional beam-forming
can be realized with low complexity and low cost. Secondly,
this paper analyses the performance of linear SA with 6 arrayelements(AEs) in different application scenarios. Comparedwith carrier phase-shifting, the implementation scheme ofbeam-forming by AW has the feature of easy to implement andhigh reliability, so that the popularization and application of
SA is possible.
Keyword-smart antenna (SA); beam-forming; amplitude
weighting (AW); QPSK;6 array elements(AEs)I.
INTRODUCTION
Recently, smart antenna(SA) application in CDMA
mobile communication system is a very important research
direction as well as the basis of future multiple-input
multiple-output(MIMO)
system.
Consequently,
theapplication research of SA has great practical value. SA,
based on the phase-controlled antenna array(PCA)
technique, is an effective method to increase the frequency
and power efficiencies for mobile communication systems.
With the directional beam-forming of SA, we can realize theinstant tracking and orientation for mobile terminals in acellular mobile communication system. In CDMA mobile
communications system, generally, due to the complexity ofrealization, SA is mainly employed in base stationtransceiver. To perform the perfect directional beamforming, the direction of arrival (DOA) of mobile station(MS) signal should be acquired firstly from uplink, and thenwith this DOA the directional beam-forming of downlink ispossible.
The effect of space division multi access (SDMA)
formed by SA directional receiving and transmission is
expected to be utilized to separate multi access interference(MAI) and multi path interference (MPI) in CDMA system,
which can improve system spectrum utilization greatly andincrease the number of users in a given channel bandwidth orCDMA code-channel. The effect of SA SDMA is originatedfrom the directional reception and transmission principle ofphase controlled array antenna. If directional reception andtransmission is to be realized in base station (BS), DOA of
MS must be known.
Reference [1] states that it could realize downlink beamforming in base-band after the direction of arrival (DOA)
estimation in the uplink, but it has not given theimplementation scheme of beam-forming with QPSK baseband signal. Reference [2] presents the block diagram ofdownlink beam-forming, which is base-band weighting andcan form k-beam. Pointing out in [3, 4, 5, 6], the directionaltransmission of SA can be achieved with the method ofbeam-forming by base-band weighting, but the specificimplementation principle and implementation method has
not been shown. Many authoritative monographs [7, 8, 9, 10]
on SA only give the principle of PCA, i.e. the basic methodof directional transmission by carrier phase-shifting
weighting. Literatures [11, 12, 13, 14] on downlink beamforming only give the interrelated matrix solutions ofdownlink beam-forming, and have not given the relation
among the interrelated matrix solutions, DOA and the
direction vector of SA or the related processing. Many
patents [15, 16, 17, 18] related to TD-SCDMA system also
remain at the level of matrix solutions. One chief engineerof ZTE R&D center has said in [19] "In addition, comparing
to extensive research of uplink adaptive beam-forming
technique, the downlink performance has become the‘bottle-neck’ for the systems performance, so that there is anurgent need for the method of effective downlink adaptive
beam-forming." The top authority [20] of TD technique said
"The current application of SA technique is an initial stateof multi-antenna technique. In the future it will become
more powerful multi-antenna technique, which would havethe functions of beam-forming, space-time multiplexing (i.e.
MIMO) and SDMA." Therefore, it has been shown in thosepatents and articles that the research on SA in CDMA
basically remains at the stage of theoretical research bymathematics, which sharply does not match the conclusionthat SA has been widely used in mobile communication
system for TD-SCDMA.
Owing to the difficulty in realizing the accurate phaseshifting in the radio frequency and the large output of side

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

lobe and insertion loss introduced by radio frequency power
amplifier, people think that beam-forming by base-band
weighting is an effective method for SA to realize
directional transmission, while the relevant literature about
the principle or method of beam-forming by base-band
weighting have not been found.
Firstly, this paper gives the direction vector of SA andits principle of downlink beam-forming, and then derives anequivalent expression of beam-forming by amplitude
weighting (AW) based on that by phase-shifting weighting(PW) in line array. According to the expression, we canderive the schematic diagram, which uses the QPSK signal
by AW to achieve the beam-forming for SA in line array.
The correctness of scheme will be proved by the simulation
results. It also shows some simulation performance of 6-AE
in line array in the CDMA application. Compared withcarrier phase-shifting, the implementation scheme of beamforming by AW has the feature of easy to implement andhigh reliability, so that the popularization and application of
SA is possible.

II.

