MC—CDMA系统中分集接收的改进AFSA多用户检测

文章在多载波码分多址（MC—CDMA）系统上行链路的频率选择性信道中，提出了一种使用多天线分集接收的基于改进人工鱼群算法（AFSA）的多用户检测（MUD）方案。为了解决多目标问题，考虑针对不同天线分支代价函数根据Pareto优化准则进行个体选择，使改进后的鱼群算法具有选择行为和交叉行为，同时也独立利用不同天线分支信号携带的有用信息。仿真结果表明，在相同计算复杂度下，基于Pareto优化准则的个体选择机制AFSA—MUD的误码率（BER）性能要远远优于基于代价函数线性合并的个体选择机制；通过与单用户和最优检测仿真结果比较也表明，基于该策略的改进人工鱼群算法的多用户检测是有效的。

Artificial Fish Swarm Algorithm -Assisted and Receive Diversity- Aided Multi-user Detection in MC-CDMA Systems

An improved artificial fish swarm algorithm （AFSA） assisted multi-user detection （MUD） is proposed for the receive-antenna-diversity-aided multi-carrier code-division multiple-access （MC-CDMA） systems in frequency-selective fading channel. Due to the receive-diversity, the signals received at the different antennas are faded independently, resulting in an independent objective function for each antenna. The indi- viduals associated with the AFSA are selected based on the concept of Pareto optimality, which uses the information from the antennas independently. The uniform crossover process from GA is applied, which improve performance, Simulation results showed that： with the same computation complexity, the strategy has much better bit error rate （BER） performance than the convention one. Comparisons with the conventional singleuser matched filter and the optimum multi-user detector （OMD） verified the effectiveness of the proposed scheme.