{"title":"Spread Spectrum Code Estimationby Particle Swarm Algorithm","authors":"Vahid R. Asghari, Mehrdad Ardebilipour","volume":7,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":1075,"pagesEnd":1079,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/12337","abstract":"In the context of spectrum surveillance, a new method\r\nto recover the code of spread spectrum signal is presented, while the\r\nreceiver has no knowledge of the transmitter-s spreading sequence. In\r\nour previous paper, we used Genetic algorithm (GA), to recover\r\nspreading code. Although genetic algorithms (GAs) are well known\r\nfor their robustness in solving complex optimization problems, but\r\nnonetheless, by increasing the length of the code, we will often lead\r\nto an unacceptable slow convergence speed. To solve this problem we\r\nintroduce Particle Swarm Optimization (PSO) into code estimation in\r\nspread spectrum communication system. In searching process for\r\ncode estimation, the PSO algorithm has the merits of rapid\r\nconvergence to the global optimum, without being trapped in local\r\nsuboptimum, and good robustness to noise. In this paper we describe\r\nhow to implement PSO as a component of a searching algorithm in\r\ncode estimation. Swarm intelligence boasts a number of advantages\r\ndue to the use of mobile agents. Some of them are: Scalability, Fault\r\ntolerance, Adaptation, Speed, Modularity, Autonomy, and\r\nParallelism. These properties make swarm intelligence very attractive\r\nfor spread spectrum code estimation. They also make swarm\r\nintelligence suitable for a variety of other kinds of channels. Our\r\nresults compare between swarm-based algorithms and Genetic\r\nalgorithms, and also show PSO algorithm performance in code\r\nestimation process.","references":"[1] D. Thomas Magill, Francis D. Natali, Gwyn P. Edwards, \"Spread\r\nSpectrum Technology for Commercial Applications,\" Proceeding of the\r\nIEEE, vol. 82, pp. 572-584, April. 1994.\r\n[2] Raymond. L. Picholtz, Doland L. Schilling, Laurence B. Milstein,\r\n\"Theory of Spread Spectrum Communications - A Tutorial,\" IEEE\r\nTransactions on Communications, vol. COM-30, pp. 855-884, May.\r\n1982.\r\n[3] John G. Proakis, Digital communication, Third Edition, Mac Graw Hill\r\nInternational Editions, 1995.\r\n[4] V. R. Asghari and M. Ardebilipour, \"Spread Spectrum Code Estimation\r\nby Genetic Algorithms,\" International Journal on Signal Processing,\r\nvol. 1, pp. 301-304, Dec. 2004.\r\n[5] Dilip V. Sarwate, Michael B. Pursley, \"Crosscorrelation Properties of\r\nPseudo-random and Related Sequences,\" Proceeding of the IEEE, vol.\r\n68, pp. 593-619, May. 1980.\r\n[6] Michail K. Tsatsanis, Georgios B. Giannakis, \"Blind Estimation of\r\nDirect Sequence Spread Spectrum Signals in Multipath,\" IEEE\r\nTransactions on Signal Processing, vol. 45, pp. 1241-1252, May. 1997.\r\n[7] T. Baeck, \"Generalized convergence models for tournament and\r\n(mu,lambda)-selection,\" Proc. of the Sixth International Conf. on\r\nGenetic Algorithms, pp. 2-7, Morgan Kaufmann Publishers, San\r\nFrancisco, CA, 1995.\r\n[8] M. Potter, K. De Jong, and J. Grefenstette, \"A coevolutionary approach\r\nto learning sequential decision rules,\" Proc. of the Sixth International\r\nConf. on Genetic Algorithms, pp. 366-372, Morgan Kaufmann\r\nPublishers, San Francisco, CA, 1995.\r\n[9] R. Eberhart and J. Kennedy, \"A new optimizer using particle swarm\r\ntheory,\" Proc. 6th Int. Symp. Micro Machine Human Sci., pp. 39-43,\r\n1995.\r\n[10] J. Kennedy and R. C. Eberhart, \"Particle Swarm Optimization,\" Proc.\r\nIEEE Int. Conf. Neural Networks, Piscataway, NJ, pp. 1942-1948, 1995.\r\n[11] E. C. Laskari, K. E. Parsopoulos, and M. N. Vrahatis, \"Particle swarm\r\noptimization for minimax problems,\" Proc. 2002 Congress Evolutionary\r\nComputation, vol. 2, pp. 1576-1581, 2002.\r\n[12] J. Kennedy and R. Mendes, \"Population structure and particle swarm\r\nperformance,\" Proc. 2002 Congress Evolutionary Computation, vol. 2,\r\npp. 1671-1676, 2002.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 7, 2007"}