DIRECTION VECTOR OF SA AND ITS PRINCIPLE
OF DOWNLINK BEAM-FORMING

Structure of even line array is illustrated in Figure 1.
Assuming that the angle between the normal plane and thereceiving signal is  , then the delay of the receiving signals
between the neighboring two AEs is d sin  ,correspondingly, the phase between them is

2

n 

2



(n  1)d sin  , n  1, 2, N

 n is regarded as the intrinsic phase of the nth AE, which isrelated to the array position and the DOA of signal, i.e.
 .The values of phase-shifter inserted are respectively[ 1,  2   N ] , used to offset the impact of the intrinsic
phase, shown in Figure 2. Assuming that the receiving signalof the first AE is S (t )  cos wt , and then the formula of thephase-shifting weighting signal is as follows:
Sr (t )  cos(wt  1  1)  cos(wt   2   2)    cos(wt   N   N ) N cos wt(2)

If the impact of noise is not considered temporarily, thegain of beam-forming for SA, i.e. GSA  20lg N (dB) can beobtained. Because  n is related to the DOA, when the valueof the phase-shifter is determined, G SA is related to thenumber of code channel, thus, forming the function of
directional receiving, which is regarded as the diversity
reception of array antenna.
The schematic diagram in Figure 2 can also be used tounderstand the principle of beam-forming for transmitting.
Assuming that the transmitting carriers are the same
frequency and the phase-shifters are not introduced, thetransmitting carrier exist the phase difference. Otherwise,
there is no phase difference and can realize the diversity
transmitting.
Customarily, the intrinsic phase parameters of SA
above can be expressed by the complex formula, that  [1, ed sin  .


Making the phases of receiving signals by each AE
respectively [ 1,  2   N ] , we can draw the schematic
diagram of carrier phase-shifting weighting for beamforming, shown in Figure 2, where

108

j

2



d sin , , e

j

2



( N 1) d sin (3)]Frequently, the parameters above are called the direction
vector of SA, which are necessary and sufficient parameters
for SA. Making the weighting parameter of the receiving
signal is(1)
Intrinsic
Parameter

1

 2

...

 N

Phase
Shifter

1

 2

...

 N

Sr(t)

+

Fig.2. Schematic diagram of carrier phase-shifting weighting for beamforming

Fig.1. Diagram of even linear arrayj2w  [1, e d sin j2, , e ( N 1) d sin ](4)Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Thus,
Sr (t )   wnS (t )e jn  NS (t )(5)Consider that the high angle  has an impact on
DOA in Figure 3, where  is an intersection angle of DOA
and array plane. And then the delay of the receiving signals
between the neighboring two AEs is d sin  sin  ,correspondingly,
the
phase
betweenthemis2
d sin  sin  . The direction vector of SA is as follows:  [1, e

j

2



d sin  sin , , e

j

2



( N 1) d sin  sin ]

In the condition of multi-user and multi-direction
transmission, the PCA gives the basic principle of beamforming, i.e., feeding a different phase current into each AE
by inserting a phase-shifter in each AE circuit system, so thatthe radio waves given by each AE are able to realize
superposition in phase, and then form the synchronism
diversity transmitting gain of some specific direction.
III.

THE IMPLEMENTATION SCHEME OF BEAMFORMING BY AMPLITUDE WEIGHTING

The two carrier signals for orthogonal modulation in
QPSK modulation can be shown as following:(6)

For convenience, the impact of high angle  is notconsidered.
The schematic diagram of beam-forming for multipledirection in downlink is shown in Figure 4. In the conditionof directional transmission in downlink, it needs to detect
the user’s DOA and then make sure the direction vector ofSA., which is the sufficient parameter to realize thedirectional transmission.

sin(wt   ) , cos(wt   )Re[e jwt e j ] , Im[e jwt e j ]cos( wt   )  Re[e jwt  e j ] Re[e j ]cos wt  Im[e j ]sin wt

jt  j
e
]
 j
 j
 Im[e]cos t  Re[e]sin tsin(t   )  Im[e

θ

. . .

. . .

Weighting
Device

S1

(9)

(10)

According to the expressions above, the circuit diagramof directional transmission for SA realized by AW can bederived.
Circuit structure unit of a modulator is illustrated in
Figure 5. The upper part in figure 5 is for the realization
given by cos( wt   mn ) , which realizes the I-phase spacefrequency modulation and results in the I-phase carrier
phase-shift with  mn . Where  mn is the carrier phase-shift ofthe mth subscriber signal in the nth AE. I m (t ) is the input
data of I-phase path in downlink QPSK modulator, which isthe chip information stream of the input data spread andscrambled. The real part and image part of weighted
coefficient wmn generated by the generation circuit are

. . .

. . .

(8)

According to the above complex formulas of orthogonal
carrier, the following formulas are derived

DOA

Fig.3. High angle’s impact on DOA(7)Their corresponding complex formulas can be written as:

normalArray plane

109

Re( wmn )  Re[e jmn ](11)

Im( wmn )  Im[e jmn ]

(12)

Thus,

. . .
SN

Fig.4. Schematic diagram of beam-forming for multipledirection in downlink

Re[wmn ]  cos[ (n  1)sin m ](13)

Im[wmn ]  sin[ (n  1)sin m ](14)

 m is the DOA of the mth user.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012110

path denotes the data stream I m (t ) after series parallelIV.

