International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014 www.seipub.org/ijepr doi: 10.14355/ijepr.2014.0303.03
A survey on Resource Optimization Techniques of MC‐CDMA System used in 4G Wireless Mobile Communication systems Suriavel Rao R S*1, Malathi P2 Department of Information and Communication Engineering, Guindy, Anna University, Chennai, India Principal, Bharathiyar Institute of Engineering for Women, Deviyakurichi, Salem, India
1 2
*1
selvakrishnavel@gmail.com; 2drpmalathi2012@gmail.com
Received 20th Mar. 2013; Accepted 15th May 2013; Published 12th Aug. 2014 © 2014 Science and Engineering Publishing Company
Abstract Multi‐Carrier Code Division Multiple Access (MC‐CDMA) is becoming a very promising and robust multiple access technique to fading channels. This paper is mainly focused on reviewing different sub‐carrier selection techniques for MC‐CDMA system and highlighting the best one among them. It has been found that appropriate subcarrier selection strategy can significantly improve BER performance, Throughput performance, and data rate with higher spectrum. Here, three subcarrier selection techniques viz., MOPSO, modified SSK, and slantlet transform (SLT) are extensively analysed and at the end the slantlet transform was found to be superior to the rest of them. Keywords MC‐CDMA; MOPS;, SL; SSK; SNR; BER
Introduction 4G systems Future generations of broadband wireless systems will support a wide range of services and bit rates in a bandwidth of the order of tens or even hundreds of megahertz. FCC has mandated that the UWB radio transmission lies between 3.1 and 10.6 GHz, with a minimum bandwidth of 500 MHz. The most important objectives in the design of 4G wireless systems are to address the severe inter symbol interference (ISI) resulting from the high data rates, and to utilize the available bandwidth in a spectrally efficient manner. Multicarrier code division multiple access system (MC‐CDMA) has been considered as the potential candidate to mitigate the ISI effect and fading channels. Sub‐carrier selection in MC‐CDMA system is
employed in to minimize the effect of high Doppler frequency shift which is otherwise the concerning issue because of the wide transmission bandwidth requirement. Multicarrier CDMA Fourth generation wireless communication demands a better multiple access technique for reducing the multiple access interference (MAI) and inter‐symbol interference (ISI) and to improve the bit error rate performance. It is pointed out by G.K.D.Prasanna venkatesan in that, MC‐CDMA is proved to be the best candidate which satisfies the demands of 4G wireless communication. The conventional code division multiple access (CDMA) technique used in third generation system faces serious limitations by channel dispersion causing inter symbol interference (ISI), and it requires advanced signal processing algorithms to implement it. The MC‐CDMA employing multiple stream of data channel can combat channel dispersion, hence ISI, thereby increasing system capability to accommodate a higher number of users and its data rate requirements. In MC‐CDMA the transmitter spreads each parallel sub stream of data generated with the aid of serial‐to‐ parallel (S‐P) conversion given Np‐chip spreading code, {c[0], c[1], …, c[Np – 1]} As seen in Fig. 1, the transmitted MC‐CDMA signal using BPSK modulation can be expressed as sMC (t )
2 P M U 1 N P 1 bi [u ] C[ j ]. UN P i M u 0 j 0
(1)
PTs (t iTs ) cos[2 ( f c F jU u )t ]
where P and f c represent the transmitted power and
59
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014
carrier frequency respectively, and the processing gain (spreading factor) of N Tb / Tc represents the number of chips per bit. Tb and Tc represents the bit duration and chip duration, respectively. Furthermore, from Eqn. 1, a quantity of 2M + 1 represents the number of bits conveyed by a
transmitted data burst, b[i] 1, 1 is the i th transmitted bit, while c[ j ] 1, 1 is the j th chip of the spreading code, and finally, P (t ) represents the
chip waveform defined over the interval [0,τ). In Eq. 1, U represents the number of bits that are serial to parallel converted, where each transmitted symbol contains U data bits, 2M + 1 represents the number of U ‐ bit symbols conveyed by a transmitted data burst, and bi [u ] 1, 1 represents the uth bit of the ith transmitted symbol. A basic block diagram of MC‐ CDMA system is shown in Fig. 2.
