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
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
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Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
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Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
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Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
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 1

1

Joint Source-Channel Coding with Unequal Error Protection using Asymmetric Turbo Codes

1

Hanxin WANG, Cuitao ZHU, Chengyi XIONG and Shaoping CHEN
Department of Electronics and Information Engineering, South-Central University for Nationalities, Wuhan, China

2

Efficient and Format-Compliant Video Encryption Algorithm inCompressed Domain for H.264/AVC

11

Li ZHUO, Niansheng MAO, Haojie SHEN, Jing ZHANG, Xiaoguang LI
Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, Chin

3

A Method for Evaluation of Quality of Service in Computer Networks

17

Tomasz Bujlow, Sara Ligaard Nørgaard Hald, Tahir Riaz, Jens Myrup Pedersen
Section for Networking and Security, Department of Electronic Systems Aalborg University, DK-9220, Aalborg East, Denmark4

Frequency Offset Estimation and Cell Search Algorithms for OFDMA Based Mobile WiMAX

26

Fakher Eldin M. Suliman, Nuha M. Elhassan, Tertiel A. Ibrahim
Fakher Eldin M. Suliman, Nuha M. Elhassan, Tertiel A. Ibrahim

5

Design and Performance Analysis of a newly designed 32-User Spectral Phase Encoding system operating at
2.5Gb/s for Fiber-Optic CDMA Networks
Savita R.Bhosale* Mr. S.B.Deosarkar **Dr. BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY, LONERE, TAL. - MANGAON, DIST. - RAIGAD, PIN – 402103

33

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

1

Joint Source-Channel Coding with Unequal Error
Protection using Asymmetric Turbo Codes
Hanxin WANG, Cuitao ZHU, Chengyi XIONG and Shaoping CHEN
Department of Electronics and Information Engineering, South-Central University for Nationalities, Wuhan, China
wanghx8888@163.com, zhucuitao@163.com, xiongchengyi@qq.com, spchen@scuec.edu.cn
Abstract—In this paper, we devise an efficient joint
source-channel coding scheme for robust image transmission
over noisy channels. We firstly present a novel interleaver, named
unequal row column cyclic cross interleaver, which could
improve the error correction capability of turbo codes effectively.
Secondly, we devise two types of asymmetric turbo codes which
consist of the parallel concatenated turbo codes using two
non-identical component encoders with the different constraint
lengths and mixed types of generator polynomials. The presented
asymmetric turbo codes can optimize the bit error rate of bothwater-fall region at low signal to noise ratio and error-floor
region at high signal to noise ratio, they outperform theconventional symmetric turbo codes but with reduced decoding
complexity. Finally, we propose a joint source-channel coding
scheme based on unequal error protection using asymmetricturbo codes. This scheme can adaptively adopt different coding
strategies, different interleavers of turbo codes, various decoding
algorithms and appropriate decoding iterative numbers
according to the different significant levels of image data streams
and the varying conditions of estimated channel state
information. The proposed scheme can also dynamically adjust
the source compression ratios and channel code rates by
optimizing the rate allocation according to the calculated peaksignal to noise ratio of reconstructed images and the estimatedchannel states information. The experimental results show thatthe proposed joint source-channel coding scheme can evidently
increase the peak signal to noise ratio of the reconstructed images
and improve the visual effect of the images but with no additional
bandwidth, the scheme is more adaptive and feasible.
Index Terms—Joint source-channel coding; Unequal error
protection; Asymmetric turbo codes; Interleaver; Bit error rate;
Water-fall, Error-floor; Peak signal to noise ratio

Manuscript received May 3, 2012. This work was sponsored by the National
Natural Science Foundation of China with Grant No.60972081, No.61072075,
the Natural Science Foundation of State Ethnic Affairs Commission with
Grant No.10ZN07 and the Key Project for the Natural Science Foundation of
Hubei Province with Grant No.2009CDA139, it was also supported byInternational Fellowship Researcher of Chonbuk National University of Korea
with Grant No. IFR2002.
Hanxin WANG is with the Department of Electronics and Information
Engineering, South-Central University for Nationalities, Wuhan, China (Tel:86-13343589736; fax: 86-027-67842815; email: wanghx8888@163.com)
Cuitao ZHU is with the Department of Electronics and Information
Engineering, South-Central University for Nationalities, Wuhan, China (email:zhucuitao@163.com)
Chengyi XIONG is with the Department of Electronics and Information
Engineering, South-Central University for Nationalities, Wuhan, China (email:
xiongchengyi@qq.com)
Shaoping CHEN is with the Department of Electronics and Information
Engineering, South-Central University for Nationalities, Wuhan, China (email:spchen@scuec.edu.cn)

I. INTRODUCTION
With the development of wireless multimedia
communication services, the fast growing consumer demandhas spurred more interest in the image and video datatransmission over noisy channels. The varying wirelesschannel causes high transmission errors for multimedia data
streams and results in deterioration of reconstructed image andvideo qualities. To enhance the efficiency and reliability ofdata streams, source data compression and channel errors
correction technologies are two effective solutions in thedigital multimedia communication. On the one hand, thesource data have numerous redundant information which
require to be processed. Compressed data streams are very
sensitive to channel errors because their most redundancy bits
are removed. Sometimes a few errors may destroy the entire
data streams and thus affect the qualities of restored image andvideo. On the other hand, a large amount of channel errors
could occur due to the poor wireless channels whose
capacities are limited and time-varying. Considering thecharacteristics of both source data streams and channel state
information (CSI), joint source-channel coding (JSCC) is apromising approach to achieve better performance in limited
bandwidth and limited power system. JSCC technique has
been attractive in delay-sensitive multimedia transmission
system, especially in heterogenous networks where users may
have different quality of service (QoS) requirements [1] [2].
JSCC has two main design issues. One is to design anappropriate channel protection method, and the other is to
optimally allocate the given bandwidth resource between thesource code and the channel code so as to achieve the best
possible end-to-end performance in the noisy environment [3].
Unequal error protection (UEP) is an intelligent solution for
JSCC. UEP is often applied to Forward Error Correction(FEC), the data bits are grouped according to some criteria
capable of determining their importance to the restored source
data, and the different channel codes are assigned to different
source bit groups [4] [5] [6]. In recognition of the fact that theeffects of bit errors in encoded sources are usually moredetrimental in some bit groups than in others, several UEP
schemes for image transmission using turbo codes are reported
according to the significant levels of the image bit streams andvarying channel characteristic [7], [8], [9]. But most of them
mainly focus on UEP with conventional symmetric turbo
codes (STC). However, STC evidently exist some defects that

Copyright ⓒ 2012 GiRI(Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

they have either a good water-fall performance at low signal tonoise ratio (SNR) or a good error-floor at high SNR, but not
both over the entire range of SNR [10]. Asymmetric Turbo
codes (ATC) are the better choice among the improvement of
performance, the overall time delay and the computational
complexity of decoding algorithms. Several ATC are
investigated to improve bit error rate (BER) in the whole SNR
ranges compared with STC [11], [12]. This paper presents aneffective JSCC design based on UEP using ATC, which canadaptively adopt different coding strategies, different
interleavers of turbo codes, various decoding algorithms andappropriate decoding iterative numbers in terms of thedifferent significant levels of source bit streams and thevarying conditions of estimated CSI. This scheme candynamically adjust the source compression ratios (CR) andchannel code rates according to the calculated peak signal tonoise ratio (PSNR) of the reconstructed images and theestimated channel conditions. The proposed scheme can also
evidently increase PSNR of the images and improve the visual
effect of the images but with no additional bandwidth. The
remainder of paper is organized as follows. In section II, we
present a novel interleaver named unequal row column cyclic
cross (URCCC) interleaver, it could improve the error
correction capability of turbo codes effectively. In section III,
we devise two types of ATC using the different constraint
lengths and mixed types of generator polynomials to optimize
the BER performance of both water-fall and error-floor forturbo codes. In Section IV, focusing on the characteristic thatthe image data streams compressed by set partitioning inhierarchical trees (SPIHT) algorithm have different sensitivity
to channel bit errors, we propose an efficient JSCC schemewith UEP using the designed ATC. The simulation results for
JSCC are shown in section V and the conclusions are drawn in
the last section.
II. UNEQUAL ROW COLUMN CYCLIC CROSSINTERLEAVER
The interleaver is one of the essential components for turbo
codes and plays an important role in determining theperformance and decoding computational complexity, because
the interleaver directly affects the distance properties of theturbo codes. An interleaver is used between the twoconstituent encoders of turbo codes to provide randomness
and produce high weight codewords. The design aim of a goodcode-matched interleaver is to eliminate low weightcodewords with significant contributions to the error
performance and to reduce the number of other low weightcodewords which could not be eliminated [13], [14], [15].
Considering the drawbacks of common block and random
interleavers which can not eliminate the more information bits
correlation or not permute the low weight codewords sequenceeffectively, we present a new unequal row column cyclic crossinterleaver, i.e. URCCC. This new interleaver can morethoroughly eliminate the information bits correlation thanblock and random interleavers, it can also permute the low
weight codewords sequence into high weight codewords ones.

2

A. Algorithm of URCCC interleaver
To improve the BER performance of turbo codes
significantly, the good interleaver can be implemented to
avoid low weight codewords and to provide sufficient
randomness for the input sequences. Many interleavers havebeen done on design of turbo codes. The block and random
interleavers are the most commonly used in turbo codes. But
they can not preferably produce the fewest output codewords
sequence with low weight. An optimal interleaver should be
able to generate the largest minimum codewords weight withthe lowest number of codewords of that weight. So, a novel
interleaver, named URCCC, is firstly proposed to offer
superior performance for ATC design. The proposed algorithm
of URCCC is described as follows:
Step 1: Information bits data are written into m × n blockinterleaver matrix in row wise from left to right and top tobottom, as shown in Figure 1 (a). For convenience ofdescription, we set m = 8 and n = 8 , the data in Figure 1
represents bits sequence number of information data.
Step 2: Odd row bits data are rearranged as follow formula(1) and even row data are reordered as formula (2),respectively. C i denotes interleaver table, i is column
number, n is column length, moddenotes modular
arithmetic, k and p are adjustable parameters, k < n ,p < n , k and p are relatively prime to n , while k ≠ p .
The process result is shown in Figure 1 (b).

Ci = (k × i )(mod n) + 1,

0<i≤n

(1)

Ci = ( p × i )(mod n) + 1,

0<i≤n

(2)

Step 3: Odd column bits data are rearranged as follow
formula (3) and even column bits data are reordered as follow
formula (4), respectively. C j is also interleaver table, j is
row number, m is row length, a and b are adjustableparameters, a < m , b < m , a and b are relatively prime
to m , while a ≠ b . The process result is shown in Figure 1(c).
(3)
C j = ( a × j )(mod m ) + 1,
0< j≤mC j = (b × j )(mod m) + 1,

0< j≤m(4)

Step 4: The interleaved bits data are read out frominterleaver matrix of Figure 1 (c) in column wise from top tobottom and left to right.
The good interleaver design would be capable of providing
sufficient randomness. For some special information bit
streams, such as low weight and symmetric bit data, thedifferent interleavers can obtain complete different interleaved
effectivenss. E.g. the input bits sequence 100000010...
010000001, the output bits data sequence after the blockinterleaver are all the same as the original input sequence, as
shown in figure 2 (a), it can't reach the purpose of interleaver.
But using URCCC interleaver, the input sequence is wellpermuted randomly, it can avoid the appearance of the fixed
point, as shown in figure 2 (b). Here, the data in Figure 2
represents information bits.
Copyright ⓒ 2012 GiRI(Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

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Fig. 2 (b)Fig. 2. Effectivenss of the different interleaver(a) Block interleaver (b) URCCC interleaver

Fig. 1 (a)
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B. Simulation and Analysis of URCCC interleaver
The BER performance simulations of URCCC interleaver
used in turbo codes is shown in Figure 3. For the ease ofcomparison, the simulation results of block and random
interleavers are also drawn. In simulation experiments,
information bits size is 378, generator polynomials of two
component codes for turbo codes are 13 and 15 (in octal), one
half code rate, 4 iteration number for log-MAP algorithm,
BPSK modulation, AWGN channel. It is observed that
URCCC interleaver outperform conventional block andrandom interleavers.
-1

10

-2

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-3

10
BER

4

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-6

Fig. 1 (c)10Fig. 1. Design for URCCC interleaver(a) step 1 (b) step 2 (c) step 3

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Fig. 2 (a)

-7

0.5

Block Int
Random Int
URCCC Int
1

1.5
2
Eb/N0(dB)

2.5

3

Fig. 3. BER versus Eb/N0 for URCCC interleaver in turbo codes

URCCC interleaver can not only reduce the more
information sequence correlation than block and random
interleavers, it can also permute the low weight codewords
sequence into high weight codewords sequence andsignificantly improve the performance of turbo codes. In thewhole process of interleaving algorithm, URCCC interleaver
are based on linear modular arithmetic, it has faster calculation
speed and low complexity hardware requirement. For theprocess of row or column interleaving, it would complete only
through the simple cyclic operation but not with a interleaving
memory table, so it can save storage space. Moreover,

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

URCCC interleaver has adjustable parameters, it candynamically select the parameters to adapt for various channel
states and to improve the system error correction capability,
the presented URCCC interleaver is more flexibility androbust.
III. ASYMMETRIC TURBO CODES

4

shown in Figure 5, Lc = 4 ⋅ ( Eb / N 0 ) ⋅ R , where Eb / N 0
denotes received bit energy to noise power ratio and R
represents the code rate [16]. Through serial-to-parallel (S/P)
de-multiplexer, Lc yk is separated into Lc yks , Lc y1k p and

Lc yk2 p corresponding to the systematic and two parity bits,
respectively.

A. Review of Turbo Codes
Turbo codes have a near Shannon limit error correction
capability through iterative decoding based on soft-input andsoft-output (SISO) decoding algorithm. Turbo codes havebeen widely used in the wireless mobile communication
system [16]. The conventional STC consist of two parallel
concatenated recursive systematic convolutional (RSC)
constituent encoders separated by an interleaver. Generally,
these constituent encoders are the identical component codeswith the same constraint lengths and the same generator
polynomials [17], [18]. The BER performance curve of turbo
codes is divided into two regions. The first region is called
water-fall in lower SNR, the BER curve decreases rapidly atwater-fall region. The second region is error-floor in higher
SNR, the curve flattens in this region [10]. The STC have
either good water-fall performance in lower SNR or gooderror-floor in higher SNR, but not both over the entire rangesof SNR. ATC are the better trade-off among the improvementof performance, overall delay and computational complexityof decoding algorithm. The performance evaluation of the
ATC for the 3rd generation communication system (3G) are
given in [11], [12].
ATC encoder is composed of two different parallel
concatenated RSC1 and RSC2 encoder separated by aninterleaver (Int), as shown in Figure 4. u k
is input
information bit streams, x ks is the systematic information bits,

x

1p
k

and x

2p
k

are the parity bits generated by RSC1 and RSC2,

respectively. Puncturing of parity bits x 1k p

and x k2 p

achieve the various required code rate. ck

is the coded bit

streams which are consisted of

x ks

and

x kp

can
by

parallel-to-serial (P/S) multiplexer.

x ks

uk

Le 2 ( uk )L e1 ( u k )L a 2 (u k )1p

Lc y kLcs

Lc y kyk

2p

Lc y kuk

L2 (u k )Fig. 5. ATC decoder

At the first iteration of decoding, a priori Log Likelihood
Ratio (LLR) value of SISO1 decoder La1 (u k ) is initially set
to 0, the systematic bits LLR value Lc yks and parity bits LLR
value Lc y1k p are inputted into SISO1 decoder. The extrinsic
LLR value

Le1 (uk )produced by SISO1 decoder isinterleaved and sent to SISO2 decoder as a priori value
La 2 (uk ) . Moreover, Lc yk2 p and interleaved Lc yks are
simultaneously sent to SISO2 decoder to produce extrinsic
value Le 2 (uk ) , and then Le 2 (uk ) is de-interleaved (De-int)
and fed back to SISO1 decoder as a priori value for the next
decoding iteration. The iterative decoding will keep on
working until either a stopping criteria is met or a preset
maximum number of iterations is reached. When the iterative
decoding is finished, the output value L2 (u k ) of SISO2
decoder is de-interleaved and sent to hard decision (HD) to
produce estimated value û k .
B. Proposed Two Types of ATC

ck

x1k p
x kp

xk2 p

Fig. 4. ATC encoder

The coded bits ck

L a1 ( u k )are then binary phase shift keying(BPSK) modulated and transmitted through an additive white
Gaussian noise (AWGN) channel with a double sided power
spectral density of N0 /2. At the receiver, the matched filter
output yk is multiplied by the channel reliability value Lc , asCompared with STC, the constraint lengths or generator
polynomials of two RSC component codes are different forATC. Here, we design two types of ATC which consist of theparallel concatenated turbo codes using the non-identical RSC
component codes with the different constraint lengths or thedifferent types of generator polynomials.
1) Type-I of ATC:
Type-I of ATC consist of two different component codeswith the identical constraint lengths but the different types ofgenerator polynomials. As we all know, the performance of
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

turbo codes at low SNR is determined by the distancespectrum, but the performance at high SNR is determined by
the effective free distance. There are a number of key design
parameters involved in determining the distance spectrum andfree distance for turbo codes, such as the choice of component
encoders and the types of interleavers, etc. Several interleavers
are designed to improve BER performance of turbo codes [13],[14], [15]. Here, we firstly focus all attention on using thedifferent component codes with the primitive andnon-primitive feedback generator polynomials to construct
ATC, because the component codes with non-primitive
generator polynomials may optimize the distance spectrum toimprove water-fall performance and primitive generator
polynomials may enlarge the free distance to reduce
error-floor.
For convenience, the generator polynomial is denoted by G
(in octal), the constraint length is denoted by K, respectively.
P-G denotes primitive generator polynomial of the constituent
encoder of turbo codes, NP-G denotes non-primitive
polynomial. The various generator polynomials withconstraint lengths K=3, 4, 5 and the types of the primitive andnon-primitive polynomials are listed in Table I.
TABLE IThe various constraint lengths and the types of generator polynomials
Constraint
lengths

Generator polynomials
P-G

NP-G

K=3

G=[ 7, 5]

G=[ 5, 5]

K=4

G=[13,17]

G=[15,17]

K=5

G=[23,35]

G=[37,21]