IMPLEMENTATION SCHEME OF BEAM-FORMING
BY AMPLITUDE WEIGHTING IN MULTI-DIRECTION

In the third section, we obtain the circuit unit of QPSK
modulator based on the fundamental of baseband beamforming. It can be seen that the modulation process ofeach path signal I-phase or Q-phase includes two
independent processes: space-domain modulation andfrequency-domain modulation. In this section, we discuss thebeam-forming method of multiple subscriber directions
based on the modulator circuit unit proposed. The simplified
model of QPSK modulator is given. From Figure 5, in themodulator scheme, space domain modulation signal (11) and(12) of each user is different with the result of different DOA
for different subscriber m. But two orthogonal signalscos( wt ) and  sin(wt ) are same not only for the signals of Iphase path and Q-phase path but also for each subscriber.
Thus, a simplified circuit structure is obtained as shown in
Figure 6. In the simplified circuit structure, adders M n1 andM n 2 contain all space domain modulators needed byeach user, while multipliers M n 3 and M n 4 only containtwo frequency modulators respectively as shown in Figure5.
For the mth user data in Figure 6, I Im and I Q m denotethe input signal of I-phase path; Q Im and QQ m denote theinput signal of Q- phase path. With the simplified circuit
shown in Figure 4.Base-band beam-forming circuit neededwith N transmission directions can be drawn,shown in
Figure7.
The circuit block of baseband QPSK modulation beamforming of N subscribers is given in Figure 7.
The generation process of N baseband modulation
signals is similar with Figure 5.
V.

transformation, as well as that of the Q path. Because thecarriers of I and Q are orthogonal, the values of data I and Q
have no impact on beam diagram formed by SA. By figure 8
for the directional beam of single code channel in single
direction by using 6-AE in line array, better performance
could be achieved by tracking the mobile station. RAKE
receiver is unneeded for that situation.
Beam pattern for two code channels and two directions
is shown in Figure 9. The direction of beam slightly offsets
in the direction of 30° in Figure 9 (a), when the data of 2user fed to I path and Q path is same. It is also shown that
amplitudes of two directions have a slight increase, which
may be caused by introducing adjacent beam interference.
While the data is opposite, the amplitudes of beam have a
big drop, shown in Figure 9 (b), whose impact waiting for
further study.
Beam pattern for two code channels is shown in Figure
10, which is in the direction of 60°and 45°. Because of lessphase difference between them, the two beams can besuperimposed as one. When the data of 2-user is opposite,
the amplitude of beam changes slightly.
Beam pattern is given in Figure11 (a),in the directionof 30°、60°、90°、120°、150°,where the transmitting dataof each code channel is"1".If the transmitting data ofI1  Q1  1 is "-1" and the other is "1", the beam pattern isgiven in Figure 11 (b).
The simulation results above are satisfied with therequirement of infinite reflector, and also it requires that
there is no beam existing in the opposite direction. However,
it is difficult to satisfy the above condition in practicalapplication.
The simulation results in the case of no-reflector are
shown in Figure 13 (a), Figure 13 (b), Figure 14 (a)andFigure 14 (b) .

THE SIMULATION RESULTS

There are different scenarios in which a signal with one
or multi code channel transmits to the same direction, or
multi code channels transmits to different direction, theeffect of beam-forming from QPSK modulation data of
every code channel will be considered in these scenarios.
We have found that the polarity of QPSK modulation data
in multi-channel parallel transmission will have great impact
on the beam diagram of SA directional transmission.
Based on the above modulation beam-forming scheme,
some simulation results are presented in this section, withsimulation software matlab 7.0. Assuming that SA is a N=6
element line array with the AE space of  / 2 .
Beam pattern for a code channel is shown in Figure 8.
Figure 8 (a) and Figure 8 (b) respectively give the beam
pattern of QPSK modulator for I and Q paths with the samedata values in the direction of 90° and 30° ,where data of I

Fig. 8 (a)Direction of 90°

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Fig. 8 (b)Direction of 30°

111

I1  I 2  1 Q1  Q2  1

Fig.8. Beam pattern of single-user and single-direction.

I1   I 2  1 Q1  Q2  1

Fig.10. 2-user and direction differs 15°
Fig. 9 ( a) I1  I 2  1 Q1  Q2  1

Fig. 9 (b) I1   I 2  1 Q1  Q2  1

Fig. 11 (a) All transmission data “1”Fig.9. Beam pattern of two-user and two-direction

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

Fig. 11 (b) I1= Q1=-1, the other data “1”
Fig.11.

112

Fig. 13 (b) Direction of 90°

Result of 5-direction

Fig. 14 (a) Direction of 30°and 90°
I1  I 2  1 Q1  Q2  1
Fig.12.

