Spread Spectrum Code Estimationby Particle Swarm Algorithm

In the context of spectrum surveillance, a new method to recover the code of spread spectrum signal is presented, while the receiver has no knowledge of the transmitter-s spreading sequence. In our previous paper, we used Genetic algorithm (GA), to recover spreading code. Although genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems, but nonetheless, by increasing the length of the code, we will often lead to an unacceptable slow convergence speed. To solve this problem we introduce Particle Swarm Optimization (PSO) into code estimation in spread spectrum communication system. In searching process for code estimation, the PSO algorithm has the merits of rapid convergence to the global optimum, without being trapped in local suboptimum, and good robustness to noise. In this paper we describe how to implement PSO as a component of a searching algorithm in code estimation. Swarm intelligence boasts a number of advantages due to the use of mobile agents. Some of them are: Scalability, Fault tolerance, Adaptation, Speed, Modularity, Autonomy, and Parallelism. These properties make swarm intelligence very attractive for spread spectrum code estimation. They also make swarm intelligence suitable for a variety of other kinds of channels. Our results compare between swarm-based algorithms and Genetic algorithms, and also show PSO algorithm performance in code estimation process.

Authors:

• Vahid R. Asghari
• Mehrdad Ardebilipour

Keywords:

• Code estimation
• Particle Swarm Optimization(PSO)
• Spread spectrum.

References:

[1] D. Thomas Magill, Francis D. Natali, Gwyn P. Edwards, "Spread
Spectrum Technology for Commercial Applications," Proceeding of the
IEEE, vol. 82, pp. 572-584, April. 1994.
[2] Raymond. L. Picholtz, Doland L. Schilling, Laurence B. Milstein,
"Theory of Spread Spectrum Communications - A Tutorial," IEEE
Transactions on Communications, vol. COM-30, pp. 855-884, May.
1982.
[3] John G. Proakis, Digital communication, Third Edition, Mac Graw Hill
International Editions, 1995.
[4] V. R. Asghari and M. Ardebilipour, "Spread Spectrum Code Estimation
by Genetic Algorithms," International Journal on Signal Processing,
vol. 1, pp. 301-304, Dec. 2004.
[5] Dilip V. Sarwate, Michael B. Pursley, "Crosscorrelation Properties of
Pseudo-random and Related Sequences," Proceeding of the IEEE, vol.
68, pp. 593-619, May. 1980.
[6] Michail K. Tsatsanis, Georgios B. Giannakis, "Blind Estimation of
Direct Sequence Spread Spectrum Signals in Multipath," IEEE
Transactions on Signal Processing, vol. 45, pp. 1241-1252, May. 1997.
[7] T. Baeck, "Generalized convergence models for tournament and
(mu,lambda)-selection," Proc. of the Sixth International Conf. on
Genetic Algorithms, pp. 2-7, Morgan Kaufmann Publishers, San
Francisco, CA, 1995.
[8] M. Potter, K. De Jong, and J. Grefenstette, "A coevolutionary approach
to learning sequential decision rules," Proc. of the Sixth International
Conf. on Genetic Algorithms, pp. 366-372, Morgan Kaufmann
Publishers, San Francisco, CA, 1995.
[9] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm
theory," Proc. 6th Int. Symp. Micro Machine Human Sci., pp. 39-43,
1995.
[10] J. Kennedy and R. C. Eberhart, "Particle Swarm Optimization," Proc.
IEEE Int. Conf. Neural Networks, Piscataway, NJ, pp. 1942-1948, 1995.
[11] E. C. Laskari, K. E. Parsopoulos, and M. N. Vrahatis, "Particle swarm
optimization for minimax problems," Proc. 2002 Congress Evolutionary
Computation, vol. 2, pp. 1576-1581, 2002.
[12] J. Kennedy and R. Mendes, "Population structure and particle swarm
performance," Proc. 2002 Congress Evolutionary Computation, vol. 2,
pp. 1671-1676, 2002.