And then, four type-I of ATC with the same constraint
lengths but mixed types of generator polynomials arepresented, as shown in Table II. Here, K1 and K2 denote
constraint lengths of two component codes RSC1 and RSC2,
respectively ； G1 and G2 denote generator polynomials of
RSC1 and RSC2, P and NP denote primitive andnon-primitive polynomial, suffix 1, 2 of P and NP are
corresponding to RSC1 and RSC2. We select K1=K2=5. In
fact, P1-P2 and NP1-NP2 turbo codes with the same constraint
lengths are just the conventional STC.
TABLE II
Type-I of ATC with the same constraint lengths and mixed types ofgenerator polynomials
Generator polynomials

Constraint
lengths

P1-P2

NP1-NP2

NP1-P2

P1-NP2

K1=5

G1=[23,35]

G1=[37,21]

G1=[37,21]

G1=[23,35]

K2=5

G2=[23,35]

G2=[37,21]

G2=[23,35]

G2=[37,21]

2) Type-II of ATC:
Type-II of ATC consist of two non-identical component
codes with the different constraint lengths and mixed types of5generator polynomials. On the one hand, the constraint lengthof the constituent encoder for turbo codes plays an importantrole in determining the performance and decoding
computational complexity. Free distance is shorter for turbo
codes with shorter constraint length, it can cause poorer
error-floor performance. Turbo codes with longer constraint
length can achieve longer free distance and better performance,but
thecomputational
complexity
will
increasecorrespondingly. In order to obtain good performance andreduce decoding computational complexity, it is necessary to
decrease the constraint lengths of one constituent encoder of
STC. And then, type-II of ATC can be constructed by using
two component codes with different constraint lengths. On theother hand, the types of generator polynomials of component
encoder are a key factor for ATC design. The primitive
feedback generator polynomials for component code are
known to be able to enlarge the free distance to improve error
floor at high SNR, but at a cost of degrading water-fall at low
SNR. On the contrary, the non-primitive generator
polynomials would optimize the distance spectrum to enhancewater-fall performance at low SNR, but reduce error-floor athigh SNR. For this reason, type-II of ATC can also beconstructed by using two component codes with mixed typesof the primitive and non-primitive generator polynomials.
The constraint length K=5 of one of the constituent
encoders of conventional STC in [17] is reduced to 3 and 4,
primitive and non-primitive polynomial are chosen as
feedback generator polynomials of two constituent codes.
According to Table 1, Our proposed sixteen types-II of ATCwith the different constraint lengths and mixed types of thegenerator polynomials are listed in Table III.
TABLE III
Types-II of ATC with different constraint lengths and mixed types ofgenerator polynomials
Generator polynomials

Constraint
lengths

P1-P2

NP1-NP2

NP1-P2

P1-NP2

K1=3
K2=5
K1=5
K2=3
K1=4
K2=5
K1=5
K2=4

G1=[ 7, 5]
G2=[23,35]
G1=[23,35]
G2=[ 7, 5]
G1=[13,17]G2=[23,35]
G1=[23,35]
G2=[13,17]

G1=[ 5, 5]
G2=[37,21]
G1=[37,21]
G2=[ 5, 5]
G1=[15,17]
G2=[37,21]
G1=[37,21]
G2=[15,17]

G1=[ 5, 5]
G2=[23,35]
G1=[37,21]
G2=[ 7, 5]
G1=[15,17]
G2=[23,35]
G1=[37,21]
G2=[13,17]

G1=[ 7, 5]
G2=[37,21]
G1=[23,35]
G2=[ 5, 5]
G1=[13,17]G2=[37,21]
G1=[23,35]
G2=[15,17]

C. Simulation and Analysis of ATC
For all simulations of two types of ATC, the information
size is set to 378 bits, the constraint lengths K are 3, 4 and 5,
maximum number of iteration is 8, one-half code rate,
URCCC interleaver, SISO decoder, log-MAP decoding
algorithm, BPSK modulation, AWGN channel.
Figure 6 gives the BER versus Eb/N0 for the type-I of ATCwith the same constraint lengths and mixed types of generator
polynomials. Here, we take K1=K2=5. It was observed fromthe simulation results in Figure 6:

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

length of RSC2 component code is a good scheme when thistype-II of ATC with the different constraint lengths areconstructed.
010

K1=3,K2=5
K1=4,K2=5
K1=5,K2=3
K1=5,K2=4
K1=5,K2=5

-2

10

BER

(1) The error-floor performance of P1-P2-STC is good athigh SNR, but its water-fall is poor at low SNR. On thecontrary, the water-fall of NP1-NP2-STC is superior at low
SNR, but its error-floor is inferior at high SNR.
(2) At low SNR, the water-fall of NP1-P2-ATC and
P1-NP2-ATC is superior to P1-P2-STC but inferior to
NP1-NP2-STC. However, the error-floor of NP1-P2-ATC and
P1-NP2-ATC is inferior to P1-P2-STC but superior to
NP1-NP2-STC at high SNR.
These simulation results effectively indicate that the type-Iof ATC is optimal trade-off scheme over the entire range of
SNR.

6

-4

10

0

10

-6

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10

BER

10

P1-NP2-ATC
NP1-P2-ATC
NP1-NP2-STC
P1-P2-STC

-8

10

0

0.5

1

1.5

2
Eb/No

2.5

3

3.5

4

-4

10

Fig. 7. BER versus Eb/N0 for type-II of P1-P2-ATC with the different
constraint lengths and the same primitive generator polynomials-6

The BER versus Eb/N0 for the type-II of ATC with thedifferent constraint lengths and mixed types of generator
polynomials is shown in Figure 8. Here, we select K1=5,
K2=4.

10

-8

10

0

0.5

1

1.5

2
Eb/No

2.5

3

3.5

4
0

10

P1-NP2-ATC
NP1-P2-ATC
NP1-NP2-ATC
P1-P2-ATC

Fig. 6. BER versus Eb/N0 for type-I of ATC with the same constraint
lengths K1=K2=5 and mixed types of generator polynomials
-2

10

BER

Figure 7 shows the BER versus Eb/N0 for the type-II of
P1-P2-ATC with the different combination of the constraint
lengths K=3, 4, 5 and the same primitive generator
polynomials. For the ease of comparison, the BER versus
Eb/N0 for the P1-P2-STC with K1=K2=5 and primitive
generator polynomials is also shown in Figure 7, thesimulation analysis and conclusions are as follow:(1) By decreasing the constraint lengths of one constituent
encoder of STC, the water-fall and error-floor performances oftype-II of ATC are all become deteriorated. It is caused by thesmaller free distance of the component codes with short
constraint lengths. However, the computational complexity of
decoding algorithm can be reduced for the type-II of ATC withshort constraint lengths. It is demonstrated that type-II of ATC
can get a favorable trade-off between the performance andcomputational complexity.
(2) The performance of ATC with K1=4, K2=5 or K1=5,
K2=4 is slightly inferior to STC with K1=K2=5. Especially,
the performance of ATC with K1=5, K2=4 is nearly close toconventional turbo codes with negligible performancedegradation but with a reduced computational complexity.
(3) The ATC with K1=5, K2=4 is superior to one with K1=4,
K2=5. Similarly, the ATC with K1=5, K2=3 is superior to thatwith K1=3, K2=5. It is evident that reducing the constraint

-4

10

-6

10

-8

10

0

0.5

1

1.5

2
Eb/No

2.5

3

3.5

4

Fig. 8. BER versus Eb/N0 for ATC with the different constraint lengths
and mixed types of generator polynomials

Our analysis and conclusions from figure 8 are as follow:(1) At low SNR (Eb/N0 ≤ 1.75dB), the performance of
NP1-NP2-ATC is the best, NP1-P2-ATC better, P1-NP2-ATCinferior, P1-P2-ATC poor. But at high SNR (Eb/N0 ≥ 1.75dB),
the results are in reverse order. The reason is that the
non-primitive generator polynomials can optimize the distancespectrum to improve water-fall performance of turbo codes
and the primitive polynomials enlarge the free distance toreduce error-floor.

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012(2)
Considering
theentirerangeof
SNR(0dB ≤ Eb/N0 ≤ 4dB), NP1-P2-ATC and P1-NP2-ATC are thebetter trade-off between the water-fall and the error-floor
performance. Evidently, NP1-P2-ATC and P1-NP2-ATC are
superior to P1-P2-ATC and NP1-NP2-ATC turbo codes overthe entire range of SNR.
Therefore, it is a good choice that one constituent encoder iscommonly constructed by using a primitive generator
polynomial and another using non-primitive polynomial when
this type-II of ATC are designed.
IV. JOINT SOURCE-CHANNEL CODING WITH UEP
USING ATC7

switch S1, S2, S3. If the CSI is good, i.e. high SNR, then theencoder chooses P1-P2-STC or P1-P2-ATC because of theirbest error-floor performance. If the CSI is slightly inferior, i.e.
medium SNR, then chooses NP1-P2-ATC or P1-NP2-ATC
because of their best trade-off between water-fall anderror-floor performance. If the CSI is worse, i.e. low SNR,
then chooses NP1-NP2-STC because of their best water-fallperformance.
The presented UEP scheme both considers conditions of
CSI from channel estimator and the bits error effect of thedifferent importance levels of image bit streams, it can achieve
the better trade-off among the reliability, the time delay andthe computational complexity.

A. UEP Scheme using ATC
In digital multimedia communication system, the sensitivityof the image or video bit streams to channel errors is generally
not uniform, the reconstructed image or video is insensitive toerrors affected by insignificant bit streams (e.g. highfrequency components of image), while it is rather sensitive toerrors caused by significant bits (e.g. low frequency
components of image), i.e. the sensitivity of the significant
bits to errors is far greater than the insignificant bits. E.g.
eight bits quantized gray-scale image, the priority of eight bits
for per pixel is different, the sensitivity of the Most Significant
Bits (MSB) to errors is greater than the Least Significant Bits
(LSB). Here, we propose an UEP scheme for imagetransmission by using the ATC. We classify source data into
several classes and use UEP encoder to strongly protect theimportant classes bit streams and weakly protect the
non-important classes. The diagram of UEP encoder is shownin Figure 9.
According to the different importance levels, image data
streams are divided into three blocks and then hierarchicallycoded by UEP encoder. Specific processes are as follows:(1) Image bit streams X are partitioned into three groups sothat the important bits can obtain strong protection. The firstgroup consists of three MSB bits for per pixel, it is so called
most significant bit streams X1. The second group iscomposed by two middle bits, named significant bit streams
X2. The third group is comprised of three LSB bits, denoted as
insignificant bit streams X3.
(2) In terms of different importance classes of image bit
streams, the UEP encoder adaptively adopts different types ofturbo codes. For X1, ATC or STC with constraint lengths
K1=5 and K2=5 are chosen because they have the best
performance to protect X1 strongly. For X2, ATC withconstraint lengths K1=5 and K2=3 are selected due to theirrelative low complexity and moderate error correction ability.
For X3, ATC or STC with constraint lengths K1=3 and K2=3
are chosen because of their most low complexity and lower
time delay.
(3) On the conditions of CSI from channel estimator, theUEP encoder adaptively adjusts coding strategies through theFig. 9. UEP encoder using ATC

B. JSCC with UEP using ATC
With above mentioned UEP design using ATC, we present
an effective JSCC scheme. The block diagram of the JSCC isshown in Figure 10.
Original image is firstly decomposed by discrete wavelet
transformation (DWT) and compressed by SPIHT encoder [19][20] [21]. As the importance levels of the compressed bit
streams X trend towards decreasing progressively, i.e. thesignificant bit streams locate the nearer front and theinsignificant bit streams are behind, we would partition X into
three classes block, X1, X2 and X3. Then they are encodedwith UEP encoder hierarchically so that the significant
information can obtain strong protection. After UEP, the coded
bit streams are combined to Y and modulated, then transmitted
into the noisy channel. In receiver end, the received data

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

streams are correspondingly demodulated, UEP decoded,
SPIHT decoded, inverse DWT (IDWT) and the image would
be reconstructed.

8

the image. Quality measure of the reconstructed images isgiven by the PSNR, where
I2(5)
PSNR = 10 log10 ( max )MSEand I max = 255
is the maximum pixel value for thegray-scale image when the initial pixel value is represented by
8 BPP, MSE
denotes mean square error between thereconstructed image and the original image, here
M −1 N −1∑∑ [ I (i, j ) − I ′(i, j )]

2

MSE =

Fig. 10. Block diagram of proposed JSCC scheme based on UEP using
ATC

To received data streams, the UEP decoder can be adapted
to adopt various decoding algorithms and appropriate
decoding iterative numbers for the different importance levelsof image data streams.
(1) For received most significant data streams, UEP decoder
chooses Log-MAP algorithm because of its good error
correction ability. Meanwhile, properly increases decoding
iterative numbers to strongly protect most significant data.
(2) For significant data, UEP decoder adopts low
complicated Max-Log-MAP algorithm and moderate of theiterative numbers to reduce decoding complexity.
(3) For insignificant streams, selects lowest complexity
SOVA algorithm and suitably reduces iteration numbers tofurther reduce computational complexity and decoding timedelay.
Rate allocation in our JSCC scheme is an important part, it
can optimally allocate the source CR and the channel code
rates according to the calculated PSNR value of thereconstructed image and the estimated CSI condition while
fixing bandwidth resource.
(1) If the calculated PSNR value is small or SNR is low,then properly increase SPIHT code rate, i.e. CR or BPP topreserve more image details. Meanwhile, select turbo coeds
puncture mode to get low code rate so as to decrease thewhole UEP code rate to protect image data strongly.
(2) If the PSNR is big or SNR is high, then timely reduce
CR or BPP ratios to save storage size and increase UEP code
rate to save channel bandwidth resource.
V. SIMULATION RESULTS AND ANALYSISThe proposed JSCC scheme is applied to digital image
communication system. Experiments are performed on
128×128 gray-scale image Lena, using 5-level wavelet
decomposition based on the 9/7 tap filters, the reliability andeffectiveness of the JSCC scheme was evaluated. The measureof compressed image is given by the compression ratios (CR)and the bit per pixel (BPP) ratios. CR indicates that thecompressed image is stored using CR % of the initial storage
size while BPP is the number of bits used to store one pixel of

i =0 j =0(6)
MN
M and N are the number of rows and columns of theimages, I and I ′ are pixel value of the original image andthe reconstructed image, respectively, and 0 ≤ i ≤ M − 1 ，
0 ≤ j ≤ N −1 .
In the transmitter, if SNR is 0-1dB, i.e. worst CSI, X1
adopts NP1-NP2-STC with K1=K2=5, X2 adopts
NP1-NP2-ATC with K1=5, K2=3, X3 adopts NP1-NP2-STC
with K1=K2=3. If SNR is 1-2dB, i.e. medium CSI, X1 adopts
NP1-P2-ATC with K1=K2=5, X2 adopts NP1-P2-ATC with
K1=5, K2=3, X3 adopts NP1-P2-ATC with K1=K2=3. And if
SNR is 2-3dB, i.e. good CSI, X1 adopts P1-P2-STC with
K1=K2=5, X2 adopts P1-P2-ATC with K1=5, K2=3, X3
adopts P1-P2-STC with K1=K2=3.
In the receiver, for received most significant data, thedecoder chooses Log-MAP algorithm and 5 iterative. For
significant data, chooses Max-Log-MAP algorithm and 4
iteration numbers. For insignificant data, selects SOVA
algorithm and 3 iteration numbers. For comparison, two equal
error protection (EEP) schemes using STC are also performed.
In EEP, three classes bit streams all adopt NP1-NP2-STC with
K=5 and 4 iteration decoding, while the decoder selects
Max-Log-MAP algorithm for EEP 1 and SOVA for EEP2.
The simulation results for PSNR of JSCC with UEP and
EEP in various source BPP ratios or CR and channel SNR
values are listed in Table IV. The reconstructed images for
JSCC with UEP and EEP in source ratio=0.5 BPP, channel
SNR=1.5dB are shown in Figure 11.
From Table 4, it is obvious that the PSNR of reconstructed
image for JSCC scheme with UEP using ATC is superior to
EEP1 and EEP2 because the significant bit streams arestrongly protected in UEP. We can see that PSNR will bebetter in relative higher BPP ratios with the same SNR
condition, i.e. the reliability is improved, but the CR is also
higher in this case, i.e. the compression effectiveness isdeteriorated. We can also see that PSNR become worse inlower SNR with the same BPP ratios. To achieve the bestcompromise between a low CR and a good perceptual result
and to adapt various channels, our JSSC scheme can optimally
adjust the source BPP ratios (or CR) and channel code rates
according to the calculated PSNR of the reconstructed image
and the estimated SNR value.
It can be seen from Figure 11 that the reconstructed image
qualities for JSCC with UEP using ATC are obviously
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

improved and superior to EEP1 and EEP2.
Whether objective PSNR evaluation criterion or subjective
visual perceptual result, the proposed JSCC scheme iseffective and feasible.
TABLE IVPSNR (dB) of JSCC with UEP and EEP in various source ratios (BPP)
and channel SNR (dB)
Source
Ratio
(BPP)

CR
(%)

0.25

3.13

0.5

0.75

6.16

9.38

ChannelSNR(dB)

PSNR (dB)
UEP

EEP1

EEP2

0.50

24.51

19.87

18.05

1.50

28.84

24.65

22.32

2.50

31.62

28.02

25.88

0.50

26.79

22.11

20.33

1.50

31.10

27.38

25.40

2.50

34.55

31.25

29.28

0.50

29.13

25.21

22.73

1.50

33.22

29.80

27.69

2.50

36.81

33.65

31.44

Fig. 11 (a)Fig. 11 (b)Fig. 11 (c)Fig. 11. Reconstructed images with source ratio=0.5BPP and channel
SNR=1.5dB (a) UEP (b) EEP1 (c) EEP2