Result of 9-direction

Fig. 13 (a) Direction of 30°

Fig. 14 (b) Direction of 30°and 90°
I1  I 2  1 Q1  Q2  1

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012VI.

CONCLUSIONSThis paper has studied the scheme and performance of
linear SA with 6-AE in different application scenario in
detail. With a reflector and in the situation of single
direction and signal code channel, there is no MAI and MPI
in the system , so the RAKE receiver is not needed.When
the number of directions and code channels increases,thescheme still has a good performance.Considering that themoderate direction is the necessary condition for CDMAsystem maintaining stable working, it is founded that thestability of SA CDMA system is not well in the condition oftransmitting to multi-direction when the direction is very
near. With no-reflector,there is always another beam in the
opposite. In a word,compared with carrier phase-shifting,the implementation scheme of beam-forming by AW is easy
to ensure stable system performance when linear SA with 6AE is introduced to TDD-CDMA system.

113[16] Zhongxing Telecommunication Equipment Co., Ltd, one Generating
Method of Sending Weight on Multi-user Beam-Forming, China
Patent No. 200810145717.X[17] Dingqiao Telecommunication Technology Co., Ltd (HuaWei), one
Method of Downlink Beam-forming in TDD System, China Patent
No.200710119516.8[18] Putian Information technology Research institute, one Method of
Beam-forming Combining Space with Time , China Patent
No.200510059383.0[19] Yanwen Wang, Current Research and Development of Beam-forming
for SA, Zhongxing Telecommunication Technology, 2003.6[20] Developing Smart Antenna Technology of TD-SCDMA with Strong
Determination—An Interview to Shihe Li-the Father of TD-SCDMAStandard,Telecommunication
Technology,
2008.3,pp.11-12,
www.ttm.com.cn

AUTHOR'S PICTURE & BIOGRAPHY

REFERENCES[1]

Zhongxing Telecommunication Equipment Co., Ltd, Principle andrealization of ireless System for TD-SCDMA , Posts & Telecom Press,2007.12[2] Liang Ma, Smart Antenna for Wireless Communications IS-95 andThird Generation CDMA Applications [M], China Machine Press,
2002.8[3] Ayman F.Naguib and Aroryaswami Paulraj, Capacit Improvementwith Base-Station Antenna Arrays in Celluar CDMA[J]. IEEE Trans.
on Vehicular techn, 2000,43(3):691-698.
[4] Ayman F.Naguib, Aroryaswami Paulraj and Thomas Kailath,
Performance of CDMA Cellular Networks with Base-Station Antenna
Arrays: the Downlink [J]. IEEE 1994
[5] Hsueh-Jyh Li and Ta-yung Liu, Comparison of Beamforming
Techniques for W-CDMA Communication Systems [J]. IEEE
Trans.on Vehicular techn, vol.52. NO.4.July 2003
[6] Klaus Ingemann Pedersen, Preben Elgaard Mogensen and Juan
Ramiro-Moreno,Application and Performance of Downlink Beamforming Techniques in UMTS [J].IEEE Communications Magazine.
October 2003
[7] Nanjing Institute of Electronic technology, Phase-Controlled Array
Antenna handbook [M], Electronic Industry Press, 2008.1[8] Ming Liu, Chaowei Yuan, the Technology and Application of Smart
Antenna [M], China Machine Press, 2007.1[9] Wei Yang, Junshi Chen, Array Antennas Technology in Mobile
Communication [M], Tsinghua University Press, Beijing Jiaotong
University Press, 2005.10[10] Ahmed El Zooghby, Smart Antenna Engineering [M], 2005 ARTECH
HOUSE,INC., Norwood[11] Josef Johannes Blanz, Apostolos Papathanassion, “Smart Antennas
for Combined DOA and Joint Channel Estimation in Time-SlottedCDMA Mobile Radio Systems with Joint Detection,” IEEE
transactions on vehicular technology, Vol.49, No.2, 2000
[12] Shaoli Kang, Shihe Li ,Algorithm of Downlink Beam-Forming Basedon Uplink Parameter in TD-SCDMA System [J], Journal of China
Institute of Communications, 2002, 23(8): 67-71.
[13] Qunying Wu, Shihe Li, Downlink Beam-forming in TD-SCDMA
System for Time delay spread and angle spread channels[J], Signal
Processing, 2002,18 (5): 427-430..
[14] Seungwon Choi, Donghee Shim, A Novel Adaptive Beam-forming
Algorithm for a Smart Antenna System in a CDMA Mobile
Communication Environment [J]. IEEE Trans. on Vehiculartechn,Vol.49.NO.5, September 2000, PP:1793-1806[15] Datang Mobile Communication Equipment Co., Ltd, the Method of
Beam-forming for SA and Base-band Signal Processor, China Patent
No. 200710098415.7