FIG. 1 POWER SPECTRA & TIME DOMAIN SIGNAL OF MC‐ CDMA
own flying experience and its companions’ flying experiences. It is widely reported that PSO algorithm is very easy to implement to solve real world optimization problems and has fewer parameters to adjust when compared to other evolutionary algorithms. The information sharing mechanism among the particles in PSO is significantly different from the information sharing among the chromosomes in genetic algorithm (GA). In GA, the entire group moves towards an optimal solution area. However, in PSO only the global best or local best solution is reported to other particles in a swarm. Therefore, evolution only looks for the best solution and the swarm tends to converge to the best solution quickly and efficiently. Gheitanchi et al., have applied PSO for subcarrier allocation in OFDMA systems with significant reduction of computational complexity and increased flexibility compared to conventional techniques, whereas in Chakravarthy et al., it was shown that PSO resource allocation techniques improved delay characteristics while maintaining fairness and throughput utilization. Shu’aibu’s approach uses 22.5 % less CPU time than other techniques. Yang Yi et al., have used a combination of GA and PSO in the subcarrier and power allocation with improved performance than normal PSO. Ahmed has shown that the Differential Evolution techniques is better than PSO but takes more time to converge. In literature, the use of PSO for OFDM resource allocation have so far been based on single objective optimization technique, we are therefore investigating its extension to handle multi‐objective problems. Multi‐Objective Particle Swarm Optimization
FIG. 2 ADAPTIVE MC‐CDMA SYSTEM
Overview Of Multi-Objective Particle Swarm Optimization In PSO, each particle flies in the search space with a velocity which is dynamically adjusted according to its
60
PSO is a population‐ based stochastic optimization technique developed by Eberhart and Kennedy inspired by the social behaviour of flocks of bird. The PSO is initialized with a population of random solutions and this initial population evolves over generation to find optima. In PSO each particle in the population has a velocity, which enables it to fly through the problem space. Therefore, each particle is represented by a position and velocity vector. Dimensions of position and velocity vectors are defined by the number of decision variables in optimization problem. Modification of the position of a particle is performed by using its previous position information and its current velocity. Let xi (t ) denotes the position of particle pi at time step t . The position
International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014 www.seipub.org/ijepr
However, in the case of multi‐objective optimization problems, each particle might have a set of different leaders from which just one can be selected in order to update its position.
of pi is then changed by adding a velocity vi (t ) to the current position, i.e.,
xi (t ) xi (t 1) vi (t )
(2)
According to the value of the objective function, each particle knows its best position ever (personal best ‐ pbest) and (global best ‐ gbest) among all personal bests. For a single objective problem, the result of optimization problem will be the position vector of gbest at final iteration. These principles can be formulated as
vi (t ) vi (t 1) C1r1 ( x pbest xi (t )) C2 r2 ( x gbest xi (t )) (3) where C1 and C2 are the cognitive and social learning factors (usually defined as constant), and r1 , r2 [0,1] are random values. The constants C1 and C2 are normally assigned with value equal to 2 for all applications. The version of PSO given above is not suitable for solving multi‐objective optimization problems since there is no absolute global minimum. Therefore, the algorithm needs some modification to locate the Pareto front in multi‐objective optimization problems. With multi‐objective optimization we aim to find a set of different solutions (the Pareto Optimal set) with a single run and this is achieved by maximizing the number of elements of Pareto optimal set, minimizing the distance of the Pareto front produced by our algorithm with respect to the true (global) Pareto front and maximizing the spread of solutions found, so that we can have a distribution of vectors as smooth and uniform as possible. The following issues are also considered when extending PSO to multi‐objective optimization.