9VI.CONCLUSIONSThis paper presents an efficient JSCC design based on UEP
using ATC, which can adaptively adopt different coding
strategies, different interleavers of turbo codes, various
decoding algorithms and appropriate decoding iterative
numbers according to the different significant levels of image
data streams and the varying conditions of estimated channel
CSI. This scheme can also dynamically allocate the source
ratios and channel code rates according to the calculated
PSNR of reconstructed images and the estimated CSI. The
proposed JSCC scheme can not only evidently improve thereconstructed image qualities but also enhance the reliabilityof the communication system with no additional bandwidth,
our scheme is more adaptive and robust.
REFERENCES[1] G. Cheung and A. Zakhor, “Joint source-channel coding of scalable video
over noisy channels,” in IEEE Int. Conf. Image Process., vol.3, pp.
767-770, Sep. 1996.
[2] R. Hamzaoui, V. Stankovic and Z.Xiong; "Optimized error protection ofscalable image bit streams [advances in joint source-channel coding forimages]," IEEE Signal Process., vol. 22, no. 6, pp. 91-107, Nov. 2005.
[3] L.Yao and L. Cao, "Turbo Codes-Based Image Transmission for
Channels With Multiple Types of Distortion," IEEE Trans. Signal
Process., vol. 17, no. 11, pp. 2112-2121, Nov. 2008.
[4] C. Lan, T. Chu, K. R. Narayanan and Z. Xiong, “Scalable image and video
transmission using irregular repeat–accumulate codes with fast algorithm
for optimal unequal error protection,” IEEE Trans. Commun., vol. 52, no.
7, pp. 1092–1101, Jul. 2004.
[5] P. G. Sherwood and K. Zeger, “Progressive image coding for noisy
channels,” IEEE Signal Process. Lett., vol. 4, no. 7, pp. 189-191, Jul. 1997.
[6] Lei Cao, “On the unequal error protection for progressive imagetransmission,” IEEE Trans. Image Process., vol. 16, no. 9, pp. 2384-2388,
Sept. 2007.
[7] G. Caire and G. Lechner, “Turbo codes with unequal error protection,”
IET Electron.. Lett., vol. 32, no. 7, pp. 629-631, Feb. 1996.
[8] N. Thomos, N. Boulgouris and M. Strintzis, “Wireless image transmission
using Turbo codes and optimal unequal error protection,” IEEE Trans.
Image Process., vol. 14, no. 11, pp. 1890-1901, Nov. 2005.
[9] M. Aydinlik and M. Salehi, “Turbo coded modulation for unequal error
protection,” IEEE Trans. Commun., vol. 56, no. 4, pp. 555-564, Apr. 2008.
[10] Y.Takeshita, M.Collins and P.Massey, “A note on asymmetric Turbo
codes,” IEEE Commun. Lett., vol. 3, no. 3, pp. 69-71, Mar. 1999.
[11] B.Shim, S.Choi, H.Park, S.Kim and Y.Ra, “A study on performance
evaluation of the asymmetric Turbo codes,” in IEEE Proc. ICHIT' 08, pp.
667-671, Aug. 2008.
[12] K.Ramasamy, B.Balakrishnan and M.Siddiqi, “A new class ofasymmetric turbo code for 3G systems,” Int. J. Electron. Commun., pp.
447-458, Jun. 2006.
[13] H.X.Wang and S.F.Liu, "A Novel Interleaver Design for Turbo Codes," J.
South-Central University for Nationalities, vol. 29, no. 3, pp.58-60, Sep.
2010.
[14] M.Salim and S.Shrimal, “Modified interleaver design to reduce error
floor in turbo codes for wireless communication,” in IEEE Proc. AMTA'
08, pp. 698-701, Nov. 2008.
[15] S.Park and J.Jeon, “Interleaver optimization of convolutional turbo codefor 802.16 systems,” IEEE Commun. Lett., vol. 13, no. 5, pp. 339-341,
May. 2009.
[16] J.Costello, J.Hagenauer, H.Imai and B.Wicker, “Applications oferror-control coding,” IEEE Trans. Inform. Theory, vol. 44, no. 6, pp.
2531-2560, Jul. 1998.
[17] C.Berrou, A.Glavieux and P.Thitimajshima, “Near Shannon limit
error-correcting coding and decoding: Turbo codes,” in IEEE Proc. ICC'
93, pp. 1064-1070, May. 1993.
[18] J.Hagenauer, E.Offer and L.Papke, “Iterative decoding of binary block

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 201210

and convolutional codes,” IEEE Trans. Inform. Theory, vol. 42, no. 2, pp.
429-445, Mar. 1996.
[19] C.D.Creusere, “A new method of robust image compression based on theembedded zero-tree wavelet algorithm,” IEEE Trans. Image Process., vol.
6, no. 10, pp.1436-1442, Oct. 1997.
[20] P. Sherwood and K. Zeger, “Progressive image coding for noisy
channels,” IEEE Signal Process. Lett., vol. 4, pp. 189-191, Jul. 1997.
[21] A. Said and W. A. Pearlman, “A new fast and efficient image codec basedon set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video
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Hanxin Wang, South-Central University for
Nationalities, Wuhan, China.
Hanxin Wang received the B.S. degree in electronics
and information engineering from Wuhan University,
China in 1989, and finished the M.S. degree course inelectronics and information engineering fromSouth-Central University for Nationalities, China in
2002. Since 1989, he was a network engineer in
Hua-zhong Computer System Engineering Company,
China. During 2002-2003, as an invited visitor, he studied on wideband
wireless communication in Institute of Information and Communication,
Chonbuk National University, Korea. Since 2003, he was an associate
professor in College of Electronics and Information Engineering,South-Central University for Nationalities, China. His research interests
include information theory and modern coding theory, wideband wireless andmobile communication, cognitive radio network.
Cuitao Zhu, received the M.S. degree and the Ph.D.
degree in communication and information system from
Huazhong University of Science & Technology in 1999
and 2008, respectively. He is currently a professor inCollege of Electronics and Information Engineering,South-Central University for Nationalities, China. His
research interests include wideband wirelesscommunication, cognitive radio, compressed sensing.

Chengyi Xiong, received the M.S. degree incommunication and information system in 2000 fromSouth-Central University for Nationalities, and the
Ph.D. degree from Huazhong University of Science &Technology in 2006. He is currently a professor inCollege of Electronics and Information Engineering,South-Central University for Nationalities, China. His
research interests include signal processing,compressed coding of image and video, compressedsensing.
Shaoping Chen, received the M.S. degree incommunication and information system in 1990 from
Wuhan University, and the Ph.D. degree from
Huazhong University of Science & Technology in2004. He is currently a professor in College ofElectronics
andInformation
Engineering,South-Central University for Nationalities, China. His
research interests include high speed wirelesscommunication, digital signal processing, MIMO
system, OFDM technique.

Copyright ⓒ 2012 GiRI(Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 201211

Efficient and Format-Compliant Video Encryption
Algorithm in Compressed Domain for H.264/AVC
Li ZHUO, Niansheng MAO, Haojie SHEN, Jing ZHANG, Xiaoguang LI
Signal and Information Processing Laboratory, Beijing University of Technology, Beijing, China
zhuoli@bjut.edu.cn, mns150@sohu.com, 13810761814 @139.com, zhj@bjut.edu.cn, lxg@bjut.edu.cn
Abstract—In this paper, an efficient video encryption scheme
is proposed for protecting H.264 bitstream. The issues on thecompressed domain video encryption have been pointed out andfully addressed. In the proposed scheme, only the mostsignificant bits for video reconstruction in H.264 bitstream are
extracted and encrypted, to optimize the trade-off between
security level and computational complexity. For intra-frames,
only the codewords of intra4×4 prediction mode and the sign
bits of the low frequency DCT coefficient are encrypted. Forinter-frames, the info_suffix of motion vector difference (MVD)
are encrypted. Owing to the proposed scheme is independent of
the compression process, thus does not need to modify the
structure of H.264 standard codec. Experimental results showthat the proposed scheme exhibits significant computational
efficiency and reliable security, can resist not only perceptual
attacks but also brute-force attacks. Furthermore, it adds alittle memory overhead. Therefore, the proposed scheme will bewell suited for real-time video applications and resource-limited
systems such as smartphone and wireless sensor network.
Index Terms—Video encryption, Security, H.264, Bitstream,
Compressed domain

I. INTRODUCTION
With the rapid development of computer and network
technology, multimedia systems such as videophone, video
surveillance and telemedicine, have been widely used. The
security and privacy of multimedia content are becoming
more and more prominent. Conventional cryptographic
algorithms such as data encryption standard (DES) [1] andadvanced encryption standard (AES) [2] are difficult to be

Manuscript received May 3, 2012. This work was sponsored by the
Program for New Century Excellent Talents in University, the Excellent
Science Program for the Returned Overseas Chinese Scholars of Ministryof Human Resources and Social Security of China, Scientific Research
Foundation for the Returned Overseas Chinese Scholars of MOE.
Li ZHUO is currently a professor of the Beijing University ofTechnology, Beijing, China (email: zhuoli@bjut.edu.cn)
Niansheng MAO was a master student of the Beijing University ofTechnology, Beijing, China (email: mns150@sohu.com)
Haojie SHEN is currently a master student of the Beijing University ofTechnology, Beijing, China (email: 13810761814 @139.com)
Jing ZHANG is currently an associate professor of the Beijing
University of Technology, Beijing, China (email: zhj@bjut.edu.cn)
Xiaoguang LI is currently an associate professor of the Beijing
University of Technology, Beijing, China (email: lxg@bjut.edu.cn)

applied directly to multimedia content duo to the large
volume of data and real-time video requirements.
Furthermore, in the case of the wireless mobile terminals,
limited processing power, memory and bandwidth always
fail to meet the encryption processing overhead. Thus,
efficient video encryption schemes need to be designed.
In real-world applications, a video encryption scheme
should take various requirements into account, such as
security, computational efficiency, compression efficiency,
format-compliance and so forth. Different video applications
require variable levels of security. For example, for Videoon Demand (VoD) or pay-TV, low security is often required,and even nonpaying users are allowed to access low quality
versions to promote them to buy high quality versions,whereas for military secrets or financial information, strict
security is demanded to completely prevent the unauthorizedaccess. The computational efficiency means that theencryption or decryption process can not cause too muchtime delay, to meet the requirements of real-time
applications. Video compression is employed to reduce thestorage space and save bandwidth, so that the encryption
process should have a least impact on the compression
efficiency. The format-compliance, also known as syntaxcompliance [3], means that the encryption scheme should do
not change the syntax structure of the compressed bitstream,thus ensures features like cutting, copying, adding or
removing, and ability of the encrypted bitstream still can be
decoded by a standard decoder.
In recent years, many video encryption algorithms havebeen proposed. As pointed out in [4], these algorithms
according to their association with video compression can beclassified into two categories, called compression-joint
encryption algorithms and compression-independentencryption algorithms. For the former, the encryption
algorithms are embedded in a certain step of thecompression process. For example, some algorithms
permute or scramble the residual coefficients after the
Discrete Cosine Transform (DCT) [5]-[7], some algorithms
encrypt the signs of DCT coefficients or motion vector
difference (MVD) after quantization [8], [9], and somealgorithms selectively encrypt intra-prediction modes, DCT
coefficients, and MVD during the entropy coding [10]-[12].

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

As these encryption algorithms are all accomplished before
the last step of the video compression process, all theencrypted bitstreams can be decoded by a standard decoder
without being decrypted, while only obtain the unintelligible
video. However, encrypting or scrambling the DCT
coefficients during the compression process usually destroys
the inherent energy impact capability of the DCT transform,
resulting in low compression efficiency.
Differently, for the compression-independent encryption
algorithms, the compression and encryption process arecarried out separately. These algorithms often directly
encrypt the compressed bitstream, also known ascompressed domain video encryption. In [13], the odd
indexed bytes in video bitstream are firstly encrypted with aconventional cryptographic algorithm, and used as keys to
XOR with the even indexed bytes. In [14], the bitstream
according to their importance for decoding are divided into
five types, and the first three are encrypted whereas others
remain. Both of the above algorithms can ensure enoughsecurity, but are all of low computational efficiency and loss
the format-compliance. In [3], the codewords of DCT
coefficients and MVD in compressed bitstream are shuffled.
In [15], the codewords of intra-prediction mode are
encrypted. Both of them demonstrate high computational
efficiency and maintain the format-compliance, but are of
low security. As can be seen, the various existing
compressed domain encryption schemes cannot optimize thetrade-off between security level and computational
complexity, and are often difficult to maintain the formatcompliance. Therefore, the compressed domain video
encryption algorithms need to be further studied.
In this paper, we propose an efficient compressed domain
video encryption scheme for protecting H.264 bitstream.
The proposed scheme directly extracts the most significant
bits for video reconstruction in H.264 bitstream,
concatenates them in an appropriate way to form a subbitstream, and then encrypts the sub-bitstream with aconventional cryptographic algorithm such as AES. Afterthe encryption process, the encrypted bits are put back into
their original positions.
The rest of this paper is organized as follows. Section II
analyses the H.264 bitstream syntax structure. Details of the
proposed video encryption scheme are described in Section
III. The performance of the proposed scheme is discussed in
Section IV. Section V presents some conclusions.
II. H.264 BITSTREAM SYNTAX STRUCTURE
H.264/AVC [16] is the state-of-the-art video coding
standard. Compared with the previous standards such as
MPEG-2, H.263, it not only has excellent compression
performance, but also has a “network-friendly” bitstream
structure. In general, the basic unit of the H.264 bitstream isvariable length code (VLC) codewords and fixed lengthcode (FLC) codewords, which are formed by a number ofbits and represent different information types. These12

codewords play different roles in the decoding process. For
example, the codewords of the header include
synchronization information, and the codewords of MVD
contain video motion information and so forth. In order toimprove the encryption scheme pertinence and efficiency,
the structure of the H.264 bitstream will be firstly analysedin this section.
To achieve higher compression efficiency, the H.264
bitstream is organized with a hierarchical structure, as shownin Figure 1. The H.264 bitstream can be divided into a seriesof Network Abstraction Layer (NAL) units. Each NAL unit
contains NAL header information and a Raw Byte Sequence
Payload (RBSP), which can be Sequence Parameter Set(SPS), Picture Parameter Set (PPS) or a coded slice. Amongthem, the SPS contains SPS_id, profile and level, thenumber of reference frames, image width and height, and soon. The PPS contains PPS_id, SPS_id, entropy coding mode,
reference frame index, the initial quantization parameter(QP), and so on. The SPS and PPS do not correspond to aparticular sequence or image, in other words, an SPS can beused for multiple sequences and a PPS can also be used for
multiple images. The coded slice consists of slice header(including slice type, PPS_id and QP offset) and a numberof macroblock (MB) data.
Start code

NAL Unit

NALU： NAL Header

RBSP

Slice Layer： Slice Header

Slice Data

MB Layer：

MB Data

MB Data

MB Data： mb_type mb_predmode CBP………

MB Data

DCT Coefficients

MVD

Fig. 1. H.264 bitstream hierarchical structure.

For video encryption, the SPS, PPS and slice header only
provide nominal security, as these coding parameters do notcontain too much information and usually have a fixed
format. The MB data can be classed into intra-macroblock
data (including MB_type, intra-prediction mode, coded
block pattern (CBP) and DCT coefficients) and intermacroblock data (including MB_type, inter-prediction mode,
CBP, MVD and DCT coefficients). Among them, the DCT
coefficients contain video texture information, the MVD
contain video dynamic information, and the intra-prediction
mode indicates the predicted direction. All these information
are the most important for video reconstruction. Therefore,
in order to obtain high security, the codewords of intraprediction mode, DCT coefficient, and MVD in H.264
bitstream should be encrypted.
III. THE PROPOSED ENCRYPTION SCHEME

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

Based on the above analysis, an efficient compressed
domain encryption scheme is proposed in this section. For
intra-prediction mode, the codewords are encrypted withIPME algorithm [15], for the low frequency DCT
coefficients of intra-frames, only the sign bits are extracted
and encrypted, and for the motion vector difference, theinfo_suffix of the codewords are encrypted. The proposed
scheme is shown in Figure 2.

SubStream

Encrypted
SubStream

Bit Resetting

MVDs

Bit Extraction

Stream Analysis

H.264
StreamIntra FrameDCT Coefficients

Intra Prediction
Mode'

AES

Intra FrameDCT Coefficients'
MVDs'

Other Parameters

Stream Recombination

Intra Prediction
Mode

Encrypted
StreamFig. 2. Diagram of the proposed encryption scheme.

A. Intra-Prediction Mode Encryption
In H.264 bitstream, the intra4×4 prediction mode isdenoted by two syntax elements, prev_intra4×4_pred_mode
with 1bit and rem_intra4×4_pred_mode with 3 bits. If theprev_intra4×4_pred_mode is set to ‘1’, the current blockuses the most_probable_mode, which is the minimum of theprediction modes of its two neighboring upper and left
blocks, and the rem_intra4×4_pred_mode is not adopted.
Otherwise, the prev_intra4×4_pred_mode is set to ‘0’, andthe prediction mode of the current block is presented by therem_intra4×4_pred_mode.
In IPME algorithm [15], only the codewords of therem_intra4×4_pred_mode in H.264 bitstream are encrypted.
The algorithm is simple and computationally efficient, butlacks of security due to the limited encryption space. In the
improved algorithm [17], in order to obtain higher security,
when the prev_intra4×4_pred_mode is set to ‘1’, theencryption operation is to reset it to ‘0’ and inserts 3 bits
chaotic sequence as the rem_intra4×4_pred_mode, and all
the intra4×4 prediction modes are encrypted by chaotic
pseudo random sequence. However, the improved algorithm
significantly increases the computational complexity, andbears a large amount of processing overhead and memory
requirements. Thus, the codewords of intra4×4 prediction
modes are encrypted with IPME in the proposed scheme.
Differently, the intra16×16 prediction modes are jointly
encoded with the luma and chroma CBP using the unsigned
Exp-Golomb entropy coding. The CBP indicates whichblocks within a macroblock contain DCT coefficients, sothat its values should not be changed during encryption,otherwise the encrypted bitstream will loss the formatcompliance. Thus, the proposed scheme does not encrypt the
intra16×16 codewords.
B. DCT Coefficients Encryption

13

The DCT coefficients in H.264 baseline profile are
encoded with the context-based adaptive variable length
coding (CAVLC). The encoding process can be described asfollows [16]: Encoding the number of coefficients and trailing ones
(coeff_token), Encoding the sign of each trailing ones,
 Encoding the levels of the remaining non-zerocoefficients,
 Encoding the total number of zeros before the lastcoefficient, and Encoding each run of zeros.
After the CAVLC process, the residual data is represented
by numerous coding parameters such as the number ofnonzero coefficients and trailing ones (coeff_token), the signof trailing ones (TrailingOnes), the remaining nonzero
coefficients (NonCoeff), the total number of zeros before thelast coefficient (TotalZeros) and each run of zeros(run_before). Since it is context adaptive, in order tomaintain the format-compliance, the context adaptive
property should not be destroyed during the encryption. In
other words, the codewords of coeff_token, TotalZeros andrun_before should not be changed. More specifically, only
the sign bits of NonCoeff codeword and the codewords ofTrailingOnes can be encrypted. For optimize the tradeoff
between security and computational complexity, only thesign bits of the low frequency DCT coefficients of intraframes are encrypted in the proposed scheme.
C. MVD Encryption
In H.264, each MVD is independently coded by thesigned Exp-Golomb entropy coding. It means that the MVD
codewords in H.264 bitstream are mutually independent.
Furthermore, each MVD codeword in H.264 bitstream isconstructed as [M Zeros][1][INFO], where INFO is a M-bit
suffix information called info_suffix. Here, the MVD levelis X  2M  INFO  1 and the last one bit of the info_suffix
is the MVD sign. Therefore, the entire info_suffix of the
MVD codewords should be extracted and encrypted.
IV. PERFORMANCE ANALYSIS
In our experiments, a variety of standard video sequencesin CIF and QCIF format, such as “Akiyo”, “Foreman”,“Mobile”, “Football”, “Tempete” and “Silent” are applied todemonstrate the performance of the proposed encryption
scheme. Each video sequence contains 150 frames. These
videos are all encoded by JM86 with a frame rate 15Hz, andthe intra-frame period is set as 15. The performance of the
proposed scheme, such as security, computational
complexity and memory requirement, are analysed as follow.
A. Security
For video encryption, the security requires not only
cryptographic security but also perceptual security. The
former one specifically deals with the security against

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

cryptographic attacks, for example, brute-force attacks. The
perceptual security means that, whether or not the perceptual
attacks such as the error-concealment-based attacks and thereplacement attacks are used, the encrypted video remain
appears unintelligible to a viewer without being decrypted.
1) Perceptual security
As we know, the most significant bits for video
reconstruction in H.264 bitstream are encrypted in the
proposed scheme. That is to say, the proposed scheme will
make it difficult to recognize the encrypted videos. Figure 3
shows the encrypted results of several videos. It is obviousthat all the encrypted videos appear unrecognizable. Besides,
the quality of the encrypted videos is measured with thePeak Signal-to-Noise Ratio (PSNR) and the Structural
Similarity (SSIM) respectively. As can be seen from Table 1,for the encrypted videos, the PSNR value is all about 10 dB
and the SSIM value is less than 0.3, both of them are much
lower compared with the corresponding original videos.
Thus, the proposed scheme can achieve a high perceptual
security.

