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

机译:人工鱼群算法辅助和接收多样性辅助多用户检测的MC-CDMA系统

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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. To resolve the multi-objective dilemma when choosing one signal estimation for multiple receive antenna-branches, the individuals associated with the AFSA are selected based on the concept of Pareto optimality, which uses the information from the antennas independently. 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 multiuser detector and the decorrelator verified the effectiveness of the proposed scheme.
机译:针对频率选择性衰落信道中的接收天线分集辅助多载波码分多址(MC-CDMA)系统,提出了人工鱼群算法(AFSA)辅助多用户检测(MUD)。由于接收分集,在不同天线处接收的信号会独立衰减,从而为每个天线产生独立的目标函数。为了解决在为多个接收天线分支选择一个信号估计时的多目标难题,基于帕累托最优的概念选择了与AFSA相关的个人,该概念独立使用来自天线的信息。仿真结果表明:在相同的计算复杂度的情况下,该策略的比特误码率(BER)性能要优于传统算法。与常规多用户检测器和解相关器的比较证明了该方案的有效性。

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