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

In real uplink multicarrier CDMA (MC CDMA) communication system, the asynchrony of active users’ 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.