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Differential Evolution Algorithm Aided Minimum Symbol Error Rate Multi-User Detection for Multi-User OFDM/SDMA Systems

机译:用于多用户OFDM / SDMA系统的差分进化算法辅助最小符号错误率多用户检测

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A Differential Evolution (DE) algorithm assisted Minimum Symbol Error Ratio (MSER) Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing / Space Division Multiple Access (OFDM/SDMA) systems. Quadrature Amplitude Modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. The MSER Cost Function (CF) may be deemed to be the most relevant one for QAM, but finding its minimum is challenging. Hence we propose a sophisticated DE assisted MSER-MUD scheme, which directly minimizes the SER CF of multi-user OFDM/SDMA systems employing QAM. Furthermore, the effects of the DE assisted MSER-MUD's algorithmic parameters, namely those of the population size $P_{s}$, of the scaling factor $lambda$ and of the crossover probability $C_{r}$ on the number of DE generations required for attaining convergence were investigated in our simulations. This allowed us to directly quantify their complexity. The simulation results also demonstrate that the proposed DE assisted MSER-MUD scheme significantly outperforms the conventional MMSE-MUD in term of the system's overall BER and it is capable of narrowing its BER performance discrepancy with respect to the optimal Maximum Likelihood (ML) MUD to about $4dB$, while requiring about $200$ times less CF evaluations compared to the optimal ML-MUD scheme.
机译:针对多用户多输入多输出(MIMO)正交频分多路复用/空分多址(MIMO)(DE)算法,提出了一种基于差分进化(DE)算法的最小符号误码率(MSER)多用户检测(MUD)方案。 OFDM / SDMA)系统。正交幅度调制(QAM)通过提供高吞吐量而用于大多数无线标准中。 MSER成本函数(CF)可能被认为是与QAM最相关的函数,但要找到其最小值则具有挑战性。因此,我们提出了一种复杂的DE辅助MSER-MUD方案,该方案直接最小化了采用QAM的多用户OFDM / SDMA系统的SERCF。此外,DE辅助MSER-MUD的算法参数的影响,即总体大小$ P_ {s} $,缩放因子$ lambda $和交叉概率$ C_ {r} $对DE数量的影响在我们的仿真中研究了达到收敛所需的几代人。这使我们可以直接量化其复杂性。仿真结果还表明,就系统的整体BER而言,提出的DE辅助MSER-MUD方案明显优于传统的MMSE-MUD,并且能够将其BER性能差异缩小到最佳的最大似然(ML)MUD到大约$ 4dB $,而与最佳ML-MUD方案相比,所需的CF评估要少$ 200 $倍。

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