MC-CDMA系统中基于遗传算法的多用户检测

提出了在频率选择性信道下使用多天线分集接收的多载波码分多址(MC-CDMA)系统上行链路中基于遗传算法(GA)的多用户检测。针对三种不同的代价函数,研究了两种不同的个体选择机制对GA多用户检测的性能影响。代价函数分别为各个天线分支接收信号与估计信号的对数似然函数(LLF)、误差平方和相位误差平方。仿真分析表明:在相同代价函数下,基于Pareto优化准则的个体选择机制要优于按线性合并准则的个体选择机制;在相同个体选择机制下,基于相位误差平方的代价函数的误比特率性能和抗远近性能要优于基于LLF和误差平方的代价函数。

Multi-Antenna and Genetic-Algorithm-Based Multiuser Detection for MC-CDMA Systems

A genetic-algorithm (GA) based on multiuser detection (MUD) is proposed for the antennadiversity-assisted multicarrier code-division multiple-access (MC-CDMA) systems in the frequency-selective fading channel. Two kinds of GA-based individual-selection strategies are investigated with three different cost functions, which are log-likelihood function (LLF), squared error and squared phase error, respectively. Simulation analysis shows that:with the same cost function, the individual-selection strategy based on Pareto optimality criterion has better performance than the linear combing criterion; with the same individual-selection strategy, the cost function based on squared phase error has the better bit error rate (BER) performance and the better near-far resistance performance than those of two other cost functions.