Size

QCIF

CIF

TABLE IThe encrypted videos quality
PSNR-Y(dB)
SSIM
Video
Original Encrypt
Original
Encrypt
Akiyo
38.54
11.25
0.969
0.273
Carphone
37.24
8.65
0.965
0.238
Hall
37.43
9.38
0.971
0.208
Silent
36.01
7.64
0.947
0.224
Foreman
36.82
7.81
0.936
0.257
Football
36.53
12.77
0.933
0.282
Harbour
34.41
9.99
0.962
0.057
Tempete
34.67
9.01
0.961
0.094
City
34.94
10.51
0.931
0.159

14

2) Perceptual attacks
The error-concealment-based attack means that theattackers usually treat the encrypted data as bit-error or
packet-loss, and then try to minimize the impact on video
reconstruction as a result of the encryption by using various
error-concealment techniques. However, it is very difficult
for an attacker to identify the encrypted parts from a formatcompliant encrypted bitstream and therefore the errorconcealment-based attacks become invalid. The replacement
attack is to attempt to recover the encrypted information andmake it more visually acceptable by replacing the encrypted
data with some particular data. For example, the encrypted
intra-prediction modes can be replaced by themost_probable_mode (the minimum of the prediction modesof its neighbouring blocks), since the adjacent blocks often
have the same intra-prediction modes. Figure 4 shows therecovered frames with replacement attacks. As can be seen,
the recovered frames remain unintelligible after replacement
attacks. Therefore, the proposed scheme is secure enoughagainst the replacement attacks.

(a)

(b)

(c)

Fig. 4. The recovered frames with replacement attacks. (a) and (b)are the recovered frames by replacing the intra-prediction mode,(c) is the recovered frame by replacing the sign bits of MVD.

3) Brute-force attacks
For video encryption algorithms, the brute-force attack is
not only based on cryptographic system analysis trying toenumerate all the system keys but also to enumerate theintra-prediction modes, DCT coefficients and MVD. Since
the AES supports 128, 192 and 256 bits key length, theminimum brute-force space is 2128. That is to say, the bruteforce space for each encrypted frame using the proposed
scheme is 2128. It is too large for attackers to break thecryptographic system. In addition, the brute-force space of
the intra-prediction mode, the sign of DCT coefficient and
MVD are 23, 2 and 2R (R is the length of the info_suffix of
MVD), respectively. Therefore, for a W×H size frame, thebrute-force space of intra-frame is Sint ra  [2  2 ] , L  0, M  WH / 256 ,where L is the number of the encrypted low frequency DCT
coefficients, and the brute-force space of inter-frame
3 N
R 2N
N  N  WH / 256
is Sint er  [2 ] [2 ] , R  1 , 1 2, where N1 and N2
are the number of intra-coded macroblocks and inter-coded
macroblocks respectively. Taking QCIF (W×H =176×144)
3

1

(a)

(b)

(c)

(d)

Fig. 3. The encrypted results of several videos. The column (a) is
the original frames, the column (b) is the encrypted intra-frames,
and the column (c) and (d) are the firth and last inter-frames in a
GOP respectively.

L M

2for example, the brute-force space is Sint ra  2 and Sint er  2 .
Similarly, this brute-force space is also large enough to resist
brute-force attacks.
297

198

Copyright ⓒ 2012 GiRI(Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

TABLE IIThe Edr testing results
Size

QCIF

Video
Akiyo
News
MotherSalesman
ForemanEdr
10.02%
9.35%
8.68%
11.76%
10.76%

Size

CIF

Video
Akiyo
MobileTempeteFootballForemanEdr
9.60%
10.46%
7.03%
11.09%
9.82%

TABLE IIIThe Testing Results of Computational Cost

Video

Size

Foreman
Akiyo
MotherAkiyo
Foreman
Mobile

QCIF
QCIF
QCIF
CIF
CIF
CIF

Time ratio
Encryption/
Decryption/
Compression
Decompression
SEAH264 PVEA SEAH264 PVEA
0.9%
0.5%
5.2%
2.9%
1.1%
0.6%
4.9%
4.6%
0.7%
0.3%
5.9%
2.8%
0.7%
0.4%
6.1%
3.6%
1.0%
0.3%
6.2%
4.3%
0.9%
0.8%
6.2%
5.8%

C. Memory requirement
Generally, the larger amount of data is processed inencryption, the more memory is required. Therefore, thememory requirement of the proposed scheme can bemeasured by the amount of the encrypted data. Figure 5
shows the ratio between the encrypted data and thecorresponding slice data. As can be seen, for intra-frames,
the encrypted data is about 15% of the corresponding slice
data, and for inter-frames, the ratio is only about 5%. That is
because, considering intra-frames are more important thaninter-frames, the proposed scheme provides enhanced
encryption to this kind of information. All in all, comparedwith the existing encryption schemes, the proposed scheme
adds less overhead of memory requirement.
Encrypted Data Ratio

15
Foreman

10

5

0

5

10

15

20

25
30
Frame Num

35

40

45

50

20

Encrypted Data Ratio

B. Computational complexity
The proposed scheme can be achieved after three steps,
including bit extraction, encryption and bit resetting. Hence,the computational complexity of the proposed scheme will
mainly depend on these three steps. In the bit extraction
process, the codewords of intra-prediction mode, DCT
coefficients and MVD can be quickly detected according to
the H.264 bitstream hierarchical structure, and then the bits
which should be encrypted are directly extracted from thesecodewords based on the corresponding entropy coding
without being decoded firstly. For example, the info_suffix
of MVD can be extracted according to the M-bit leading
zeros. The encryption time consumption depends on the data
volumes to be encrypted. Table 2 gives the ratio between theencrypted data and the entire bitstream (Edr) of various
standard videos. The table clearly shows that all the Edr are
no more than 15%. To save the bit resetting time
consumption, the location information of the encrypted bits
is recorded during the bit extraction processing, so that theencrypted bits can be easily put back into their original
position just like a replacement. Thus, the computational
complexity of the proposed scheme will be very low.
In our experiments, the Encryption-to-compression time
ratio (Etr) and the Decryption-to-decompression time ratio(Dtr) are tested. Table 3 gives the experimental results ofvarious videos, where the proposed scheme is called
PEH264. As can be seen, most of the Etr of the proposed
scheme is no more than 1%, and the Dtr of the proposed
scheme is also no more than 5% and 10% for QCIF and CIF
videos respectively. In addition, the Etr and Dtr of the
proposed scheme are all superior in comparison to the
SEH264 algorithm [11]. All in all, the proposed scheme
obtains significant computational efficiency. Thus, it will bewell suited for real-time video applications.

15

Mobile
15
10
5
0

0

5

10

15

20

25
30
Frame Num

35

40

45

50

Fig. 5. The encrypted data ratio versus frame number

V. CONCLUSION
In this paper, an efficient video encryption scheme in
H.264 compressed domain has been proposed. Firstly, thehierarchical structure of the H.264 bitstream is analysed, todetect the most significant bits for video reconstruction in
H.264 bitstream. Then, the rem_intra4×4_pred_mode of theintra-prediction mode codewords, the sign bit of the low
frequency DCT coefficients of intra-frames and theinfo_suffix of the MVD codewords are directly extracted
and encrypted with the AES algorithm. Experimental results
show that the proposed scheme exhibits reliable perceptual
security, can secure against not only replacement attacks but
also brute-force attacks, and meanwhile obtains significant
computational efficiency. Furthermore, it maintains theformat-compliance to the H.264 standard decoder and has
no impact on the compression ratio. Thus, the proposed
scheme will be well suited for real-time video applications
and resource-limited systems such as smartphone andwireless sensor network.
REFERENCES[1][2]

Data Encryption Standard (DES), FIPS PUB 46, Jan. 1977.
Advanced Encryption Standard (AES), FIPS-PUB 197, Nov. 2001.

Copyright ⓒ 2012 GiRI(Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

J. Wen, M. Severa, W. Zeng, et al, “A format-compliant configurable
encryption framework for access control of video,” IEEE Trans.
Circuits Syst. Video Technol., vol. 12, no. 6, pp. 545-557, Jun. 2002.
[4] F. Liu and H. Koenig, “A survey of video encryption algorithms,”
Computers and Security, vol. 29, pp. 3-15, Feb. 2010.
[5] L. Tang, “Methods for encrypting and decrypting MPEG video data
efficiently,” Proceedings of the 4th Multimedia Conference (ACM
Multimedia 96), pp. 219-229, Boston, MA, USA, Nov. 1996.
[6] A. S. Tang and W. C. Feng, “Efficient multi-layer coding andencryption of MPEG video streams,” IEEE International Conference
on Multimedia and Expo., vol. 1, pp. 119-122, Aug. 2000.
[7] W. Zeng and S. Lei, “Efficient frequency domain selective
scrambling of digital video,” IEEE Transactions on Multimedia, vol.
5, no. 1, pp. 118-129, Mar. 2003.
[8] C. Shi and B. Bharqava, “An Efficient MPEG video encryption
algorithm,” Proceedings of the 17th IEEE Symposium on Reliable
Distributed Systems, pp. 381-386, Oct. 1998.
[9] C. Shi, S. Wang, and B. Bhargava, “MPEG video encryption in realtime using secret key cryptography,” In Proc. of PDPTA’99, Las
Vegas, Nevada, pp.2822-2828, 1999.
[10] C. P. Wu and C. J. Kuo, “Design of integrated multimedia
compression and encryption systems,” IEEE Transactions on
Multimedia, vol. 7, no. 5, pp. 828-839, Oct. 2005.
[11] S. Lian, Z. Liu, Z. Ren and H. Wang, “Secure advanced video coding
based on selective encryption algorithms,” IEEE Transactions on
Consumer Electronics, vol. 52, no. 2, pp. 621-629, May 2006.
[12] J. Zhou, Z. Liang, Y. Chen and O. C. Au, “Security analysis ofmultimedia encryption schemes based on multiple Huffman table,”IEEE Signal Processing Letters, vol. 14, no. 3, pp. 201-204, Mar.
2007.
[13] L. Qao and K. Nahrstedt, “A new algorithm for MPEG video
encryption,” Proceedings of the First International Conference on
Imaging Science, Systems and Technology (CISST’97), pp. 21-29,
Las Vegas, Nevada, July 1997.
[14] T. Shi, B. King and P. Salama, “Selective encryption for H.264/AVC
video coding,” In SPIE International Society for OpticalEngineering (San Jose, CA, USA), vol. 6072, pp. 171-179, Feb. 2006.
[15] J. Ahn, H. Shim, B. Jeon and I. Choi, “Digital Video Scrambling
Method Using Intra Prediction Mode,” Advanced in Multimedia
Information Processing PCM2004, vol. 3333, pp. 386-393, Dec.
2004.
[16] T. Wiegand, G. J. Sullivan, G. Bjntegaard, et al, “Overview of the
H.264/AVC video coding standard,” IEEE Tran. Circuits Syst. Video
Technol., vol. 13, no. 7, pp. 560-576, July 2003.
[17] J. Jiang, S. Xing and M. Qi, “An intra prediction mode-based video
encryption algorithm in H.264,” International Conference on
Multimedia Information Networking and Security (MINES 2009), pp.
478-482, Hubei, China, Nov. 2009.
[18] Y. Mao, and M. Wu, “A joint signal processing and cryptographic
approach to multimedia encryption,” IEEE Transactions on Image
Processing, vol. 15, no. 7, pp. 2061-2075, July 2006.
[3]

Li ZHUO received the B.E degree inRadio Technology from the University
of Electronic Science and Technology,
Chengdu, China, in 1992, the M.E
degree in Signal & Information
ProcessingfromtheSoutheast
University, Nanjing, in 1998, and thePH.D degree in Pattern Recongnization
and Intellectual System from Beijing
University of Technology, in 2004. She has been a professor
in Beijing University of Technology since 2007. She has
published over 160 research papers and authored 4 books.
Her research interest includes image/video coding and16transmission ， multimedia content analysis, Multimedia
information security.
Niansheng MAO received the B.E
degree in Electronic and Information
Engineering from the Beijing Universityof Technology, Beijing, China, in 2005,
the M.E degree in Information andCommunication Engineering from the
Beijing University of Technology,
Beijing, China, in 2012. His researchinterest includes video coding, scalable
video coding, multimedia information security.
Haojie SHEN received the B.E degreeinElectronicandInformation
Engineering from the Beijing Universityof Technology, Beijing, China, in 2011.
He is currently pursuing the M.E degree
in Information and Communication
Engineering from the Beijing Universityof Technology, Beijing, China. His
research interest includes video coding,multimedia information security.
Jing ZHANG received the Ph.D degreefrom Beijing University of Technology.
She is currently an associate professor
and a master supervisor at Beijing
University of Technology-China and the
Signal & Information Processing Lab.
Her
research
interests
includeimage/video processing, retrieval.

Xiaoguang LI was born in Beijing,
China. He received the B.E and Ph.D
degrees in Electronic Engineering fromthe Beijing University of Technology,
Beijing China, in 2003 and 2008
respectively. He is currently an
Associate Professor and master student
supervisor of the Beijing University ofTechnology. His research interests include image and video
processing and computer vision.

Copyright ⓒ 2012 GiRI(Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 201217

A Method for Evaluation of Qualityof Service in Computer Networks
Tomasz Bujlow, Sara Ligaard Nørgaard Hald, Tahir Riaz, Jens Myrup Pedersen
Section for Networking and Security, Department of Electronic Systems
Aalborg University, DK-9220, Aalborg East, Denmark
tbu@es.aau.dk, slh@es.aau.dk, tahir@es.aau.dk, jens@es.aau.dk

Abstract—Monitoring of Quality of Service (QoS) in highspeed Internet infrastructures is a challenging task. However,precise assessments must take into account the fact that therequirements for the given quality level are service-dependent.
The backbone QoS monitoring and analysis requires processingof large amounts of data and knowledge of which kindsof applications the traffic is generated by. To overcome thedrawbacks of existing methods for traffic classification, we
proposed and evaluated a centralized solution based on the C5.0
Machine Learning Algorithm (MLA) and decision rules. The firsttask was to collect and to provide to C5.0 high-quality training
data divided into groups, which correspond to different types ofapplications. It was found that the currently existing means ofcollecting data (classification by ports, Deep Packet Inspection,
statistical classification, public data sources) are not sufficient andthey do not comply with the required standards. We developed
a new system to collect training data, in which the majorrole is performed by volunteers. Client applications installedon volunteers’ computers collect the detailed data about eachflow passing through the network interface, together with theapplication name taken from the description of system sockets.
This paper proposes a new method for measuring the level of
Quality of Service in broadband networks. It is based on our
Volunteer-Based System to collect the training data, Machine
Learning Algorithms to generate the classification rules andthe application-specific rules for assessing the QoS level. We
combine both passive and active monitoring technologies. The
paper evaluates different possibilities of implementation, presents
the current implementation of particular parts of the system,
their initial runs and the obtained results, highlighting parts
relevant from the QoS point of view.
Index Terms—broadband networks, data collecting, Machine
Learning Algorithms, performance monitoring, Quality ofService, traffic classification, volunteer-based system.
Manuscript received August 1, 2012. This work was a part of a PhD
project (Grant No. 8–10100) co-financed by Center for TeleInFrastruktur at
Aalborg University, a global research center (see http://www.ctif.aau.dk/),Vækstforum Nordjylland, a regional development project (seehttp://www.rn.dk/RegionalUdvikling/Vaekstforum/), and Bredbånd Nord
A/S, a regional fiber networks deliverer (see http://www.bredbaandnord.dk/).
Tomasz Bujlow is with Section for Networking and Security, Department
of Electronic Systems, Aalborg University, Aalborg East, 9220 Denmark (tel:+45 5026 2494, e-mail: tbu@es.aau.dk).
Sara Ligaard Nørgaard Hald is with Section for Networking and Security,
Department of Electronic Systems, Aalborg University, Aalborg East, 9220
Denmark (e-mail: slh@es.aau.dk).
Tahir Riaz is with Section for Networking and Security, Department ofElectronic Systems, Aalborg University, Aalborg East, 9220 Denmark (e-mail:tahir@es.aau.dk).
Jens Myrup Pedersen is with Section for Networking and Security,
Department of Electronic Systems, Aalborg University, Aalborg East, 9220
Denmark (e-mail: jens@es.aau.dk).