Selection of particles (to be used as leaders) in order to give preference to non dominated solutions over those that are dominated. Retention of non dominated solutions found during the search process in order to report solutions that are non dominated with respect to all the past populations and not only with respect to the current one. It is desirable that these solutions are well spread along the Pareto front. Maintenance of diversity in the swarm in order to avoid convergence to single solution. When solving single objective optimisation problems, the leader that each particle uses to update its position is completely determined once a neighbourhood topology is established.
Coello et al., have proposed the idea of having an external archive in which every particle will deposit its flight experiences after each flight cycle. The updates to the external archive are performed considering a geographically‐based system defined in terms of the objective function values of each particle. The search space explored is divided on hypercube. Each hypercube receives a fitness value based on the number of particles it contains. Thus, in order to select a leader from each particle of the swarm, a roulette wheel selection using these fitness values is first applied,to select the hypercube from which the leader will be taken. Once the hypercube has been selected, the leader is randomly chosen. This approach also uses a mutation operator that acts both on the particles of the swarm, and on the range of each design variable of the problem to be solved. We have incorporated the mechanism of crowding distance computation into the algorithm specifically on global best selection and in deletion method of external archive of non‐dominated solutions as suggested in the works of C.R. Raquel et al., and Deb. Overview Of Modified Ssk Algorithm 1) Traditional SSK Algorithm Considering a generic Nt N r MIMO system, then we can display basic steps of SSK algorithm as follows.
The transmitter encodes blocks of log 2 ( N t ) data bits into the index of a single transmit–antenna, which is switched on for data transmission while all the other antennas are kept idle.
The receiver solves a N t hypothesis detection problem to estimate the transmit – antenna that is activated, which results in the estimation of the unique sequence of bits emitted by the encoder.
In Table 1, an example of 8 transmitting antenna assigned with 3 data bits used for encoding process. Another SSK algorithm was introduced in which more than one transmitting antenna could be used at every signaling interval but requires data bits with length = Nt in order to select activated transmitting antenna.
61
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014
Table 2 displays an example of 4 transmitting antennas selection. In SSK algorithms mentioned before, there are many drawbacks that can cause system performance degradation. Those problems are given as follows.
The main target of space diversity is to introduce various channel conditions by using many transmitting antenna at the same time. When selecting single antenna to be used in transmission process this means that space diversity technique will not be efficient as needed. In the second SSK algorithm, the number of selected antenna i could be 1 i Ni which results in more possible channel frequency responses to be estimated at the receiver. In order to avoid disadvantages of traditional SSK algorithms, modified SSK algorithm will be introduced in this paper. TABLE 1 TRADITIONAL SSK TYPE 1
Encoding Bits
Selected Antenna
000
1
001
2
010
3
011
4
100
5
101
6
110
7
111
8
TABLE 2 TRADITIONAL SSK TYPE 2
Sort binary vectors in descending order based on the number of ones at every vector (code).
Select first L vectors from sorted vectors obtained in step 4 then put them in another pool. Those vectors contain the highest number of ones.
Every vector in the selected pool, with size L vectors, will determine selected antennas to be used for emitting one data symbol at each signaling interval.
