MC CDMA multiuser detection using random set theory and multi-value particle swarm optimization algorithm

Zhao Zhijin, Wang Bai-chuan
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引用次数: 3

Abstract

In real uplink multicarrier CDMA (MC CDMA) communication system, the asynchrony of active userspsila signature is a key factor of multiple access interference(MAI). Considering users access randomly, not only the location of active users, but also their number varies with time. In typical analysis, multi-user detection theory for MC CDMA has been developed under the assumption that the number of active users is constant and known at the receiver, and coincides with the maximum number of users entitled to access the system. Since many users might be inactive at any given time, detection under the assumption of the number of users larger than the real one may impair performance. The main goal of this paper is to introduce a general approach to the problem of identifying active users and estimating their data in a MC CDMA random-access system where users are continuously and independently entering and leaving the system. The tool we advocate is random-set theory (RST) and we propose a new particle swarm optimization (PSO) algorithm for discrete multi-values with the similar properties of BPSO to reduce the computation, the simulation result prove the efficiency of the new algorithm.
基于随机集理论和多值粒子群优化算法的mccdma多用户检测
在实际上行多载波CDMA (mccdma)通信系统中,主动用户签名的异步性是影响多址干扰(MAI)的一个关键因素。考虑到用户的随机访问,不仅活跃用户的位置不同,活跃用户的数量也随时间变化。在典型分析中,MC CDMA的多用户检测理论是在假设活跃用户数恒定且接收端已知的情况下发展起来的,并且与系统最大接入用户数一致。由于在任何给定时间都可能有许多用户处于非活动状态,因此在假设用户数量大于实际用户数量的情况下进行检测可能会损害性能。本文的主要目标是介绍一种通用的方法来识别活跃用户和估计他们的数据在MC CDMA随机接入系统中,其中用户连续和独立地进入和离开系统。为了减少计算量,我们提出了一种新的离散多值粒子群优化(PSO)算法,该算法与随机集理论(RST)具有相似的性质,仿真结果证明了新算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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