I. I NTRODUCTION
This journal paper is the extended and revised version of [1]which was presented at the 14th International Conference on
Advanced Communication Technology (ICACT 2012).
One of the most interesting challenges in today’s world
is how to measure the performance of computer network
infrastructures, when different types of networks are merged
together. In the last few years the data-oriented networks
evolved into converged structures, in which real-time traffic,
like voice calls or video conferences, is more and more
important. The structure is composed of traditional data cableor more modern fiber links, existing Plain Old Telephone
Service (POTS) lines used to provide analog services (voice
telephony), or digital services (ADSL, PBX, ISDN), andnowadays also of mobile and wireless networks. There are
numerous methods for the measurement of Quality of Service(QoS) in current use, which provide the measurements bothon the user side and in the core of the network. Internet
Service Providers are interested in centralized measurements
and detecting problems with particular customers before
the customers start complaining about the problems, andif possible, before the problems are even noticed by thecustomers.
Each network carries data for numerous different kindsof applications. QoS requirements are dependent on theservice. The main service-specific parameters are bandwidth,
delay, jitter, and packet loss. Regarding delay, we candistinguish strict real time constraints for voice and videoconferences, and interactive services from delivery in relaxed
time frame. In conversation, a delay of about 0.1 s is hardly
noticeable, but 0.25 s delay means an essential degradationof transmission quality, and more than 0.4 s is considered asseverely disturbing [2].
Therefore, in order to provide detailed information about
the quality level for the given service in the core of the
network, we need to know, what kind of data is flowing in
the network at the present time. Processing all the packets
flowing in a high-speed network and examining their payload
to get the application name is a very hard task, involving
large amounts of processing power and storage capacity.
Furthermore, numerous privacy and confidentiality issues canarise. A solution for this problem can be use of Machine
Learning Algorithms (MLAs), which use previously generated
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012decision rules, which are based on some statistical information
about the traffic. In our research, we used one of the newest
MLAs - C5.0. MLAs need very precise training sets to learn
how to accurately classify the data, so the first issue to besolved was to find a way to collect high-quality training
statistics.
In order to collect the necessary statistics and generate
training sets for C5.0, a new system was developed, inwhich the major role is performed by volunteers. Client
applications installed on their computers collect the detailed
information about each flow passing through the network
interface, together with the application name taken from thedescription of system sockets. Information about each packet
belonging to the flow is also collected. Our volunteer-based
system guarantees precise and detailed data sets about the
network traffic. These data sets can be successfully used togenerate statistics used as the input to train MLAs and to
generate accurate decision rules.
The knowledge about the kind of application to which thetraffic belongs obtained from MLAs can be used together withtraffic requirements for the given application to assess the QoSlevel in the core of the real network. The real traffic needs to besampled to obtain the necessary raw statistics. Parameters likejitter, burstiness, download and upload speed can be assessed
directly on the basis of information obtained from the captured
traffic. To assess delay and packet loss, active measurement
techniques must be involved (like ping measurements in both
directions).
The remainder of this document is split into several sections,which describe in detail the system architecture and some partsof the implementation. Section II contains the overview ofcurrent methods of assessing the network QoS level. Bothpassive and active methods are described along with theiradvantages and weaknesses. Section III gives an overview
of our methods, so the reader is able to understand how
the particular components are built and connected with eachother. Section IV describes current methods used for trafficclassification in computer networks and it explains why they
are not sufficient for our needs. Section V presents our new
tool used for collecting and classification of the network
traffic – the Volunteer-Based System (VBS). Section VI shows
how the statistical parameters are obtained from the datacollected by VBS. Section VII evaluates different Machine
Learning Algorithms and shows why we chose C5.0 to be
included in our system. Section VIII demonstrates design andimplementation of the system, while Section IX summarizes
the most important points.
II. R ELATED WORK
During the last 20 years we have been witnesses to thesubsequent and increasing growth of the global Internet
and the network technology in general. The broadband andmobile broadband performance today is mainly measured
and monitored by speed. However, there are several otherparameters, which are important for critical business and realtime applications, such as voice and video applications or firstperson shooter games. These parameters include download

18

and upload speeds, round trip time, jitter, packet loss, andavailability [3], [4].
The lack of the centralized administration makes it difficult
to impose a common measurement infrastructure or protocol.
For example, the deployment of active testing devices
throughout the Internet would require a separate arrangementwith each service provider [3]. This state of affairs led tosome attempts to make simulation systems representing realcharacteristics of the traffic in the network. Routers and traffic
analyzers provide passive single-point measurements. They do
not measure performance directly, but traffic characteristics arestrongly correlated with performance. Routers and switches
usually feature a capability to mirror incoming traffic to a
specific port, where a traffic meter can be attached. The maindifficulty in passive traffic monitoring is the steadily increasingrate of transmission links (10 or 100 GB/s), which can simply
overwhelm routers or traffic analyzers, which try to process
packets. It forces introduction of packet sampling techniques
and, therefore, it also introduces the possibility of inaccuracies.
Even at 1 Gbit/s, the measurement can result in enormous
amount of data to process and store within the monitoring
period [3].
To overcome the heavy load in the backbone and to not
introduce inaccuracies, a smart monitoring algorithm wasneeded. There are several approaches to estimate which traffic
flows need to be sampled. Path anomaly detection algorithm
was proposed in [5]. The objective was to identify thepaths, whose delay exceeds their threshold, without calculating
delays for all paths. Path anomalies are typically rare events,and for the most part, the system operates normally, so there
is no need to continuously compute delays for all the paths,
wasting processor, memory, and storage resources [5]. Authors
propose a sampling-based heuristic to compute a small set ofpaths to monitor, reducing monitoring overhead by nearly 50 %comparing to monitoring all the existing paths.
The next proposals on how to sample network traffic in an
efficient way were made on the basis of adaptive statisticalsampling techniques, and they are presented in [6] and [7].
If a congestion is detected, from user’s perspective it is very
important to know, if the congestion is located on the local oron the remote side. If the link experiences a local congestion,
the user may be able to perform certain actions, e.g. shut down
an application, which consumes a lot of bandwidth, to easethe congestion. On the other hand, if the congested link is aremote link, either in the Internet core or at the server side,
the back-off of the low-priority applications on the user’s sideis unnecessary. It only benefits the high-priority flows fromother users, which compete for that link. Since this altruistic
behavior is not desirable, the low priority TCP only needs toback off, when the congested link is local [8].
Detecting the location of congestion is a challenging
problem due to several reasons. First of all, we cannot send
many probing packets, because it causes too much overhead,
and it even expands the congestion. Secondly, without router
support, the only related signals to the end applications arepacket losses and delays. If packet losses were completely
synchronized (packets were dropped from all the flows), theproblem would be trivial. In reality, the packet loss pattern is
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

only partially synchronized [8]. Authors of [8] attempted tosolve the problem of detecting the location of the congestion
by using the synchronization of the behavior of loss anddelay across multiple TCP sessions in the area controlled
by the same local gateway. If many flows see synchronizedcongestion, the local link is the congested link. If the congested
link is remote, it is less likely that many flows from the same
host pass the same congested link at the same time. If there
is only a small number of flows which see the congestion,
the authors performed an algorithm based on queuing delay
patterns. If the local link is congested, most flows typicallyexperience high delays at a similar level. Otherwise, thecongestion is remote [8].
Traffic can be profiled according to the protocolcomposition. Usually, predominance of the TCP traffic isobserved (around 95 % of the traffic mix). When congestion
occurs, TCP sources respond by reducing their offered load,
whereas UDP sources do not. It results in the higher ratio of
UDP to TCP traffic. If the proportion becomes high and the
bandwidth available to TCP connections becomes too low tomaintain a reasonable transmission window, the packet loss
increases dramatically (and TCP flows become dominated byretransmission timeouts) [3]. Packet sizes provide insight into
the type of packet, e.g. short 40-44 bytes packets are usually
TCP acknowledgment or TCP control segments (SYN, FIN or
RST) [3].
Active methods for QoS monitoring raise three major
concerns. First, the introduction of the test traffic will
increase the network load, which can be viewed as an
overhead cost for active methods. Second, the test traffic canaffect measurements. Third, the traffic entering ISP can beconsidered as invasive and discarded or assigned to a lowpriority class [3].
Within an administrative domain (but not across theentire Internet), performance can be actively monitored using
the data-link layer protocol below IP, as the Operations,
Administration and Maintenance (OAM) procedure in ATM
and MPLS networks. As a result, at the IP layer it is often
desirable to measure performance using the IP/ICMP protocol.
So far, most tools or methods are based on ping (ICMP echo
request and echo reply messages) or traceroute (which exploits
the TTL field in the header of the IP packet) [3].
Although the round-trip times measured by ping areimportant, ping is unable to measure the one-way delay
without additional means like GPS to synchronize clocks at thesource and destination hosts. Another difficulty is that pings
are often discarded or low-prioritized in many ISP networks.
Traceroute will not encounter this problem because UDP
packets are used. However, traceroute has known limitations.
For example, successive UDP packets sent by traceroute are
not guaranteed to follow the same path. Also, a returned ICMP
message may not follow the same path as the UDP packet thattriggered it [3].
Although end-to-end performance measurements can be
carried out at the IP layer or the transport/application layer, thelatest is capable of measurements closer to user’s perspective.
The basic idea is to run a program emulating a particularapplication that will send traffic through the Internet. All the19parameters (delay, loss, throughput, etc) are measured on thetest traffic. This approach has one major drawback - custom
software needs to be installed at the measurement hosts [3].
On the basis of the mentioned work we found out thatthe existing solutions are not sufficient for precise QoS
measurements. This state of affairs motivated us to create
a new system which combines both passive and active
measurement technologies.
III. T HE OVERVIEW OF THE METHODS
The flow chart of the system is shown in Figure 1. The
following paragraphs contain detailed description of our
methods. At first, the volunteers must be recruited from the
network users. The volunteers install on their computer a client
program, which captures relevant information about the trafficand submits the data to the server. On the server these data
are used to generate per-application traffic statistics. The C5.0
Machine Learning Algorithm uses these statistics to learn how
to distinguish between different types of applications and,later, it generates the classification rules (decision trees).
In order to assess the network QoS level in the core of the
network for particular users we needed to find a method to
capture the relevant traffic. The challenging task is to process
significant amount of traffic in the high-speed networks. Whenthe relevant flows are captured, per-flow statistics need tobe generated. There are two kind of statistics generated at
this step: One used for determining the kind of application
associated with that flow, and one used for assessing the QoSlevel in the passive way. The system uses previously generated
classification rules together with the first type of statistics tofind out which application the flow belongs to. Then, on thebasis of the kind of the application, the system determines
ranges of values of the relevant QoS parameters. The last stepis to check if the current values (obtained from flow statistics
or in the active way) match the expected ones. If not, thequality of the given service is considered as degraded.
IV. T HE CURRENT METHODS FOR OBTAINING
PRE - CLASSIFIED DATA
There are many existing methods for obtaining pre-classified
data, but none of them were feasible to deliver data requiredby us to obtain accurate statistics, which could be used
to train Machine Learning Algorithms (MLAs). The trafficclassification requires the packets to be logically grouped intosome structures, which could be assigned to the particularapplication. The most common used structure among theclassification methods is the flow defined as a group of packets,
which have the same end IP addresses, ports, and use thesame transport layer protocol. In this paragraph we describe
the methods and evaluate their usefulness in providing datafor our system.
A. Capturing raw data from the network interfaces
The first possibility is to install one application at a time
on a host, and to capture its traffic by an external tool,such as Wireshark [9]. Unfortunately, this approach is very
slow and it is not scalable. At first, it requires us to install
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

volunteers

Figure 1.

recruit20

users in the
production network

capture information
about all the
traffic by VBS

capture selected flows
from the users in thecore of the network

generate per-application
traffic statistics

generate per-flow
traffic statistics

per-flow statistics
for estimating
QoS leveltrain C5.0 and generate
decision rules for
assigning a flow toparticular application

per-flow statistics
for application
recognitionassess jitter, burstiness,
download andupload speed

decision trees

discover to whichapplications eachflow belongs

per-flow
application nameactivemeasurements
in both directions

assess delay
and packet loss

The flow chart of the systemon a host each application that generates traffic we want tocapture. Before installing the application, we must uninstall
all other applications that can generate any network traffic.
The next drawback is that every operating system has somebackground processes and many of them transmit some data
through the network. An example of such a process is thesystem updater, which can run in background. There is no
simple way to recognize packets belonging to the traffic
generated by the application intentionally run by us, so thecaptured sample contains a variable percentage of noise.
Finally, some applications, for example, web browsers, cangenerate various types of traffic. Raw traffic capturers cannot
distinguish interactive web traffic, web radio podcasts, video
transmissions, or downloads of big files, performed by thesame browser.
B. The classification by ports
The port-based classification [10], [11] is very fast, and it
is supported on almost all the layer-3 devices in computer
networks. Unfortunately, this method is limited to services,

protocols, and applications, which use fixed port numbers. It
means that with big probability we can correctly classify, forexample, traffic generated by e-mail clients and file transfer
clients using File Transfer Protocol (FTP), when they use thedefault ports to connect to servers. However, even in this case
we have false positives and false negatives. False negatives
result from non-standard ports used in this example by SMTP,
POP3, or FTP servers. When a network administrator changes
the port used by the given service (due to security reasons),
the traffic is not classified correctly. False positives result frommalicious applications, which intentionally use some wellknown port numbers to be treated in the network with apriority, or to be able to transmit data at all. Such situation
exists when a Torrent user runs his client on port 80, whichcause the traffic to be treated as if it originated from a web
server. Another big concern of port-based classification is theinability of recognizing different types of traffic using thesame transport-layer protocol and the same transport-layer
port. This drawback is strongly visible in the example of HTTP
traffic, which can consist of data generated by interactive
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012web browsing, audio and video streaming, file downloads, andHTTP tunneling for other protocols. Finally, the classification
made by ports is unable to deal with protocols using dynamic
port numbers, like BitTorrent or Skype [9], [12], [13].
C. The Deep Packet Inspection (DPI)The big advantage of the Deep Packet Inspection (DPI) [14] isthe possibility to inspect the content of the traffic. It includes
both inspecting particular packets, and inspecting flows in the
network as the whole. For that reason, it makes it possible
to distinguish different kinds of content generated by thesame application, or using the same application-layer protocol,
such as HTTP. However, DPI is slow and requires a lot ofprocessing power [9], [12]. Therefore, due to high load intoday’s network infrastructures, it is not feasible to run DPI
in the core of the network. Speed of Internet connections
provided to private users tends to increase much faster thanprocessing power of their machines, so performing DPI on
user’s machines became impossible in my case. Feasibility
to perform DPI on the user side does not depend only on
possessing the necessary processing power, but also on theuser’s impression. High CPU usage tends to slow down themachine and it causes additional side-effects, for example,
a howling CPU fan. For that reason, full DPI can be done
only in a limited number of cases, namely on fast machines
using a slow Internet connection. DPI also brings privacy andconfidentiality issues, as it can reveal some highly sensitive
personal data, such as information about used credit cards,
logins and passwords, websites visited by the user, etc [9].
Moreover, DPI is unable to inspect encrypted traffic. Finally,
DPI depends on signatures of various protocols, services, andapplications, which need to be kept up to date.
D. The statistical classification
Solutions using statistical classification became quite popularduring the last few years [14]. To its characteristics we can
include fast processing and low resource usage. Statisticalclassifiers are usually based on rules, which are automaticallygenerated from samples of data. Therefore, such kinds ofclassifier often make use of Machine Learning Algorithms(MLAs). Apart from all these advantages, statistical classifier
have one big drawback – they need to be trained on thesamples of data. So the technique assumes that we havealready correctly classified data, which we can provide as
the input to train the statistical classifier. For that reason, we
cannot use this method to collect and classify the initial portionof data.
V. T HE VOLUNTEER -BASED S YSTEM
The drawbacks of the existing methods for classification of
traffic in computer networks led us to the conclusion that
we need to design and build another solution. Therefore,
we decided to develop a system based on volunteers, which
captures the traffic from their network interfaces, and groups
the traffic into flows associated with the application nametaken from Windows or Linux sockets. The architecture

21

and the prototype were described and analyzed in [15] and[16], and the first version of our current implementation
was presented in [17]. Afterwards, the system was extended
to support recognizing different kinds of HTTP traffic, andit was named Volunteer-Based System (VBS). The detailed
description and evaluation of the extended version of the VBS
system can be found in [18]. We released the system under
The GNU General Public License v3.0, and we published it as
a SourceForge project. The project website [19] contains all
the information needed to use the system ( binary packages,
screenshots, documentation and bug tracking system) as wellas to perform further development (source code, roadmap,
comprehensive documentation of the source code).
The architecture of the system is shown in Figure 2. Thiscross-platform solution consists of clients installed on users’
computers (Microsoft Windows XP and newer and Linux aresupported), and of a server responsible for storing the collected
data. The client registers information about each flow passing
the Network Interface Card (NIC), with the exception of traffic
to and from the local network. The captured information
are: The start time of the flow, the anonymized identifiersof the local and the remote IP addresses, the local and theremote ports, the transport layer protocol, the anonymized
identifier of the global IP address of the client, the name of
the application, and the identifier of the client associated withthe flow. The system also collects information about all thepackets associated with each flow: The identifier of the flow
to which the packet belongs, the direction, the size, the TCP
flags, the relative timestamp to the previous packet in the flow,
and the information about the HTTP content carried by the
packet. It is worth mentioning that one flow can contain manyhigher-layer streams, for example, one TCP flow can containmultiple HTTP conversations. Each of these conversations cantransfer different kinds of content, like web pages, audio andvideo streams, or downloads of big files. For that reason we
extract from HTTP headers information necessary to precisely
separate the HTTP streams, and we append the information
about the type of the stream to the first packet of the stream.
The collected information is then transmitted to the server,which stores all the data in a MySQL database for further
analysis. The system was shown in [18] to be feasible
and capable of providing detailed per-application information
about the network traffic. An example of stored flows on
the server side is shown in Table I. The IP addresses forprivacy reasons are translated by a one-way hash function
and they are stored as anonymized identifiers. The information
about the packets belonging to one complete TCP conversation
is presented in Table II. As shown, this is an HTTP
communication, during which there were transferred two filesof the same type with identifier 22 (text/html).
The data collected during our experiments by the VolunteerBased System were used for training the C5.0 Machine
Learning Algorithm to be able to recognize traffic generated
by different types of applications and different types of traffic.
The first approach, focusing on distinguishing 7 different
applications (Skype, FTP, torrent, web browser, web radio,
America’s Army and SSH) and achieving accuracy of over
99 % was described and evaluated in [20]. The second
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

Figure 2.

