Research of Ecologic System Optimization Algorithms 

for Multi-user Detection in CDMA Communication Systems

Xu Yao-hua; Hu Yan-jun

Volume 28 Issue 11

Sep. 2010

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2006 > 28(11): 2111-2115

Xu Yao-hua, Hu Yan-jun. Research of Ecologic System Optimization Algorithms for Multi-user Detection in CDMA Communication Systems[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2111-2115.

Citation:

Xu Yao-hua, Hu Yan-jun. Research of Ecologic System Optimization Algorithms for Multi-user Detection in CDMA Communication Systems[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2111-2115.

Citation:

PDF( 362 KB)

Research of Ecologic System Optimization Algorithms for Multi-user Detection in CDMA Communication Systems

Received Date: 2005-03-10
Rev Recd Date: 2005-10-31
Publish Date: 2006-11-19

Abstract

Abstract

An ecologic system optimization algorithm is a type of new developed evolutionary algorithm, which is based on swarm intelligence, and has the properties of converge quickly, simple rules. This paper research the ant colony optimization algorithm and the particle swarm optimization on discrete space, and describe two algorithms for the multi-user detection problem in Code Division Multiple Access(CDMA) communication system．The approach using some new methods to improve the search quality and efficiency, analyses and simulation results show the approach has low computational complexity, and the BER property of the algorithm is better than the conventional detector, to find a new method to solve the problem of MUD in CDMA.

FullText(HTML)

References(1)

References

Verdu. S, Multi-user Detection, Cambridge University Press, 1998: 303－312.[2]吴新余，郑宝玉，倪梁方. 分集接收混合遗传算法多用户检测器在异步CDMA移动通信中的应用研究[J]. 通信学报, 2003, 24(2): 27－36.[3]Dorigo. M, Maniezzo. V, Colorni. A. Ant system: Optimization by a colony of cooperating agents [J]. IEEE Trans. on Systems, Man and Cybernetics-PartB, 1996, 26(1): 1－13.[4]Kennedy J, Eberhart R. Particle swarm optimization [A]. Proc IEEE Int Conf on Neural Networks[C], Perth, 1995: 1942－1948.[5]谢晓锋, 张文俊, 杨之廉. 微粒群算法综述[J]. 控制与决策, 2003, 18(2): 129－134.[6]Kennedy J, Eberhart R. A discrete binary version of the particle swarm algorithm [A]. Proc IEEE Int Conf on Systems, Man, and Cybernetics[C].Orlando, 1997: 4104－4108.[7]Verdu. S. Computational complexity of optimum multiuser detection[J].Algorithmica.1989, 4(3):303-[8]忻斌健, 汪镭, 吴启迪. 蚁群算法的研究现状和应用及蚂蚁智能体的硬件实现[J]. 同济大学学报, 2002, 30(1): 82－87.[9]吴斌, 史忠植. 一种基于蚁群算法TSP问题分段求解算法[J].计算机学报, 2001, 24(12): 1328－1333.

Relative Articles

Supplements(0)

Cited By

Proportional views