粒子群优化算法在多用户检测中的应用

粒子群优化算法是一类有效的随机全局优化技术。它利用一个粒子群搜索解空间,每个粒子表示一个被优化问题的解,通过粒子间的相互作用发现复杂空间中的最优区域。多用户检测技术是直扩序列码分多址中的一项关键技术。将粒子群优化算法应用于多用户检测中,能有效抑制多址干扰,实现结构简单、鲁棒性强的目的,在加速收敛的同时降低了计算复杂度。仿真结果表明,这种多用户检测器充分利用了粒子群优化算法的优良特性,与传统的码分多址接收机、基于进化算法的多用户检测器和基于遗传算法的多用户检测器比较,在误码率和收敛速度等方面都有显著的改善。

Multiuser Detection Using the Particle Swarm Optimization Algorithm

Particle Swarm Optimization(PSO) algorithm is an efficient stochastic global optimization technique making use of a particle population,where each particle represents a solution to the problem being optimized.PSO find optimal regions of complex search spaces through the interaction of individuals in a population of particles.In this paper,PSO algorithm is applied to solve the Multiuser Detection(MUD) problems in Direct Sequence Code Division Multiple Access(DS-CDMA) system,which reduces the computational complexity by providing faster convergence.The algorithm can be implemented with ease,and provide a more robust algorithm and better near-far resistance with parallel processing.The simulation results show that the proposed detections benefit greatly from the PSO and have significant performance improvements over Conventional Detector(CD) and previous multiuser detectors based on Genetic Algorithm(GA) or Evolution Programming(EP) in terms of bit-error-rate and convergence rate.