Let’s display an example for modified SSK algorithm results using Nt = 3 , L = 5 in Table 3. TABLE 3 MODIFIED SSK ALGORITHM
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6 Selected antennas
Nt = 3 L = 5
0 0 0 0 0 1 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 1 1 1
1 0 0 1 0 1 1 1 0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 1
1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0
1 1 1 1 0 1 1 1 0 0 1 1 1 0 0
(1 , 2 ,3) (1 , 3) (1, 2) (2 , 3) (1)
Proposed Realization Of Slt-Based Mc-Cdma The MC‐CDMA transmitter spreads the original data stream over different sub‐carriers using a given spreading code in the frequency domain. Eq. 3 shows the MC‐CDMA transmitter output of the kth user for BPSK scheme, where d k (t ) is the data bits for kth user,
Selected Antenna
GMC denotes the processing gain, NC is the number of
0001
(4)
sub‐carriers and ck (t ) is the spreading Pseudo Noise
0010
(3)
(PN) sequence of the kth user. It is assumed that NC =
0011
(3,4)
GMC . In MC‐CDMA systems, the original data stream
0100
(2)
0101
(2,4)
0110
(2,3)
from a user is spread with the user’s specific spreading code in the frequency domain. In other words, a fraction of the symbol corresponding to a chip of the spreading code is transmitted through a different subcarrier. The narrowband subcarriers are generated using BPSK modulated signals, each at different frequencies, which at baseband are multiples of harmonic frequency 1/ Ts . That is,
Modified SSK algorithm differs from traditional SSK algorithms in many points. The steps of modified SSK algorithm are displayed as follows.
Select an integer L so that L < 2 N t
Put integers serial [0 , 2 N t – 1] and then convert these integers to binary form with length N t bits per code.
62
Encoding Bits
2) Modified SSK Algorithm
step 2.
Invert the first bit at every vector obtained in
f fi fi 1
1 Ts
(4)
where, Ts is the symbol duration of data stream. The subcarrier frequencies are orthogonal to each other at baseband. The transmitted signal of the kth user is
International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014 www.seipub.org/ijepr
given by: NC
S kMC (t ) d k (t ) cki cos(i t ) i 1
response is estimated by using the training and the received sequences as follows.
(5) H (k )
The total bandwidth required for transmission is
( N C 1)GMC N C Ts
(6)
The received signal of MC‐CDMA systems for all K users is K
NC
k 1
i 1
r (t ) d k (t )
h(i )cki
cos(i t ) n(t )
(7)
where n(t) is the Additive White Gaussian Noise (AWGN) and h(i) is the complex signal channel coefficient of the ith subcarrier. The received signal is demodulated with corresponding subcarrier followed by low pass filtering to generate the baseband signal. The baseband signal is weighted by some coefficients, and then all baseband signals are combined together. It can be seen that the received signal is combined in the frequency domain therefore the receiver can always employ all the received signal energy scattered in the frequency domain. This is the advantage of MC‐CDMA. Different combining techniques to enhance the system performance can be used, which correspond to different choices of coefficients. Typical combining techniques include maximum ratio combining, equal gain combining and minimum mean square error combining. The drawback of this system is that, it does not consider the multipath and fading effects. The block diagram of the proposed system for SLT based MC‐CDMA is depicted. In this design the FFT‐Based OFDM is replaced with a SLT‐based OFDM which has a better performance and a reduced ISI and ICI in comparison with FFT‐based OFDM. Each data symbol is multiplied with a spreading sequence, the Gold sequence can be used since it has a relatively good correlation values. Other spreading codes that have a relatively good correlation values like Walsh‐Hadamard(WH) code can be used, but it can be used when a small number of users are considered, since the orthogonality of the code is reduced due to the multipath propagation. Binary Phase Shift Keying (BPSK) or Quadrature Phase Shift Keying (QPSK) signal mapping can be used for mapping the spreading and the training sequences. After that a pilot‐carrier (training sequence) which may be a bipolar sequence previously informed to receiver is generated. The channel frequency
received training sample( k ) transmitted training sample(k )
(8)
The channel frequency response previously found is used to compensate the channel effects on the data. Conclusions From the survey, it is found that the slantlet transform (SLT) based MC‐CDMA system outperforms the modified SSK based MC‐CDMA and MOPSO based MC‐CDMA systems. Using SLT – OFDM, the role of FFT and insertion of cyclic prefix are eliminated and also two zero moments are introduced such that the time localization of samples at the receiver is easy and hence fast recovery of data is possible.