22

Overview of the VBS system [16]Table IE XAMPLE OF THE STORED FLOWS DATA

flow idclientid

start time

local IPremote IP

local
port

remoteport

protocolnameglobal clientIPapplication name1193430
23934171193423148467334296743329860
382958934740274194793

4
5
1
4
4
1
1
4
1

1325445237826039
1325445237826176
1325445237826304
1325445237825884
1325445236820017
1325445237044777
1325445236797638
1325445212663601
1325445280781252

d1e0229
f4c025ed20022bd1e0229d1e0229d20022bd20022bd1e0229d20022bfb70266
12230296
11920285
12170293
14cb02b91199028a124d0296
14db02c2
1206028f

48293
2276
53778
58104
61159
47801
36868
63409
53331

25395
80
80
993
80
80
80
24536
22849

UDP
TCP
TCP
TCP
TCP
TCP
TCP
UDP
TCP

178a02f1
177d02ef
12350297
178a02f1
178a02f1
12350297
12350297
178a02f1
12350297

uTorrent
chromefirefoxthebatDropboxplugin-container
wget
Skype
amule

Table IIO NE TCP COMMUNICATION STORED IN THE DATABASE
flow id

direction

packet size
[B]

SYN
flag

ACK
flag

PSH
flag

FIN
flag

RST
flag

CWR
flag

ECN
flag

URG
flag

relative timestamp[µs]

content type278467327846732784673278467327846732784673278467327846732784673278467327846732784673278467327846732784673

OUT
IN
OUT
OUT
IN
IN
OUT
IN
OUT
OUT
OUT
IN
OUT
IN
OUT

60
60
52
431
52
527
52
539
52
287
52
269
40
52
40

1
1
0
0
0
0
0
0
0
0
0
0
0
0
0

0
1
1
1
1
1
1
1
1
1
1
1
0
1
0

0
0
0
1
0
1
0
1
0
1
0
1
0
0
0

0
0
0
0
0
0
0
0
0
0
1
0
0
1
0

0
0
0
0
0
0
0
0
0
0
0
0
1
0
1

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0
30012
44
395
30241
2554
27
10455
15
1383
15047
16408
45
13354
29

1
1
1
1
1
22
1
22
1
1
1
1
1
1
1

approach, focusing on recognizing different kinds of HTTP
content (audio, video, file downloads, interactive websites) waspresented in [21].
VI. O BTAINING PER - APPLICATION STATISTICS
The next step was to obtain statistical profiles of flows fordifferent applications. Therefore, we developed a tool for
calculating statistics on several traffic attributes for each flowin the database, which fulfills our requirements. The statistics
include 32 attributes based on sizes and 10 protocol-dependent
attributes [20]. We suspect that the attributes based on sizes

are independent of the current conditions in the network (likefor example congestion). All the protocol-dependent attributes
are very general. Precise port numbers are not used, but only
information about whether the port is well-known or dynamic.
This way we avoid constructing a port-based classifier, but wecan retain the information if the application model is more
like client-server or peer-to-peer.
The general calculated statistics are [20]:number of inbound / outbound / total payload bytes inthe sample.
• proportion of inbound to outbound data packets / payload•

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

bytes.
mean, minimum, maximum first quartile, median, third
quartile, standard deviation of inbound / outbound / total
payload size in the probe.
• ratio of small inbound data packets containing 50 B
payload or less to all inbound data packets.
• ratio of small outbound data packets containing 50 B
payload or less to all outbound data packets.
• ratio of all small data packets containing 50 B payload orless to all data packets.
• ratio of large inbound data packets containing 1300 B
payload or more to all inbound data packets.
• ratio of large outbound data packets containing 1300 B
payload or more to all outbound data packets.
• ratio of all large data packets containing 1300 B payload
or more to all data packets.
• application: skype, ftp, torrent, web, web_radio, game,ssh.
The protocol-dependent attributes are [20]:• transport protocol: TCP, UDP.
• local port: well-known, dynamic.
• remote port: well-known, dynamic.
• number of ACK / PSH flags for the inbound / outbounddirection: continuous.
• proportion of inbound packets without payload toinbound packets: continuous.
• proportion of outbound packets without payload tooutbound packets: continuous.
• proportion of packets without payload to all the packets:
continuous.
The precise process of obtaining these statistics was
described in detail and evaluated in [20]:23•VII. M ACHINE L EARNING A LGORITHMS
In the recent literature we can find numerous approaches touse Machine Learning Algorithms to classify the traffic incomputer networks. The most widely used MLA classifiers
are C4.5 [9] and its modified Java implementation called J48[12], [22]. Based on statistical analysis, MLAs have the ability
to assign a particular class (like P2P) even to traffic generated
by unknown applications [9]. It was also proved in [22] thatthe statistical parameters for encrypted and unencrypted traffic
produced by the same application are similar and, therefore,
the encrypted payload does not influence results of the trainingor the classification. The accuracy of the classification by
MLAs was claimed to be over 95 % [9]–[11], [13], [14], [23]–[25]. The analysis of the related work can be found in [20].
It was found in [11] that results of the classification are mostaccurate when the classifier was trained in the same networkas the classification process was performed. This may be due
to different parameters, which are constant in the particular
network, but which differ among various networks. A good
example is the Maximum Transmission Unit, which can easily
influence statistics based on sizes. Therefore, in our design
we decided to train the classifier by volunteers in the same
network as the classifier will be installed. This allows us tomake a self-learning system, where a group of volunteers

Figure 3.

Average error rates of the classifiers [20]in the network deliver data used for training the classifier
constantly improving its accuracy, while all the users can bemonitored in the core using the generated decision rules. The
next advantage of the design is that even if some network
users cannot participate in the data collecting process becauseof using other operating systems or devices than supported(like MacOS, Apple or Android smartphones), they will still
be able to be monitored in the core of the network becauseof rules created on the basis of data collected from the otherusers.
Our system uses the C5.0 MLA, which is a successorof C4.5. It is proven to have many advantages overits predecessor, such as higher accuracy, possibilities touse boosting, pruning, weighting and winnowing attributes.
Furthermore, the time to generate the decision tree or rules
drastically decreased [26]. In order to test the efficiency of
C5.0, we performed a set of tests during which we used various
training and classification options. The training statistics wereobtained from the data provided by our VBS. During ourresearch we found relevant set of arguments and discoveredthat the best results were obtained using the boosted classifier.
The average accuracy fluctuated between 99.3 % and 99.9 %depending on number of training and test cases and amount ofdata from each case. This behavior is illustrated in Figure 3. It
is worth mentioning that in our experiment we considered only
7 different groups of applications and only flows longer than
15 packets. In our small-scale prototype for tests we decided tolimit the number of applications and take into account Skype,
FTP, torrent, web traffic, web radio traffic, interactive gametraffic and SSH [20]. The limitation of the flow length wasdone because we needed to have at least 5 packets to generate
the statistics (the first 10 packets of each flow were skipped
as their behavior is different than the behavior of the rest of
the flow). The detailed description of our methods and results
can be found in [20]. The decision tree generated in this step
can be used to classify the traffic in the real network.
VIII. T HE CENTRALIZED MONITORING SOLUTION
This paragraph presents the proposed design of the centralized
monitoring solution which can be placed in any point in the
network to examine network QoS.
Because of heavy load in the high-speed networks, it is notpossible to monitor all the flows passing the central point at the
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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012

same time. Therefore, statistics from only selected flows canbe captured and passed to the C5.0. Selection of such flows
can be based on two methods: Capturing one flow per user andintelligent switching between the flows. From the QoS pointof view, it is important to discover problems with a particularuser or to inform the user that problems experienced by him
are results of problems in the remote network. If it is the user
who has the problem, then the problem usually influences all
the user’s network activity.
Each application has some special requirements regarding
network parameters. When a small congestion occurs, theservice level can still be sufficient for P2P file downloads,
but Skype communication may be not possible because of big
jitter and delays. It is, therefore, not sufficient to monitor one
random flow at a time, but we need to monitor a flow whichhave high quality requirements. Our solution should be built
based on the following assumptions:
Only one flow per user at a time is consistently monitored
for QoS.
• Statistics for another random flow per user at a time arepassed to C5.0 to discover the application.
• If the application has higher QoS requirements than thecurrently monitored, switch monitoring to the new flow;
if not, stick to the current.
• If monitoring of the selected flow discovers problems,
start monitoring few flows at a time to check if thisproblem lay on the user’s side or on the remote side.
•

Because of the dynamic switching between the flows when
determining the application, it is most probable that the system
will not be able to capture flows from their beginning. The
classifier designed by us, which use the C5.0, is able todetermine the application on the basis of the given numberof packets from any point in the flow [20].
Monitoring of the QoS can be done in passive or active
mode. The passive mode relies mostly on time-based statistics,
which are obtained directly from the flow passing themeasurement point. This way, we can assess the jitter, theburstiness and the transmission speed (both download andupload). Unfortunately, it is not possible to receive information
about the packet loss or the delay for other than TCP streams
while using this method. For that reason, additional tools
performing active measurements must be involved in theprocess of estimating the QoS. One option is to use the pingbased approach, as it can measure both delay and packet
loss. Unfortunately, other issues can arise. Ping requests andresponses are often blocked by network administrator, or
their priority is modified (decreased to save the bandwidth
or increased to cheat the users about the quality of theconnection). Other options include sending IP packets withvarious TTL and awaiting Time Exceeded ICMP messages,
which are usually allowed to be transmitted in all the networks
and their priority is not changed. Active measurements must
be done in both directions (from the user and from the remoteside). The total packet loss and the delay can be calculated
as the sum of the delays and the packet losses from both
directions of the flow. Furthermore, the knowledge of thedirection that causes problems can be used to assess if the24

problems are located in the local network or somewhereoutside.
IX. C ONCLUSION
The paper shows a novel method for assessing the Qualityof Service in computer networks. Our approach involves agroup of volunteers from the target network to participate in
the initial training of the system, and later in the self-learning
process. The accurate data obtained from the volunteers
are used by the C5.0 MLA to create the per-application
profiles of the network traffic as classification decision trees.
The centralized measurement system uses the decision trees
to determine the applications associated with flows passing
through the measurement point. This knowledge allows us toprecisely define the QoS requirements for each particular flow.
To assess the QoS level two methods are proposed: The passive
and the active one.
X. ACKNOWLEDGMENT
This research work was conducted in the context of a PhD
project in the Section for Networking and Security in the
Department of Electronic Systems at Aalborg University. We
are also very grateful, for the possibility to install and test
our Volunteer-Based System, to Gimnazjum nr 3 z Oddziałami
Integracyjnymi i Dwuj˛ezycznymi imienia Karola Wojtyły w
Mysłowicach, a high school in Poland.
R EFERENCES[1] Tomasz Bujlow, Tahir Riaz, Jens Myrup Pedersen, A Method forAssessing Quality of Service in Broadband Networks, Proceedings of the
14th International Conference on Advanced Communication Technology(ICACT 2012), IEEE 2012, pp. 826–831.
[2] Gerhard Haßlinger, Implications of Traffic Characteristics on Quality ofService in Broadband Multi Service Networks, Proceedings of the 30th
EUROMICRO Conference (EUROMICRO’04), IEEE Computer Society
2004.
[3] Thomas M. Chen, Internet Performance Monitoring, Proceedings of theIEEE, vol. 90, no. 9, September 2002, pp. 1592–1603.
[4] LIRNEasia Broadband QoSE Benchmarking project, 2008. [Online].
available: http://lirneasia.net/projects/2008-2010/indicators-continued/
broadband-benchmarking-qos-20/[5] K. v. M. Naidu, Rajeev Rastogi, Bell Labs Research India, Bangalore,
Detecting Anomalies Using End-to-End Path Measurements, IEEE
INFOCOM 2008 proceedings, 2008, pp. 16–20.
[6] A. Dogman, R. Saatchi, S. Al-Khayatt, An Adaptive Statistical Sampling
Technique for Computer Network Traffic, IEEE CSNDSP 2010, pp. 479–
483.
[7] Baek-Young Choi, Jaesung Park, Zhi-Li Zhang, Adaptive Random
Sampling for Traffic Load Measurement, IEEE International Conference
on Communications, IEEE 2003, pp. 1552–1556.
[8] Shao Liu, Mung Chiang, Mathias Jourdain, Jin Li, Congestion Location
Detection: Methodology, Algorithm, and Performance, 17th International
Workshop on Quality of Service, IEEE 2009.
[9] Jun Li, Shunyi Zhang, Yanqing Lu, Junrong Yan, Real-time P2P traffic
identification, IEEE GLOBECOM 2008 PROCEEDINGS.
[10] Riyad Alshammari, A. Nur Zincir-Heywood, Machine Learning based
encrypted traffic classification: identifying SSH and Skype, Proceedingsof the 2009 IEEE Symposium on Computational Intelligence in Security
and Defense Applications (CISDA 2009).
[11] Sven Ubik, Petr Žejdl, Evaluating application-layer classification
using a Machine Learning technique over different high speed
networks, 2010 Fifth International Conference on Systems and Networks
Communications, IEEE 2010, pp. 387–391.
[12] Ying Zhang, Hongbo Wang, Shiduan Cheng, A Method for RealTime Peer-to-Peer Traffic Classification Based on C4.5, 12th IEEE
International Conference on Communication Technology, IEEE 2010,
pp. 1192–1195.

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ICACT Transactions on the Advanced Communications Technology(TACT) Vol. 1, Issue 1, July 2012[13] Jing Cai, Zhibin Zhang, Xinbo Song, An analysis of UDP trafficclassification, 12th IEEE International Conference on Communication
Technology, IEEE 2010, pp. 116–119.
[14] Riyad Alshammari, A. Nur Zincir-Heywood, Unveiling Skype encrypted
tunnels using GP, IEEE Congress on Evolutionary Computation (CEC),IEEE 2010.
[15] Kartheepan Balachandran, Jacob Honoré Broberg, Kasper Revsbech,Jens Myrup Pedersen, Volunteer-based distributed traffic data collection
system, Feb. 7-10, 2010 ICACT 2010, pp. 1147–1152.
[16] Kartheepan Balachandran, Jacob Honoré Broberg, Volunteer-based
distributed traffic data collection system, Master Thesis at Aalborg
University, Department of Electronic Systems, June 2010.
[17] Tomasz Bujlow, Kartheepan Balachandran, Tahir Riaz, Jens Myrup
Pedersen, Volunteer-Based System for classification of traffic in computer
networks, 19th Telecommunications Forum TELFOR 2011, IEEE 2011,
pp. 210–213.
[18] Tomasz Bujlow, Kartheepan Balachandran, Sara Ligaard Nørgaard Hald,
Tahir Riaz, Jens Myrup Pedersen, Volunteer-Based System for researchon the Internet traffic, to appear in Telfor Journal Vol. 4 (2011).
[19] Volunteer-Based System for Research on the Internet, 2012. [Online].
Available: http://vbsi.sourceforge.net/[20] Tomasz Bujlow, Tahir Riaz, Jens Myrup Pedersen, A method forclassification of network traffic based on C5.0 Machine Learning
Algorithm, International Conference on Computing, Networking andCommunications (ICNC 2012), IEEE 2012, pp. 244–248.
[21] Tomasz Bujlow, Tahir Riaz, Jens Myrup Pedersen, Classification of
HTTP traffic based on C5.0 Machine Learning Algorithm, to appear
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[22] Jason But, Philip Branch, Tung Le, Rapid identification of BitTorrent
Traffic, 35th Annual IEEE Conference on Local Computer Networks,
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[23] Jun Li, Shunyi Zhang, Yanqing Lu, Zailong Zhang, Internet
Traffic Classification Using Machine Learning, Second International
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[24] Yongli Ma, Zongjue Qian, Guochu Shou, Yihong Hu, Study ofInformation Network Traffic Identification Based on C4.5 Algorithm,
4th International Conference on Wireless Communications, Networking
and Mobile Computing, IEEE 2008.
[25] Wei Li, Andrew W. Moore, A Machine Learning Approach for Efficient
Traffic Classification, Proceedings of the Fifteenth IEEE International
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[26] Is See5/C5.0 Better Than C4.5?, 2009. [Online]. Available: http://www.
rulequest.com/see5-comparison.html

25

Tomasz Bujlow is working as a Ph.D. Student in
the Section for Networking and Security (NetSec)
in the Department of Electronic Systems at Aalborg
University in Denmark. He received his Masterof Science in Computer Engineering from SilesianUniversity of Technology in Poland in 2008,
specializing in Databases, Computer Networks andComputer Systems. Previously, he obtained his
Bachelor of Computer Engineering from Universityof Southern Denmark in 2009, specializing insoftware engineering and system integration. His
research interests include methods for measurement of Quality of Service
and traffic classification in computer networks. He is also a Cisco Certified
Network Professional (CCNP) since 2010.

Sara Ligaard Nørgaard Hald is working as a Ph.D.
student in the Section for Networking and Security(NetSec) in the Department of Electronic Systems
at Aalborg University in Denmark. She received
her Master of Science in Computer Engineeringand Management from Aalborg University in 2002,
and has since worked for the Danish Defense
and as a consultant specializing in enterprisearchitecture and cybersecurity. Research interests
include threat assessments and attack detection in
dedicated networks.

Tahir Riaz is working as an Assistant Professor in
the Section for Networking and Security (NetSec)
in the Department of Electronic Systems at Aalborg
University in Denmark. He received his Masterand PhD degrees in Electrical and Electronics
Engineering, specializing in Network Planning andManagement, from Aalborg University in 2004
and 2008, respectively. He has also worked in
Nokia, Linkoping, Sweden. He has authored or coauthored over 70 papers published in conferences
and journals. His research interests include accessand backbone fiber optic networks, network planning and design, architectureof next generation radio of fiber networks, reliability and QoS issues in largescale access and core network infrastructures, performance and optimizationin networks.