Jijiang Chen, male, from Shaoxing,
Zhejiang, is graduated from Collegeof
Telecommunications
andInformation Engineering, NJUPT in
2010, and currently pursuing themaster degree at NJUPT.Researchareas are the mobile Internet, Mobile
communications
andwireless
technology.
Email:chenjijiang060703@126.com

Jing Zhou, female, from Nanjing,
Jiangsu, is graduated from College of
Telecommunications
andInformation Engineering, NJUPT in
2010, and currently working in the
PAP Division of XX city.
Email:angelazj928@vip.qq.com

Jianfeng Lu, male, from Suzhou,
Jiangsu, is graduated from Harbin
Flight Academy, and currently
working in the XX Regiment of The
Air Force.
Email:air_jerry@yahoo.cn

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012114

Fig5. Beam-forming modulator circuit unit

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

115

Fig.6. Simplified form of the QPSK modulation

Fig.7. N-direction modulation beam-forming

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

116

Point-to-Multipoint andMultipoint-to-Multipoint Services on PBB-TE
SystemWonkyoung Lee*, Chang-Ho Choi*, Sun-Me Kim*
*Optical Internet Research Department, Electronics and Telecommunications Research Institute, 161
Gajeong-dong, Yuseong-gu, Daejeon, 305-350, Korea
wklee@etri.re.kr, chhchoi@etri.re.kr, kimsunme@etri.re.kr
Abstract— We have implemented point-to-multipoint (PtMP)
and multipoint-to-multipoint (MPtMP) services on a packet
transport system (PTS) based on PBB-TE. The point-to-multipoint(PtMP) connection in the PBB-TE system has been realized bygrouping point-to-point (PtP) packet transport layer (PTL) trunks
and mapping a BSI onto the PtP PTL trunks using a multicast
backbone destination address. For providing different capabilities
for service selection and priority selection, the PTS offers
customers three basic types of the port-based, C-tagged, andS-tagged service interfaces defined by the IEEE 802.1ah. To offer
customers different capabilities of the layer 3 applications andservices, moreover, an IP-flow service interface have been added.
In order to evaluate traffic performance for PtMP services in the
PTS, the PtMP throughputs for the link capacity of 1 Gbps at thefour service interfaces were measured in the leaves of the ingressedge node, the transit node, and the egress edge node. The
throughputs were about 96% because the B-MAC overhead of 22
bytes occupies 4 % of the 512-byte packet.
Keyword—Point-to-multipoint,
multipoint-to-multipoint,
packet transport system, PBB-TE, MAC-in-MAC encapsulation,
service interface

I. INTRODUCTIONT

HE network has been evolved into simpler and more
efficient structure since demands for bandwidth in today’s
network have been increased rapidly. In this situation,
SDH/SONET platforms are being replaced by packet transport
platforms as in reference [1]. The packet transport technology
such as Provider Backbone Bridge – Traffic Engineering(PBB-TE) and MPLS Transport Profile (MPLS-TP) is getting
the spotlight as a key point of the next generation network.
Provider backbone bridge – traffic engineering (PBB-TE)

Manuscript received October 10, 2012. This work was
supported by MKE. under Development of Packet-Optic
Integrated Network Transport Technology 2008-S-009-02.
The authors are with Optical Internet Research Department,
Electronics and Telecommunications Research Institute, 161
Gajeong-dong, Yuseong-gu, Daejeon, 305-350, Korea. (phone:+82-42-860-6519; fax: +82-42-860-1495; e-mail: wklee@
etri.re.kr).

defined by IEEE 802.1Qay [2] is representative carrier Ethernet
transport technology that extends well-known and widelydistributed Ethernet services to core of the public network while
maintaining simplicity, flexibility, and cost effectiveness of the
Ethernet service [3]. The PBB-TE adds transport hierarchy of
MAC-in-MAC encapsulation to Ethernet frames and provides
traffic engineering for connection-oriented paths and protection
switching within 50 ms.
In the next generation network, the PBB-TE technology
should provide multicast video streaming services and support
traffic engineering for end-to-end label switched paths. There
have been no proper solutions to multicast services on packet
transport platforms based on PBB-TE so far [4] since the
PBB-TE technology does not allow MAC learning, spanning
tree protocol, and broadcast of unknown frame for providing
deterministic, protected, and connection-oriented trunks andservices. Moreover, it has not been easy to classify layer 3
applications and services due to layer 3 service transparency ofthe carrier Ethernet transport. In this study, we propose asolution to multicast services and IP flow awareness that havebeen weak points of PBB-TE technology. We have
implemented a packet transport system (PTS) based on the
PBB-TE. The PTS provides multicast services of PtMP and
MPtMP and IP flow awareness. In order to evaluate theperformance of the PtMP services on the PTS, we havemeasured traffic throughputs for the link capacity of 1 Gbps at
port-based, C-tagged, S-tagged, and IP-flow service interfaces.
II. PACKET TRANSPORT SYSTEM BASED ON PBB-TE
As shown in Fig. 1, a PBB-TE network comprises a set of
backbone edge bridges (BEBs) and backbone core bridges(BCBs) that are connected by Ethernet tunnels referred as
Ethernet switched paths (ESPs) [2]. Backbone edge bridges are
responsible for adding transport hierarchy to customer frames iningress edge nodes and restoring customer frames by removing
the transport hierarchy in egress edge nodes. Backbone corebridges in transit nodes are responsible for swapping transportlabel or backbone VLAN identifier (B-VID). Each ESP as aconnection-oriented path is identified by the triplet of a
backbone source address (B-SA), a backbone destination
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