FIG. 3 BER PERFORMANCE OF SLT‐MC‐CDMA SYSTEM
Since the SLT‐OFDM introduces more orthogonal subcarriers than FFT‐OFDM, the spectral efficiency and hence high data rate can be achieved which caters the 4G communication system demands. In Fig. 3 below, at an SNR of 9.5 dB, the SLT‐MC‐CDMA offered BER of the order of 105 , whereas FFT‐MC‐ CDMA offers highly erroneous output at the same SNR. Hence, for broad band, high speed, and secure data communication scenarios in 4G and above technologies, the SLT based MC‐CDMA system can be regarded as one of the best strategies to rely upon. REFERENCES
Ahmed I et al. “Margin Adaptive resource Allocation for Multi‐user
OFDM
Systems
by
Particle
Swarm
Optimization and Differential Evolution.” International Journal of engineering & Technology, February 2011. Chakravarthy C. K. and Reddy P. “Particle Swarm Optimization Based Approach for resource Allocation
63
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014
and Scheduling in OFDMA Systems.” International
Ozel T. ‘Intelligent Machining.” pp 246 – 265, Wiley, 2009.
Journal of Communications, Network and System
Prasanna Venkatesan G. K. D., Ravichandran V. C. “Performance analysis of dynamic sub‐carrier allocation
Sciences, P 466 ‐471, March 2010. Chouly A., Brajal A., and Jourdan S. “Orthogonal
technique for adaptive modulation based MC‐CDMA
multicarrier techniques applied to direct sequence
system.” JCSNS International Journal of Computer
spread spectrum CDMA systems.” Proc. of IEEE
Science and Network Security, VOL.7 No.2, February
GLOBECOM’93,
2007.
Houston,
USA,
pp.
1723‐1728,
Prasanna Venkatesan G. K. D., Ravichandran V. C.
November 1993. Coello C et al. “Handling Multiple objectives with Particle
”Performance analysis of MC‐CDMA for wideband
Swarm Optimization.” IEEE transaction Evolutionary
channels.” Information technology journal, Asian
Computation, Vol. 8, No. 3, June 2004.
Network for scientific Information, 2007.
Deb K and Padhye N. “Improving a Particle Optimization
Raquel C and Naval P C Jr. “An effective use of Crowding
Algorithm Using Evolutionary Algorithn Framework.”
Distance
in
Multi‐objective
Particle
KanGAL Report 2010003, February 2010.
Optimization.” GECCO 05, June 25 – 29, 2005.
Swarm
Dhananjay Kumar, Chellappan C. “Efficient resource
Shu’aibu D S et al. “Dynamic Resource allocation in mobile
allocation in MC‐CDMA cellular wireless networks to
WiMax using Particle Swarm Optimization Techniques.”
support multimedia traffic.” Journal of Theoretical and
International Journal of Physical Sciences, Vol. 6, pp.
Applied Information Technology 2009.
1009 – 1014, March 2011.
Gheitanchi S et al. “Particle Swarm Optimization for
Teruya Fujii, Noboru Izuka, Hiroyoshi Masui, and Atsushi
Resource Allocation in OFDMA.” Proc. International
Nagate. ”A proposal of sub‐carrier selecting MC‐CDMA system for 4G systems.” 2005 IEEE.
Conference on digital Signal Processing, 2007. Hara S and Prasad R. “Overview of Multicarrier CDMA.”
Yang Yi et al. “Modified Particle Swarm Optimization and
IEEE Commun. Magazine, vol. 35, no. 12, pp. 126‐133,
Genetic Algorithm Based Adaptive Resources Algorithm
December 1997.
for
Multiuser
Orthogonal
Frequency
Division
Multiplexing System.” Information technology Journal,
Kennedy J and Eberhart R. C. “Particle Swarm Optimization,
Asian Network for Scientific Information, March 2011.
Proceedings of IEEE Conference on Neural Networks.”
