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http://dx.doi.org/10.3837/tiis.2014.05.005

Evolutionary Algorithm-based Space Diversity for Imperfect Channel Estimation  

Ghadiri, Zienab Pouladmast (Faculty of Engineering, Multimedia University)
El-Saleh, Ayman A. (Faculty of Engineering, Multimedia University)
Vetharatnam, Gobi (Faculty of Engineering, Multimedia University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.8, no.5, 2014 , pp. 1588-1603 More about this Journal
Abstract
In space diversity combining, conventional methods such as maximal ratio combining (MRC), equal gain combining (EGC) and selection combining (SC) are commonly used to improve the output signal-to-noise ratio (SNR) provided that the channel is perfectly estimated at the receiver. However, in practice, channel estimation is often imperfect and this indeed deteriorates the system performance. In this paper, diversity combining techniques based on two evolutionary algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) are proposed and compared. Numerical results indicate that the proposed methods outperform the conventional MRC, EGC and SC methods when the channel estimation is imperfect while it shows similar performance as that of MRC when the channel is perfectly estimated.
Keywords
Diversity; PSO; GA; MRC; EGC; SC; perfect/imperfect channel estimation;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Y. Q. Qin, D. B. Sun, N. Li and Y. G. Cen, "Path planning for mobile robot using the particle swarm optimization with mutation operator," in Proc. of Proceedings of the 3rd International Conference on Machine Learning, vol. 4, pp. 2473 - 2478 , August, 2004.
2 Y. Shi and R. C. Eberhart, "Empirical study of particle swarm optimization," in Proc. of Proceedings of the 1999 Congress on Evolutionary Computation (CEC), vol. 3, 1999.
3 R. C. Eberhart and Y. Shi, "Comparision between genetic algorithms and particle swarm optimization," in Proc. of Proceedings of the 7th Internatinal Conference on Evolutionary Programming, pp. 611-616, 1998.
4 M. Zubair, M. A. S. Choudhry, A. Naveed and I. M. Qureshi, "Particle swarm with soft decision for multiuser detection of synchronous multicarrier CDMA," IEICE Transactions on Communucations, vol. E91-B, no. 5, pp. 1640-1643, May, 2008.   DOI   ScienceOn
5 Y. Tokgoz and B. D. Rao, "The effect of imperfect channel estimation on the performance of maximum ratio combining in presence of cochannel interference," IEEE Transactions on Vehicular Technology, vol. 55, no. 5, pp. 1527-1534 , September, 2006.   DOI   ScienceOn
6 R. You, H. Li and Y. Bar-Ness, "Diversity combining with imperfect channel estimation," IEEE Transactions on Communications, vol. 53, no. 10, pp. 1655-1662, May, 2005.   DOI   ScienceOn
7 R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, Wiley, New Jersey, 2004.
8 D. Ashlock, Evolutionary computation for modeling and optimization, Springer, 2006.
9 M. Gen, R. Cheng and L. Lin, Network models and optimization: multi-objective genetic algorithm approach, Springer, 2008.
10 J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948, November/December, 1995.
11 V. Kachitvichyanukul, "Comparison of three evolutionary algorithms: GA, PSO, and DE," Journal of Industrial Engineering & Management system, vol. 11, no 3, pp. 215-223, September, 2012.   DOI   ScienceOn
12 J. J. Liang, A. K. Qin, S. Baskar, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE Transactions on Evolutionary Computation, vol.10, no. 3, pp. 281-295, June, 2006.   DOI   ScienceOn
13 R. Janaswamy, Radiowave Propagation and Smart Antennas for Wireless Communications, Kluwer Academic Publishers, 2000.
14 J. R. Barry, E. A. Lee and D. G. Messerschmitt, Digital Communication, Kluwer Academic Publishers, September, 2003.
15 L. C. Godara, Handbook of Antennas for Wireless Communications, CRC Press, 2002.
16 Q. Zhang, "Probability of error for equal gain combiners over rayleigh channels: some closed form solutions," IEEE Transactions on Communications, vol. 45, no. 3, pp. 270-273, March, 1997.   DOI   ScienceOn
17 S. Zheng, C. Lou and X. Yang, "Cooperative spectrum sensing using particle swarm optimisation," Electronics Letters, vol. 46, no. 22, pp. 1525-1526, October, 2010.   DOI   ScienceOn
18 N. Kong, "Performance comparison among conventional selection combining, optimum combining and maximal ratio combining," IEEE International conference on Communications (ICC), pp. 1-6, June, 2009.
19 J. Proakis and Masoud Salehi, Digital Communications, 5th edition, McGraw-Hill, November 2007.
20 R. Duan, R. Janti, M. Elmusrati, "Capacity for spectrum sharing cognitive radios with MRC diversity and imperfect channel information from primary user," Proceeding of IEEE GLOBECOM, pp. 1-5, December, 2010.
21 Y. Ma, R. Schober, S. Pasupathy, "Effect of channel estimation error on MRC diversity in rician fading channels," IEEE Transcations on Vehicular Technology, vol. 54. no. 6, pp. 2137-2142, November, 2005.
22 B. R. Tomiuk, N. C. Beaulieu and A. A. Abu-Dayya, "General forms for maximal ratio diversity with weighting errors," IEEE Transactions on Communications, vol. 47, no. 4, pp. 488-492, April, 1999.   DOI   ScienceOn
23 S. Roy and P. Fortier, "Maximal-ratio combining architectures and performance with channel estimation based on a training sequence," IEEE Transactions on Wireless Communications, vol. 3, no. 4, pp. 1154-1164, July, 2004.   DOI   ScienceOn
24 J. S. Thompson, "Antenna array performance with channel estimation errors," in Proc. of Proceeding of ITG Workshop Smart Antennas, pp. 75-78, March, 2004.
25 R. Annavajjala and L. B. Milstein, "Performance analysis of optimum and suboptimum selection diversity schemes on rayleigh fading channel with imperfect channel estimation," IEEE Transactions on Vehicular Technology, vol. 56, no. 3, pp. 1119-1130, May, 2007.   DOI   ScienceOn