address (B-DA), and a B-VID. The ESP is also called a packet
transport layer (PTL) trunk. The PTL trunk is provided by an
external management system.

117

port of the UNI line card receives a customer frame, a backbone
service instance is allocated to the customer frame and then a
PTL trunk composed of a B-DA, a B-SA, and a B-VID iscreated in the NNI line card. The B-SA and the B-DA are MAC
addresses assigned to NNI line cards.
III. IMPLEMENTATION OF BIDIRECTIONAL
POINT-TO-MULTIPOINT SERVICES

Fig. 1. The PBB-TE network

Fig. 3. A point-to-multipoint service

Fig. 2. The structure of the packet transport system based on PBB-TE

Figure 2 is the structure of the packet transport system basedon the PBB-TE that has been implemented. The PTS consists ofuser network interface (UNI) line cards, network to network
interface (NNI) line cards, and a switch fabric that connects
between the UNI line cards and the NNI line cards. The PTS has
three types of customer service interfaces called the Port-based,
C-tagged, and S-tagged, which offers customers one or moretypes of service interfaces, each providing different capabilities
for service selection, priority selection, and service accessprotection. To offer customers different capabilities of the layer
3 applications and services, moreover, we have added a typecalled the IP-flow as the customer service interface. The
port-based service interfaces that attach to VLAN-unaware
bridges (802.1D Bridges), routers or end-stations, classify abackbone service instance (BSI) by just an input port. The
C-tagged service interfaces that attach to C-VLAN bridges(802.1Q Bridges), classify a BSI by an input port and a
C-VLAN identifier. The S-tagged service interfaces that attach
to a provider bridged network, map a service instance identified
by an S-VLAN identifier to a backbone service instanceidentified by a backbone service instance identifier (I-SID).
IP-flow service interfaces subdivide layer 2 flows using 5-tuple
information of layer 3 such as a destination IP address, a source
IP address, a destination port, a source port, and a protocol. If a

Figure 3 shows a point-to-multipoint (PtMP) service that isthe virtual Ethernet communication between one root that
distributes multicast video stream and multiple leaves thatreceive multicast video stream simultaneously. In the PtMP
communication, the B-DA conveyed in the backbone service
instance tag (I-TAG) is a multicast backbone MAC address andthe B-SA is a unicast backbone MAC address as shown in
Figure 4(a). In the NNI egress and the transit egress, theMAC-in-MAC frames of IEEE 802.1ah are replicated as manyas the number of output port configured by the corresponding
multicast MAC address and transmitted to the output ports asshown in Figure 4(b). The PtMP communication in the
backward direction is transmission from single leaf to one root.
Packet forwarding in the backward direction is the same withthat of the point-to-point connection. The PTL trunks that built
up PtMP connection are traffic engineered ESPs.
Figure 5 shows the proposed method for the PtMP service in
the PTS system based on the PBB-TE. A backbone service
instance is allocated to a customer frame by the backbone
service lookup table in the UNI LC according customer service
interfaces. PtP PTL trunks connected with each leaf node are
created in the NNI LC. A multicast B-DA (MB-DA) maps the
backbone service instance (BSI) with PtP PTL trunks. In theswitch fabric (SW), the MAC-in-MAC frames are copied as
many as the number of output slots. In the NNI LC, theMAC-in-MAC frames are replicated as many as the number of
output ports in a slot. By grouping PtP PTL trunks and mappinga BSI onto the PtP PTL trunks using a multicast B-DA
(MB-DA), PtMP connection in the PBB-TE system could be
realized.