Yee N., JLinnartz‐P., and Fettweis G. “Multicarrier CDMA in
Pages 1942‐1948, 1995. Lie‐Liang Yang and Lajos Hanzo. “Multicarrier DS‐CDMA:
indoor wireless radio networks.” Proc. Of IEEE
A multiple access scheme for ubiquitous broadband
PIMRC’93, Yokohama, Japan, pp. 109‐113, September
wireless
1993.
communications.”
IEEE
Communications
Magazine, October 2003.
Yew T. J. “Multiwavelets and Scalable Video Compression.” Ph.D. Thesis, Department of Electrical and Computer
Malathi P and Vanathi P. T. “802.11a High Throughput
Engineering, National University of Singapore, 2002.
WLAN OFDM‐MIMO System,” International Journal of Algorithm Computing and Mathematics (IJACM), Vol. 1,
No. 1, pp. 48‐60, 2008.
Suriavel Rao R. S. obtained his Bachelor’s Degree in Electronics and Communication Engineering from Noorul Islam College of Engineering (Manonmaniam Sundaranar University), Kumarakovil, Kanyakumari, India. He obtained his Master’s degree in Communication Systems from Colloge of Engineering Guindy, Anna University, Chennai. He is currently a research scholar in the department of Information and Communication Engineering, Anna University, Chennai working under the supervision of Dr. Malathi P., Professor & Principal, Bharathiyar Institute of Engineering for Women, Salem,
Malathi P and Vanathi P. T. “Orthogonal Frequency Division Multiplexing (OFDM) for Wireless Local Area Network (WLAN).” International Journal of Advancement of Modeling & Simulation techniques in Enterprises (AMSE) , Vol. 51, No. 1, pp. 1‐16, 2008. Malathi P and Vanathi P. T. “Power Line Communication using OFDM and OGA.” ICGST International Journal of Artificial Intelligent and Machine Learning (AIML), Vol. 7, No. 1, pp. 23‐31, 2007.
64
International Journal of Engineering Practical Research (IJEPR) Volume 3 Issue 3, August 2014 www.seipub.org/ijepr
India. He started his carrier as Lecturer in June 2002. At present, he is working as Assistant Professor in the department of Electronics and Communication Engineering (School of electrical Sciences) at Karunya University, Coimbatore, India. He has around 7 years of teaching experience in various engineering subjects. He had also worked as SoC physical design engineer in industry for a couple of years. He presented few technical papers at some national level technical conferences. His areas of interest are Adaptive OFDM techniques, WiMAX, SoC Design and LTE Wireless Communications. Mr. Suriavel Rao is a Life member of Indian Society for Technical Education (ISTE). Malathi P. obtained her Bachelor’s Degree in Electronics and Communication Engineering from V. L. B. Janakiammal College of Engineering and Technology (Bharathiar University), Coimbatore, India. She obtained her Master’s Degree in Applied Electronics from Coimbatore Institute of Technology (Bharathiar University), Coimbatore.
She got Ph.D degree from Anna University, Chennai, India under the guidance of Dr. Vanathi P. T., Professor, ECE department, PSG College of Technology, Coimbatore. She started her carrier as Lecturer in January 1997. At present, she is working as Principal and Professor cum Head of the department of Electronics and Communication Engineering at Bharathiyar Institute of Engineering for Women, Deviyakurichi, Salem, India. She has around 15 years of teaching experience in Engineering College. Totally 10 Ph.D Scholars are pursuing their Ph.D under her guidance. She published many technical papers in various journals. Given below is the list of her papers to name a few. Her areas of interest are MIMO‐OFDM technique, Wireless Local Area Network (WLAN), VLSI design and Digital Communication. Dr. Malathi is a Life member of Indian Society for Technical Education (ISTE) and Life member of Institution of Electronics and Telecommunication Engineers (IETE). She has published 6 papers in international journals, 1 paper in national journal, 7 papers in international conferences and 8 papers in national conferences.
65