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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61364", author = "Vahid R. Asghari and Mehrdad Ardebilipour", title = "Spread Spectrum Code Estimationby Particle Swarm Algorithm", abstract = "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.", keywords = "Code estimation, Particle Swarm Optimization(PSO), Spread spectrum.", volume = "1", number = "7", pages = "1038-4", }