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118

IV. IMPLEMENTATION OF BIDIRECTIONAL
MULTIPOINT-TO-MULTIPOINT SERVICE(a)Fig. 6. A multipoint-to-multipoint service(b)
Fig. 4. The PtMP operation in (a) the UNI linecard and (b) the NNI linecard

Fig. 5. The proposed method for the PtMP service in the PTS system

As shown in Figure 6, Multipoint-to-multipoint (MPtMP)
services provide multipoint-to-multipoint virtual Ethernet
connection between two or more UNIs. More UNIs can be
added to the same virtual Ethernet connection. The MPtMP
configuration creates a wide area network (WAN) for customers
with connectivity between every network site without
restoration. The difference between MPtMP and PtMP is that
the MPtMP has root endpoints only, which implies there is nocommunication restriction between endpoints, on the other hand,
PtMP has both root and leaf endpoints, which implies there is aneed to enforce communication restriction between leaf
endpoints.
The MPtMP solution based on the PBB-TE technology has
implemented differently from that of the PBB technology. The
PBB-based MPtMP solution have used MAC learning andspanning tree protocol used in the conventional bridge, however,MPtMP solution based on the PBB-TE have realized by the
combination of point-to-point paths provisioned.
To describe the MPtMP solution based on the PBB-TE,
Figure 7(a) and Figure 7(b) show the MPtMP operation such as
the copy and flooding of flooding packets and C-SA learning.
The learned Result Lookup block of the Figure 7(a) looks the
C-DA up in the C-SA learning table and checks whether the
C-DA of a customer frame has been learned or not. If the C-DA
is not learned, the customer frame is encapsulated with amulticast B-DA for flooding process. If the C-DA is learned, thecustomer frame is encapsulated with a unicast B-DA. The C-SA
MAC learning, which is the learning process for C-SAs, B-DAs,
B-SAs, and input ports of remote sites, has been added in the
UNI egress. If the B-DA is a multicast MAC address, theflooding of packets into the PtP PTL trunks takes place in the
NNI egress. If the B-DA is a unicast MAC address, the PtP
transmission into a specific PtP PTL trunk takes place.

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119

(a)

Fig. 8. Examples of bidirectional point-to-multipoint services using the packet
transport systems(b)Fig. 7. The MPtMP operation such as copy and flooding of packet and C-SA
learnig in (a) the UNI linecard and (b) the NNI linecard

V. EXPERIMENTS AND RESULTS
Figure 8(a) and Figure 8(b) show examples of bidirectional
point-to-multipoint services using the packet transport systems.
In the Figure 8(a), packets are replicates and multicasted in theingress edge node. In the Figure 8(b), packets are replicated andmulticasted in the transit node. Figure 9 describes the test
configuration for the bidirectional PtMP service consideringboth Figure 8(a) and Figure 8(b). Packets transmitted from thetraffic generator were inputted into the P11 port of the PTS 1.
And then, packets were duplicated in the NNI LC of the PTS 1
and transmitted to the P12 and P13 ports simultaneously.
Packets transmitted from the P12 port of the PTS 1 were
inputted into the P21 port of the PTS 2 and were duplicated as
many as output ports configured by the multicast B-DA of
01:00:5E:5E:00:02 in the transit LC of the PTS 2 and were
output to the P22 and P23 ports simultaneously. Packets
transmitted from the P22 port of the PTS 2 were inputted into
the P31 port of the PTS 3, and then, packets in which B-DA,
B-SA, and B-VID were removed, transmitted to the P32 port of
the PTS 3.

Fig. 9. Test configuration of the bidirectional PtMP service

Customer packets at the rate of about 960 Mbps except
inter-packet gap and preamble were transmitted from the R portof the traffic generator to the P11 port of the PTS 1 with the four
service interfaces. The customer packets were untagged frames,
C-tagged frames with the C-VID of 0x64, and S-tagged frameswith the S-VID of 0xC8 according to the service interface typesof port-based, C-tagged, IP-flow, and S-tagged. Packets werereceived simultaneously at the L3, L2, and L1 ports of the traffic
analyzer connected with the P13 port of the PTS 1, the P23 portof the PTS 2, and the P32 port of the PTS 3.
Figure 10 described the frames captured at the L3, L2, and L1
ports. The frames output from the P13 and the P23 ports were
MAC-in-MAC frames encapsulated with backbone VLAN tags
(B-TAGs), on the other hand, the frames transmitted from P32
port of the PTS 3 were the customer frames that B-TAG was
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

removed in the egress edge node. When the L3 port of the traffic
generator sent customer frames corresponding with various
service interfaces, the only R port of the traffic analyzer
received the customer frames.

Fig. 10. Frames received in the three leaves

In order to evaluate traffic performance for PtMP services in
the PTS, we have measured PtMP throughputs at output ports of
the ingress edge node, the transit node, and the egress edge node.
Each input traffic rate for port-based, C-VLAN tagged,
S-VLAN tagged, and IP flow service interfaces was 1 Gbps
including IPG and preamble. To exclude impacts of policing
and shaping, policing and shaping set to be off. Table 1 shows
experimental results of throughputs for the bidirectional PtMP
service. Throughputs for 512-byte packet and physical link
capacity of 1 Gbps were measured 100 % at the two ports of theingress edge node and the transit node. In the egress edge node,
however, 4 % of the traffic dropped. It attributed that the
B-MAC overhead of 22 bytes occupies 4 % of the 512-byte
packet.

