A novel low complexity multiuser detector based on modied genetic algorithm in direct sequence-code division multiple access communication systems

In this paper, we present an ecient evolutionary algorithm for multiuserdetection (mud) problem in direct sequence-code division multiple access (ds-cdma)communication systems. the optimum detector for mud is the maximum likelihood(ml) detector, but its complexity is very high, and involves an exhaustive search to reachthe best tness of the transmitted and received data. thus, there has been much interestin suboptimal multiuser detectors with less complexity and reasonable performance. theproposed algorithm is a modied genetic algorithm (ga) which reduces the dimensionof the search space, and provides a suitable framework for future extension to otheroptimization problems, especially for high dimensional ones. this algorithm is comparedwith ml and two famous model-free optimization methods: particle swarm optimization(pso) and ant colony optimization (aco) algorithms, which have been used for mudin ds-cdma. the simulation results show that the performance of this algorithm is closeto the optimal detector; it has very low complexity, and it works better in comparison toother algorithms.