Jens Myrup Pedersen is working as an Associate
Professor and the head of the Section for Networking
and Security (NetSec) in the Department ofElectronic Systems at Aalborg University. His
current research interests include network planning,
traffic monitoring, and network security. He obtained
his Master of Science in Mathematics and Computer
Science from Aalborg University in 2002, and his
PhD in Electrical Engineering also from Aalborg
University in 2005. He is an author/co-authorof more than 70 publications in international
conferences and journals, and has participated in Danish, Nordic and European
funded research projects. He is also a board member of a number of companies
within technology and innovation.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 201226

Frequency Offset Estimation and Cell Search
Algorithms for OFDMA Based Mobile WiMAX
Fakher Eldin M. Suliman, Nuha M. Elhassan, Tertiel A. Ibrahim
Sudan University of Science and Technology, Khartoum, Sudan
fakhereldinmohamed@sustech.edu, nohamohamed3@gmail.com, terteil88@yahoo.com

Abstract-Frequency offset estimation is an important issue indigital transceiver design, especially for coherent wirelesstransmission such as in WiMAX systems based on the IEEE
802.16e orthogonal frequency-division multiple access (OFDMA)
due to inherited frequency and timing offset problems which
contribute to the loss of the transmitted data. To overcome these
problems, the transmitter and receiver must be well synchronized.
In WiMAX systems, the downlink synchronization involves
synchronization of carrier frequency and timing as well as
identification of the preamble index. This paper introducessynchronization algorithms for frequency offset estimation andcell search. The performance of these algorithms was tested using
simulation under adaptive white Gaussian noise and fading
channel for different values of signal to noise ratio. Simulation
provided accurate results and the frequency offset in the received
frame was successfully estimated.
Keywords-Carrier frequency, Cell search, Mobile WiMAX,
Orthogonalfrequency-divisionmultiplexing(OFDM),
Synchronization; Wireless metropolitan area network (WMAN)

technologies in terms of mobility and data rate.
The IEEE 802.16e-2005 specifications define a physical(PHY) layer and a medium access control (MAC) layer formobile and broadband wireless access systems operating at
microwave frequencies below 6 GHz [2]. Actually, threedifferent PHY layers are defined: single-carrier transmission,orthogonal frequency-division multiplexing (OFDM), andorthogonal frequency division multiple access (OFDMA).
OFDMA inherits from OFDM the ability to compensate channel
distortions in the frequency domain without the need of time
domain equalizers. In WiMAX systems based on the IEEE
802.16e orthogonal frequency-division multiple access(OFDMA) physical layer specifications; synchronization is an
essential issue. Thus without accurate synchronization
algorithms, it is not possible to reliably receive the transmitted
signal. Mobile WiMAX downlink (DL) synchronization
involves synchronization of carrier frequency and timing as wellas identification of the preamble index.
MobilityI.

INTRODUCTION

In view of the requirement of providing sufficient data rate
when the user is moving at high speed, the Institute of Electricaland Electronic Engineers (IEEE) has proposed the IEEE
802.16e standard to achieve a high speed broadband wirelessaccess network for future mobile wireless communication
systems [1]. The standard is widely known as WiMax, which isan acronym for Worldwide Interoperability for Microwave
Access.
The WiMAX network is considered as a Wireless
Metropolitan Area Network (WMAN) and is one of the
Broadband Wireless Access (BWA) techniques that haveemerged as a promising solution for last mile access technology.
Fig.1 shows positions of different existing wireless access
Manuscript received 30 April, 2012.
F. M. Suliman is now with the Department of Electrical Engineering, King
Khalid University, Abha, KSA (Tel: 00966551792532;
e-mail:fmsuliman@kku.edu.sa)
N. M. Elhassan is with the Department of Electronics Engineering, Sudan
University of Science and Technology, Khartoum, Sudan.
(e-mail: nohamohamed3@gmail.com)
T. A. Ibrahim is with the Department of Electronics Engineering, Sudan
University of Science and Technology, Khartoum, Sudan.
(e-mail: terteil88@yahoo.com )Vehicle -Pedestrian -2.5G
Mobile3G ile
b
Mo

3G +
ile
Mob A
P
H SD AP
H SU

TE
3G L XA
WiMEnterprise and Home Networking
Nomadic -Fixed Broadband Wireless AccessFixed -

Data rate (Mbps)

0.064

0.384

2.000

20.000156.000

Fig. 1. Positioning of different wireless access technologies in terms ofmobility and data rate.

Carrier frequency offset (CFO) may arise from thedifference in natural oscillator frequencies between the basestation (BS) and the mobile station (MS). However, OFDMA isextremely sensitive to timing errors and carrier frequency
offsets between the incoming signal and the local oscillator
used for signal demodulation.

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

Frequency offset in an OFDM system is introduced from twosources: mismatch between the transmitted and the received
sampling clocks and misalignment between the reference
frequencies of transmit and receive stations. Both impairments
and their effects on performance are analysed [3].
Carrier frequency errors between the transmitter and thereceiver of any digital communication system increase thenumber of errors in the received bits. These errors result fromthe mismatch between the carrier frequency oscillators of thetransmitter and the receiver in the RF section. Also, Doppler
frequency due to the receiver’s motion (up to 125 Km/hr)
contributes to frequency offset [4] [5]. Carrier frequency offset
has a great effect on OFDMA systems.
The OFDMA symbol depends on the subcarriers beingorthogonal. Each signal of a certain subcarrier should be
detected at the frequency of its maximum in the frequency
domain which is exactly the value of this subcarrier, whichmeets a zero from the signal carried on all other subcarriers. A
frequency offset will distort the signal leading to incorrect
decision and interference from all other subcarriers. If thetiming window slides to the left or the right, a unique phasechange will be introduced to each of the subcarriers. In thefrequency domain, if the carrier frequency synchronization isperfect, the receiver samples at the peak of each subcarrier,where the desired subcarrier amplitude is maximized, and theinter carrier interference (ICI) are zero. However, if the carrier
frequency is misaligned by some amount d, some of the desiredenergy is lost, and more significantly, inter carrier interference
is introduced. So it’s important to detect the frequency offset
that occur on the OFDM signal. The frequency offset-shift- canbe [6] [7]: Fine frequency offset: It’s a shift within one subcarrier
spacing.
 Coarse frequency offset: It’s a shift of multiple integer
number of subcarrier spacing.
II.

27

CP-based estimation technique depends on the repeated timesamples of the guard time. As shown in Fig.2, The cyclic prefix
samples are compared to their repeated part of the preamble
OFDMA symbol in the form of a correlation in time domain[8].

Fig. 2. Cyclic-prefix-based frequency estimation.

The proposed symbol timing technique depends on the CP
nature of the OFDMA symbols. The incoming packet slides
over two windows separated by fixed distance. The size of eachwindow is the same as the used CP. When the packet is detected,
the estimated start of the packet is shifted back and a correlation
between the fixed windows is turned on for certain period
within which the maximum correlation gives an estimate for thetrue symbol timing. Fig. 3 shows the illustration of thistechnique.

CYCLIC-PREFIX-BASED FINE FREQUENCY OFFSET
ESTIMATION

There are a number of well-known methods that are used toestimate the frequency offset, the most efficient of which isbased on exploiting time domain periodicity in the preamble.
For all OFDM systems, there is always the periodicity
involving the cyclic prefix. However, as will be shown later inthe paper, there is a performance degradation if the system uses
the cyclic prefix(CP) periodicity in estimating the frequency
offset in frequency selective fading channels. That's one of themain reasons preambles are periodic in OFDM based systems.
However, the preamble of the OFDMA mode of Mobile
WiMAX does not have a periodic portion if it is sampled at thecommonly employed Fast Fourier Transform (FFT) samplingrate. The OFDM theory requires the addition of a CP at the
beginning of the OFDM symbol to allow the receiver to absorb
the delay spread due to the multipath much more efficiently andto maintain frequency orthogonality. The CP occupies aduration called the guard time and is a temporal redundancy
that must be taken into account in data rate computations. The

Fig. 3. Symbol timing technique.

The estimated frequency offset is proved by the following
equations [9]:(1)
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 2012

If the number of time samples between the first time sampleof the guard band and its corresponding time sample inside the
OFDMA symbol is D= 1024 which is the complete FFT size,
Then to estimate the frequency offset, the following algorithm
may be applied:∑(2)

Where is the number of compared time samples, which must
be lower than the guard time. is the index time sample.
Then,
∑

(3)

and
∑

|

|

(4)

Then, the estimated frequency offset takes the form
(5)
The number of samples used for the estimation process ischosen to be less than half the guard band interval, starting fromthe beginning of its second quarter, to avoid the transient partaffected by the channel. The complete guard band cannot be
used because the errors in timing synchronization may lead to
not exactly determine the start of the guard band. Taking more
samples did not improve anything in the simulation results. The
minimum number of correlation to give acceptableperformance is determined. The limitation in this estimator isthe angle (Z) which has the range from to .

the conjugate of its predecessor with 3 samples apart.
For illustration, examine the following equation:
Ifis the received preamble in frequency domain,{}. This is on the assumption thatthe angles added by the channel on each 2 consecutive active
subcarriers are nearly the same, then multiplying by the
conjugate eliminate the channel’s effect.
2) In case of using the second method hard decision is tobe done to.
After estimating the fine frequency offset through the angle ofmaximum correlation - see equation (5) - compensating theoffset is through multiplying the preamble samples
byto correct the frequency offset.
However, there are some issues that should be noticed [11]:(i) The efficiency of the estimator is directly proportional to
the length of the repeated part.
(ii) The presence of multipath fading channel will
significantly reduce the accuracy of the estimator. The presenceof multipath will alter the cyclic prefix values of the preamble
with respect to the corresponding values in the symbol. As aresult, one would either consider a shorter duration of thepreamble which will decrease the estimation accuracy or accept
the distortion caused by the fading channel.
(iii) The phase rotation between the identical samples used toestimate the frequency offset should be in the range [−π, π),otherwise angle Z will fold by multiples of 2π and the estimate
will be incorrect.
III.

(6)
(7)
(8)
(9)
(10)
Where
is the sampling frequency and D equals to the FFT
size = 1024 samples. The limitation on the angle fromtomakes it impossible for the algorithm to estimate the coarse
frequency offset which is multiple from the , which means it ismultiples ofor .
Another conclusion from the implementation point of viewis that the use of this correlation based algorithm will allow a
hardware reuse as it’s the same correlation needed for symbol
start algorithm. Also both fine frequency estimation andsymbol start need the same basic operation, which is correlation[10]. To prepare the received preamble for correlation withstored preambles, the following steps are executed:
1) The channel effect on the received preamble must be
reduced. This is done by multiplying each sample by

28

JOINT DETECTION OF INTEGRAL CARRIER FREQUENCYOFFSET AND CELL SEARCHThis algorithm considers joint detection of integral CFO
and preamble index, under the assumption OFDM symbol
boundaries and fine frequency offset have been acquired toreasonable accuracy. This assumption is valid with the timing
and fine frequency offset simulation results. Based on an
optimization formulation, a number of detection methods are
driven of different complexity and optimization methods. The
methods exploit the quasi-orthogonality among the OFDMAWiMAX preamble sequences as well as the organization of thenonzero subcarriers in the preambles [5]. Simulation results arepresented to illustrate the performance of the methods, seesection IV. This algorithm detect the integer frequency offset inrange [-9 9 frequency offset pins] by the correlation in
frequency domain between the received preamble -that is oneof the 114 preambles- and the 114 preambles stored in thereceiver. The max correlation indicates the most probablepreamble sent, and then we can use it to calculate the integer
frequency offset.
If the spacing of the nonzero subcarriers in the preamble ismuch smaller than the coherence bandwidth of the channel,
then the channel responses at neighbouring preamble
subcarriers are approximately equal, mathematically, this canbe given by:(11)
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 2012

| |Where |
preamble subcarriers.
and,
{
}

}

|

is an index for nonzero

{

|
|
(12)
Where,
Q (k) is the received preamble in the frequency domain.
is the stored preamble of index (j) in the frequency.
Preamble (j) multiplied by ashifted version of one active subcarriern is the integral frequency offset normalized to the subcarrier
spacing.
The normalized integral frequency offset means thatmultiplying each active subcarrier by the conjugate of its
predecessor will cancel the channel-added phase and then wecan correlate the preamble patterns shifted by the expectedvalues of the integral carrier frequency offset and select the
maximum correlation as follows:
∑

{

}

29

 The area of consideration (-fs /2 to fs /2) is estimated 500
times for each dB, where fs is subcarrier spacing.
 The upper line at 2% represents the end range of error
stated by the standard, for frequency offset error inestimation.

TABLE I
WiMAX OFDM Parameters
SymbolDescription

Relation

Used value

B

Nominal bandwidth

B = 1/Ts

0 MHz

Size of IFFT/FFT1024

Numbersubcarriers

of⁄

Guard fraction
Sampling frequency⁄

11.2 MHz

Sample time⁄89.2 nano sec(13)

Guard band

256

Where {} is called the differential signal.
The estimated integral CFO and preamble index are given by:

Guard time

22.8 sec

OFDM symbol time

114.2 sec

Subcarrier spacing

10.94 KHz.

̂ ̂

(14)

It should be noted that, the coarse frequency offset isusually estimated during the Cell-ID detection phase. Cell-ID
detection is used to detect the preamble sequence beingtransmitted by the operating base station. Cell-ID detection isusually performed after fine frequency offset detection andcorrection. The Cell-ID detection block correlates the received
frequency domain signal with the possible sequences. The
correlators should take into consideration the possible shift, therange of which is determined by the allowed frequency offset
defined in the standard. The Cell-ID detection block estimatesthe coarse frequency offset value and the detected preamble
sequence [11].
IV.

As shown in Fig.4, Fig.5, Fig. 6 and Fig.7 the estimated error
percentage is less than half of the maximum allowed error ,thismeans the cyclic prefix based fine frequency algorithm haveacceptable performance in frequency offset estimation.

ALGORITHMS SIMULATION RESULTS

This section provides a detailed explanation and discussionof the simulation results of each algorithm. Those algorithms
are simulated in ideal, AWGN, and dispersive fading channel
with fixed point analysis. Each algorithm was simulated under
fixed point analysis with the minimum number of bits achievedfor each operation with acceptable performance. The
parameters used for simulation are tabulated in Table 1.
Fig. 4. Fine frequency offset estimation with different SNR under AWGN.

A. Cyclic-Prefix-Based Fine Frequency Offset Estimation
To compute the percentage of errors the following
conditions have been applied: Number of runs for x-axis = number of runs for y-axis=1000 runs.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 201230

B. Joint-Based Coarse Frequency Offset Estimation

Fig. 5. Fine frequency offset estimation with different 16-bit fixed point
analysis.

Fig. 6. Fine frequency offset estimation with different 18-bit fixed point
analysis.

The following figures illustrate the performance of thecoarse frequency offset algorithm through histograms underfading and AWGN. The conditions applied here are:
 Number of runs = 250 run.
 Number of integral carrier frequency offset tested = 6
 In these histograms, it’s taken into consideration to try
preambles of different segments, to make sure it works forall 114 preamble patterns, thus different segments.
From the simulation result of joint based frequency coarse
offset figures shown below, when the algorithm is applied
under fading at 2 dB the estimated coarse frequency shift isequal to 6. This means that the algorithm was accurate at 246
runs; this is illustrated in Fig. 8 and Fig. 9. In Fig. 10 SNR was
increased to 10 dB and the performance increased to 250 runs.
Cell search results under fading at 2dB, 5 dB, and 10 dB aregiven in Fig. 11, Fig. 12, and Fig. 13 respectively. The
preamble number is equal to 90 at both 246 and 250 runs.

Fig. 8. Integral frequency offset estimation under fading at 2dB.

V.

Fig. 7. Fine frequency offset estimation with different 20-bit fixed point
analysis.

CONCLUSIONSIn OFDMA based mobile WiMAX, the receiver must alignits carrier frequency as closely as possible to the transmitted
carrier frequency. In this paper, the fine and coarse frequency
offset estimation algorithms by using the packet preamble
structure adopted by the IEEE 802.16 standardization
workgroup have been presented and simulated. Joint detectionof the coarse frequency offset and the cell search under fading
and AWGN was obtained. The simulation results of thesealgorithms accurately estimated the frequency offset in thereceived frame. For future work, the whole transceiver systemof the WiMAX can be simulated and implemented applying thesame parameters. Also, the performance of these algorithms
can be tested under different types of noise and channels.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 201231

ACKNOWLEDGMENT
Authors would like to thank the Department of Electronics
Engineering, Faculty of Engineering, Sudan University of
Science and Technology where this work has been carried.

Fig. 11. Cell search results under fading at 2dB.

Fig. 9. Integral frequency offset estimation under fading at 5 dB.

Fig. 12. Cell search results under fading at 5 dB.

Fig. 10. Integral frequency offset estimation under fading at 10 dB.

Fig. 13. Cell search results under fading at 10dB.