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interfaces.
VI. CONCLUSION
The PBB-TE technology is the carrier Ethernet transporttechnology that provides connection-oriented Ethernet,
end-to-end QoS, and robust OAM. However there have been no
proper solutions for multicast services in the PBB-TE
technology since the PBB-TE technology does not allow MAC
learning, spanning tree protocol, and broadcast of unknown
frame for providing deterministic, protected, andconnection-oriented trunks and services. Moreover, it has notbeen easy to classify layer 3 applications and services due tolayer 3 service transparency of the carrier Ethernet transport.
We have implemented the packet transport system based on
PBB-TE, which provides the multicast services and the IP flow
awareness. The PBB-based MPtMP solution have used MAC
learning and spanning tree protocol used in the conventional
bridge, however, MPtMP solution based on the PBB-TE haverealized by the combination of point-to-point paths provisioned.
Traffic throughputs for the packet of 512 bytes and the link
capacity of 1 Gbps have been measured at port-based, C-tagged,
S-tagged, and IP-flow service interfaces. The throughputs ofleaves with MAC-in-MAC encapsulation (IEEE 802.1ah) were
measured 100 %. After removing the MAC-in-MAC
encapsulation in the egress edge node, 4 % of the traffic
dropped. It attributed that the B-MAC overhead of 22 bytes
occupies 4 % of the 512-byte packet. The larger the packet size
was, the smaller the percentage of the B-MAC overhead was.
Therefore the larger the packet size is, the impact of the B-MAC
overhead of the PBB-TE technology on the traffic throughput
will be reduced. This study supplements solutions for multicast
and L3 level services of the PBB-TE technology, therefore thepacket transport system based on PBB-TE could support
broadband services such as IPTV or multicast video streaming.
ACKNOWLEDGMENT
This project is processed by MKE. [2008-S-009-02,
Development of Packet-Optic Integrated Network Transport
Technology]
REFERENCES
[1]
[2]
[3]

Fig. 11. Impact of B-MAC overhead on the traffic throughput according topacket sizes

Figure 11 explains the impact of the B-MAC overhead thatoccupying the packet on the traffic throughput according topacket sizes. The larger the packet size is, the smaller thepercentage of the B-MAC overhead is, and then, the fewer
packets drops. In bidirectional PtMP connection, we havemeasured the throughput of 96 % for the leaf without
MAC-in-MAC encapsulation and the throughputs of 100 % for
the two leaves with MAC-in-MAC encapsulation at four service

[4]

[5]
[6]
[7]

B. Pratt, “Multi-Layer Switching in Packet Optical Transport System,”
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Virtual Bridged Local Area Networks – Amendment: Provider Backbone
Bridge Traffic Engineering, IEEE Std. P802.1Qay, 2008.
K. Fouli and M. Maier, “The Road to Carrier-Grade Ethernet,” IEEE
Communications Magazine, 2009.
Claus G. Gruber and Achim Autenrieth, “Carrier Ethernet Transport in
Metro and Core Networks,” 13th International Telecommunications
Network Strategy and Planning Symposium, 2008.
Wonkyoung Lee, and et al., “Implementation of hierarchical QoS
mechanism on PBB-TE system,” 9th COIN, 2010.
J. Heinanen and R. Guerin, “RFC 2698 – A Two Rate Three Color
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2309, 1998.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 3, November 2012

121

Wonkyoung Lee received the B.S. degree from the
Department of Electronic Engineering, Pusan National
University, Pusan, Korea in 1999 and the M.S. degree
fromthe Department of Information andCommunication Engineering, Gwangju Institute of
Science and Technology, Kwangju, Korea in 2001.
From 2001 to present, she is with the Optical Internet
Research
Department,
Electronics
andTelecommunications Research Institute, Daejeon, Korea.
Her research interests include packet optical transport network and network
management.
Chang-Ho Choi received his B.S. degree in Mining andMinerals Engineering from Chonbuk National
University, Jeonju, Rep. of Korea, in 1998 and M.S.
degree in Information and Communication Engineeringfrom Chonbuk National University, Jeonju, Rep. of
Korea, in 2000. He is currently working towards his PhD
degree at the Chungnam National University, Deajeon,
Rep. of Korea. Since 2002, he joined ETRI, Rep ofKorea, his work has been focused on Ethernet and packet transport network
technology research. His current interests are packet-optical transport systems
and protection switching in packet transport network.
Sun-Me Kim received the B.S. degree in Computer
Science from Chungnam National University, Daejeon
and the M.S. degree in Computer Science from Pohang
University of Science and Technology, Pohang, Korea, in1991 and 1993, respectively. Since 1993, she has beenwith Electronics and Telecommunication ResearchInstitute (ETRI), Daejeon, Korea, where she is currently a
Principal Researcher. Her interested research topics arepacket-circuit-optical converged switching system,
multi-layer transport network control and management in packet-opticalconverged network and IDC network control.

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