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

REFERENCES
[1]
[2]
[3]

[4]
[5]

[6]
[7]
[8]
[9]

[10]

[11]

C. Ball, LTE and WiMax Technology and Performance Comparison,
Nokia Siemens Networks, EW2007 Panel, 2007.
B. Sidhu, H. Singh,, and A. Chhabra, Emerging Wireless Standards
WiFi, ZigBee and WiMax, WASET.ORG, 2007.
Jeffrey G. Andrews, Arunabha Ghosh, and Rias Muhamed,
Fundamentals of WiMAX: understanding broadband wirelessnetworking, Pearson Education. Inc, 2007.
Juha Heiskala and John Terry, OFDM wireless LANs A theoretical andpractical Guide, Sams Publishing, 2002.
Kun-Chien Hung and David W. Lin,Joint “Detection of integral carrier
frequency offset and preamble index in OFDMA WiMAX downlink
synchronization” , IEEE Proc, WCNC2007.pp. 1959-1964.
Kwang-Cheng Chen and J. Roberto B. de Marco, Mobile WiMAX , John
Wiley & Sons. Ltd, 2008.
kwang-cheng chen, Introduction to Mobile Wimax, Taiwan university,
2007.
Loutfi Nuaymi, WiMAX technology for broadband wireless access, John
Wiley and Sons Ltd, 2007.
M. Morelli and M. Pun, “Synchronization Techniques For Orthogonal
Frequency Division Multiple Access (OFDMA) A Tutorial Review",
Proc. IEEE, July 2007, Vol. 95, no. 7. pp. 1394 – 1427, doi:
10.1109/JPROC.2007.897979.
Mohamed Ismail Ali, “Simulation and implementation of the frequency
synchronization and Viterbi decoding for the OFDMA-based Mobile
WIMAX 802.16e”, a thesis submitted to the Faculty of Engineering at
Cairo University, 2007.
Khairy, M. M., “A novel frequency offset estimation technique for
Mobile WiMAX”, Eur. Trans. Telecomm., 2011, Vol. 22. pp. 45–50.
doi: 10.1002/ett.1462

32

MS.Terteil Abdalla Abrahim was born in Sudan on De
c,28, 1987
She did her Bsc. In 2009 in Electronics Engineering (Communications), College of Engineering SUST
Since 2012 she is a Teaching Assistant in the School ofElectronics, College of Engineering, SUST anda part time teaching assistant at the Electrical and Electronic Department, College of Engineering,International Of Africa University since 2013 until now.
She published two technical papers in international conferences.
Her Research Interest includes: performance analysis and performance enha
ncement for the following:
Wireless Communications and Embedded Systems.
Ms.Terteil A. Abrahim is an Engineer in the Sudanese Engineering Council.
Email: terteil88@yahoo.com.
Tel: (+249) 922272954

Dr. Fakher Eldin Mohamed Suliman was born in Sudan in 1966.
He did his PhD, MSc, and BSc in 2004, 1999, and 1989
respectively, all in Electrical Engineering.
He is an assistant professor in the Electronics Engineer
ing Department, College of Engineering,
Sudan University of Science and Technology (SUST) sin
ce 2004.
He is the General manager of the Electronics System Research Center (ESRC)
at SUST.
He is the coordinator of the postgraduate studies in his college.
He published a number of technical papers in international conferences andhas two books under publication process.
His research interest includes; performance analysis and performanceenhancement for Optical fiber systems, Wireless communications systems,
and Switching systems.
Dr. Suliman is a Specialist Engineer in the Sudan Engineering Council and a
Full Member of the Sudan Engineering Society.
Nuha M. Elhassan was born in Jeddah, Kingdom of Sau
di Arabia, in 1988. She received the B.S. degrees in co
mmunication engineering from SUST in 2009 .
From 2010 up to now, she is a Teaching Assistant in SU
ST, University of Medical Science and Technology andKhartoum Collage of Technology.
Her Research Interest includes: performance analysis and performance enhancement for wirelessCommunications systems
Ms Elhassan is an Engineer in the Sudanese Engineering Council
Email: nohamohamed3@gmail.com
Tel: +249912493098

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 201233

Design and Performance Analysis of a newly
designed 32-User Spectral Phase Encoding
system operating at 2.5Gb/s for Fiber-Optic
CDMA Networks
Savita R.Bhosale* Mr. S.B.Deosarkar **Dr. BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY, LONERE, TAL. - MANGAON,
DIST. - RAIGAD, PIN – 402103
Correspondence should be address to sr4bhosale@gmail.com and svt4bhosale@rediffmail.com
Abstract - Multiple access techniques are required to meet thedemand for high-speed and large-capacity communications in
optical networks, which allow multiple users to share the fiber
bandwidth. Optical code-division multiple-access (O-CDMA) is
receiving increased attention due to its potential applications forbroadband access networks. We analyze a new technique forencoding and decoding of coherent ultra short light pulses. In
particular, we discuss the temporal pseudo noise bursts generated
by spectral phase coding of ultra short optical pulses.
This paper describes a performance analysis of Spectral Phase
Encoding optical code-division multiple-access scheme based on
wavelength/time (W/T) codes and random phase codes. We havestudied the optical simulator Encoding/Decoding at different fiber
lengths & gain in terms of Quality factor (Q) and Bit Error Rate
(BER) performance. We derive the bit error rate (BER) and QoS
as a function of data rate, number of users, receiver threshold. We
find that performance improves dramatically with optical power
normalizer.Ultrashort light pulse CDMA could provide tens tohundreds of users with asynchronously multiplexed, random
access to a common optical channel. The system supports 32 users
while maintaining bit-error rate (BER) < 10−9 and required QoS
for the correctly decoded signal at 2.5 Gbits/s bit rate.

correlation. Optical signal processing would be advantageous
because potentially it can be much faster than electrical signal
photon-electron-photon conversions. Several new classes of
optical networks are now emerging [1]. For example,
code-division multiple access (CDMA) networks using opticalsignal processing techniques were recently introduced [2]-[9].
CDMA is a type of spread spectrum communications [10] inwhich multiplexing is achieved by assigning different,
minimally interfering code sequences to different user pairs. In
fiber optic CDMA, users communicate by imprinting theirmessage bits upon their own unique code, which they transmit
asynchronously (with respect to the other transmitters) over acommon channel. A matched filter at the receiver end ensuresthat data are detected only when they are imprinted on theproper code sequence (see Fig. 1). This approach tomultiplexing allows transmission without delay and handles
multi-access interference (contention) as an integral part of themultiplexing scheme.

Keywords : BER, ISD, MAI, NRZ, OCDMA, OOC, PSO,
QoS, RZ.

I. INTRODUCTIOND

ue to economic advantages, maturing technology, andhigh information capacity, single-mode fiber- optic
transmission media will be embedded in future
telecommunications networks. A desirable feature for thesefuture optical networks would be the ability to process
information directly in the optical domain for purposes ofmultiplexing, demultiplexing, filtering, amplification, andManuscript received June 30, 2012.
Savita R. Bhosale is with Dr. Babasaheb Ambedkar Technological
University, Lonere, Raigad. (MS) India Pin- 402103. M:9869579071,
Email: sr4bhosale@gmail.com , svt4bhosale@rediffmail.com
Dr. S. B. Deosarkar is with Dr. Babasaheb Ambedkar Technological
University, Lonere, Raigad. (MS) India Pin- 402103

Fig. 1. Block diagram of Optical CDMA Network

In coherent OCDMA, encoding and decoding are performed
either in time domain or in spectral domain based on the phaseand amplitude of optical field . In coherent time spreading (TS)OCDMA, where the encoding/decoding is performed in time
domain. In such a system, the encoding is to spread a short
optical pulse in time with a phase shift pattern representing
specific codes. The decoding is to perform the convolution to
the incoming OOC using a decoder, which has an inverse phaseshift pattern as the encoder and generates high levelauto-correlation and low level cross correlations.

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

II. NUMERICAL SIMULATIONThe encoders use delay line arrays providing delays in terms ofinteger multiples of chip times. The placement of delay line
arrays and the amount of each delay and phase shifts are
dictated by the specific of the signatures. PSO matrix codes areconstructed using a spanning ruler or optimum Golomb ruler isa (0,1) pulse sequence where the distances between any of thepulses is a non repeating integer,hence the distances between
nearest neighbors, next nearest

34

semicolons separate the timeslots in the code. (The codes M1
and M9 are shown in bold numerals.)We focus on codes likeM1 because it shows extensive wavelength reuse, and on codes
likeM9 because it shows extensive time-slot reuse. It is theextensive wavelength and time-slot reuse that gives thesematrix codes their high cardinality and high potential ISD.
Four mode-locked lasers are used to create a dense WDM
multi-frequency light source. Pseudo-orthogonal (PSO) matrix
codes [3] are popular for OCDMA applications primarily
because they retain the correlation advantages of PSO linear
sequences while reducing the need for bandwidth expansion.
PSO matrix codes also generate a larger code set. An
interesting variation is described in [1] where some of the
wavelength/time (W/T) matrix codes can permit extensive
wavelength reuse and some can allow extensive time-slot reuse.
In this example, extensive time-slot reuse sequence is used.
There are four time slots used without any guard-band giving
the chip, period of 100 ps. Code1,code 5,code3
and code9 codes are used for time spreading. Code set toapply binary phase shift mapped as M1:{1;0;1;0;1;1;1;1}
M2:{1;0;1;1;1;1;1;1}…………………. M32:{0;0;1;1;1;1;1;0}(1 represents as a π phase shift, 0 represents as no phase shift)
TABLE I
THE 32 PSO MATRIX CODES INTERPRETED AS W/T MATRIX CODES

Wavelengths(W)
λ1
Fig. 2. Constructing the four pseudo orthogonal (PSO) matricesM1. . .M4 fromthe single optimum Golomb ruler g(1,7).

neighbors, etc., can be depicted as a difference triangle withunique integer entries. The ruler-to-matrix transformation
increases the cardinality (code set size) from one (1) to four(4)and the ISD (=Cardinality/CD)from 1/26 to 4/32=1/8. The ISD
translates to bit/s/Hz when the codes are associated with a data
rate and the code dimension is translated into the bandwidth
expansion associated with the codes as follows:

λ2
λ3
λ4
λ5
λ6
λ7
λ8

1
1,9,
17,25
2,10,
18,26
3,11,
19,27
4,9,12,
20,28
5,10,13,
21,25,29
6,11,14,
22,26,30
7,12,15,
23,27,31
8,13,16,
24,28,32

Time slots (S)
2
3
1,14,
19,24,
29
26
2,15,
20,25,
17,30
27
3,16,
1,21,
18,31
26,28
4,19,
2,22,
32
27,29
3,23,
5,20
28,30
4,17,24,
6,21
29,31
7,17,
5,9,18,
22
30,32
8,18,
6,9,10,
23,25
19,31

4
1,7,10,
11,20,32
2,8,11,
12,21
3,12,
13,22
4,13,
14,23
5,14,
15,24
6,15,
16
7,16
8

TABLE IISPE O-CDMA SYSTEM PARAMETERS USED FOR SIMULATIONThe enhanced cardinality and ISD, while preserving the OOC
property, are general results of the ruler-to-matrix
transformation. We can convert the PSO matrices towavelength/time (W/T) codes by associating the rows of the
PSO matrices with wavelength (or frequency) and the columns
with time-slots, as shown in Table I. The matricesM1….M32
are numbered 1…32 in the table, with the corresponding
assignment of wavelengths and time-slots. For example, code
M1 is (λ1 ; λ1 ; λ3; λ1 ) and M9 is ( λ1,λ4;0;λ7,λ8;0); here theParameter
Code length
Channel spacing
Wavelength
Chip time
Chip rate
Bit rate
Modulation
FormatFiber length
MeasurementsValue
8
0.4 nm4 at 1550,1550.4,1550.8,1551.2 nm
41.25E-10
2.5 Gbits /s
NRZ and RZ
60 to 180 km
Eye diagram, Bit error rate and Quality
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 2012III.

PROPOSED SCHEME SPE O-CDMA1) Lasers (mode locked laser)2) Encoders 3) Multiplexers 4)
Optical fiber of 60 to 180 km length 5) De multiplexers 6)
Decoders 7) Receiver 8)BER analyzer 9) Eye Diagram
analyzer 10) Signal analyzer The simulation setup for Spectral
Phase Encoding Optical CDMA is shown in figure 3. The MLL
is used to generate four wavelengths, range from1550 nm to1551.2 nm, with 0.4nm wavelength spacing, this carrier signal
is used to modulate the pseudo random bit sequence (PRBS)
data of the user. An intensity modulator which is External
Modulator uses on-off keying modulation to modulate themultiplexed 4 wavelengths according to the NRZ and RZ
electrical data. For analysis, Eye Diagram analyzer, Beat Error
tester and Signal analyzer is used.

IV.

35

Fig. 4. Wavelength Spectrum for Spectral Phase Encoding Optical CDMA for
32 Users

SIMULATION OF SPE O-CDMA SYSTEM ONE USER

Fig. 5. Modulated data before encoder of User 1

Figure 5 shows modulated data before encoding.
The encoded data from all users are multiplexed by Optical
MUX and then passed through a 60 km and 180 km span ofstandard single mode optical fiber followed by a loss
compensating optical amplifier which is Opt Amp. The output
signal from a fiber span is then passed through OptSplit1 tosplit the signal and routed to the user’s decoder. The decoder
uses optical filters and inverse delay line arrays providing
delays in terms of integer multiples of chip times and phaseshift pattern. The decoded signal finally arrives at opticalreceiver (Receiver), BER Test and Eye Diagram. Eye diagram
analyzer has been used to take the plot of Eye pattern at thereceiver end. Bit error rate values for different number oftransmitting users have been taken from BER Tester.

Fig. 3. Simulation setup for SPE O-CDMA Transmitter and Receiver of User1

Figure 4 shows dense wavelength spectrum for four
wavelengths respective encoders, which have been assigned aunique W/T code respective to each encoder.

The system has been redesigned for different number of users.
In spite of the use of orthogonal codes, the main effect limiting
the effective signal-to-noise ratio of the overall system is theinterference resulting from the other users transmitting at thesame time, which is called Multiple Access Interference (MAI).
MAI is the major source of noise in OCDMA systems. System
performance is tested at 2.5 Gbits/s bit rate, NRZ and RZ data
modulation format, BER and quality factor at different data
modulation format noted. Eye diagram observed at different
fiber length.

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

36

V. PERFORMANCE ANALYSIS

Fig. 6. BER and Quality factor at User1 using Optical Power Normalizer

Fig. 7. BER and Quality factor at User16 using Optical Power Normalizer

Fig. 8. BER and Quality factor at User32 using Optical Power Normalizer

Fig. 9. BER and Quality factor at User1 using Optical Attenuator

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 1, July 201237

Fig. 10. BER and Quality factor at User16 using Optical Attenuator

Fig. 11. BER and Quality factor at User32 using Optical Attenuator

Figure 6,7, 8, 9,10 and 11 shows BER and Quality factor of
SPE O-OCDMA system using optical attenuator and opticalpower normalizer at User1,User8,User16 and User32
respectively. As active number of users increases system
performance degrades. System performance is analyzed at 2.5
Gb/s bit rate, -20 dB received power and 60km to 180 km fiber
span. System performance is extremely good by using opticalpower normalizer. Spectral phase encoding O-CDMA systemusing optical attenuator and optical power normalizer system
supports 32 users at 2.5Gb/s and offer s low Bit Error Rate andgood quality of service.

Figure 12 and 13 shows System performance degrades as fiber
length increases .The SPE O-CDMA system offers High
Quality factor and extremely less BER at -20 dBm received
power and over 60km to 180 km fiber length.
Eye opening is good using optical power normalizer ascompare to optical attenuator. Eye diagram analysis is carriedout at 60 km fiber span and at 180 km fiber span.SPE OCDMA
system using optical power normalizer performance is good ascompare to optical attenuator over 60km and180 km fiber span.

Fig. 12. Eye Diagram analysis at User32 using Optical Power Normalizer over
60 km fiber span

Fig.13. Eye Diagram analysis at User32 using Optical Power Normaliszr
over180 km fiber span

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

Fig. 14. Eye Diagram analysis at User32 using Optical Attenuator over 60 km
fiber span

38

Fig. 15. Eye Diagram analysis at User32 using Optical Attenuator over
180 km fiber span

Figure 12,13,14 and 15 shows System performance degrades as fiber length increases .The SPE O-CDMA system offers highQuality factor and extremely less BER at -20 dBm received power and over 60 km to 180 km fiber length using optical power
normalizer.

Fig. 16. BER and Quality factor at User1 using Optical Attenuator for RZ data modulation format

Figure 16, 17 and 18 shows SPE O-CDMA system performance of RZ data modulation format, Results indicates enhancement in
BER and quality factor for NRZ data modulation format as compared to RZ data modulation.

Fig. 17. BER and Quality factor at User16 using Optical Attenuator for RZ data modulation format

Fig. 18. BER and Quality factor at User 32 using Optical Attenuator for RZ data modulation format

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

Active Users

Active Users
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
1.00E-09
1.00E-13
1.00E-17

BER

BER

1.00E-21
1.00E-25
1.00E-29
1.00E-33
1.00E-37
1.00E-41
1.00E-45
1.00E-49

RZ Modulation format
NRZ Modulation format

Fig. 19. System performance for RZ and NRZ data modulation format for
32Users

Figure 19 and 20 represents graphical representation of Beat
Error rate and Quality factor of SPE O-CDMAsystem .System performance is analyzed for NRZ and RZ
data modulation format and using optical attenuator andoptical power normalizer for 32 numbers of active users.
Results indicates enhancement in BER and quality factor for
NRZ data modulation format as compared to RZ data
modulation format, low BER and high Quality of service
using optical power normalizer.Beat Error Rate of SPE
OCDMA system using Optical Power Normalizer at User1and User32 is 1.00E-70 and 1.00E-16 respectively, while
SPE OCDMA system using Optical Attenuator at User1 andUser32 is 1.00E-49 and 1.00E-13 respectively, for NRZ and
RZ data modulation format BER for NRZ is at User1 andUser32 is 1.00E-49 and 1.00E-13 respectively, for RZ
format at User1 and User32 is 1.00E-17 and 1.00E-09
respectively.

1.00E-10
1.00E-15
1.00E-20
1.00E-25
1.00E-30
1.00E-35
1.00E-40
1.00E-45
1.00E-50
1.00E-55
1.00E-60
1.00E-65
1.00E-70

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Optical Attenuator
Optical Power Normalizer

Fig. 20. System performance using Optical Power Normalizer and Optical
Attenuator in terms of Quality factor for 32 Users

REFERENCES
[1]

[2]
[3]

[4]

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[6]

[7]

V. CONCLUSION

The multiple access interference effect was also seen at the
optical receiver end in optical CDMA which degraded theefficiency of system by increasing bit error rate. Use ofspectral phase encoding O-CDMA system reduced the MAI
as seen in the bit error rate performance and quality factor.
The spectral phase encoding O-CDMA system performanceis good for 32 users at 2.5Gbits/s bit rate using optical power
normalizer. The performance of SPE O-CDMA system isanalyzed by using NRZ and RZ data modulation format,
while NRZ data modulation format offers extremely good
performance than RZ data modulation format. The SPE
O-CDMA system has been successfully demonstrated at
system capacity of 80 Gbits/s over 180 km of fiber length.
This newly designed SPE O-CDMA offers high Quality
factor and less Beat Error Rate <10-9 .Moreover these results
are more realistic as practical impairments have been
considered with -15 dB and -20 dBm received power for
optical attenuator, optical power normalize respectively andfor permissible BER of 10-9.

39

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40

Shankar B. Deosarkar
Shankar B. Deosarkar Received his graduate degree
in electronics engineering in the year 1988 from
Amravati University, his M.Tech and Doctorate
degree in the area of Microwave Communication in
the year 1990 and 2004 respectively from S.G.G.S.
Institute of Engineering and Technology, Nanded.
At present he is guiding three Research Scholars in
the area of EMI / EMC and Microstrip Antenna
Design. Presented his research contribution at IEEE International
Conferences at IIT’S, USA, UK, CANADA, ITALY andSINGAPOORE.He had delivered invited talks at McGill University,
Montreal, Canada, Electromagnetic Research Center Ottawa, Governmentof Canada and Princeton University, New Jersey, USA and UGC / AICTE
refresher courses. He is also been member of the programme committee atthe various international conferences and reviewer of few books of McGraw
Hill and PHI publications in the area of Microwave Communication as wellreviewer of several national and international IEEE Conferences in the areaof Microwave Communication,optical fiber communication.

Savita R.Bhosale Research Scholar Dr.Babasaheb
Ambedkar Technological University Lonere(MS) ,India.Savita R.Bhosale received the B.E.
degree in electronics and Telecommunicationengineering from Dr.Babasaheb Ambedkar
Maratwada University, India in 1993, and finishedthe M.Tech. degree course in electronics andTelecommunication
Engineering from Dr.Babasaheb Ambedkar
Technological University Lonere (MS), India in 2006. Since 1998, She wasworked as Lecturer and from 2006 working as Assistant Professor till datein MGM’s college of Engineering and Technology, India. Her researchinterests include Telecommunication and, optical fiber communication,
optical networking, wireless and mobile communication.

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