尽管 multiple-input-multiple-output (MIMO ) 察觉在过去的年里收到了许多研究注意,到作者的知识,很少察觉方法与低复杂性表明 optimal/near-optimal 性能。这篇论文建议与非最优的 MIMO 察觉者合并自动重发请求(自动重复请求) 以便完成有利性能和低复杂性。在学习,自动重复请求导致的重发延期作为改进低复杂性算法的察觉表演的来源被利用。特别地,介绍自动重复请求改进的非最优的算法的察觉表演理论上被分析。为这,完成完整差异的性能的计划也是的一个足够的条件导出哪个与传播时间的数字联系察觉性能。而且,由重发的产量费用象它的更低的界限一样被推出。零力量(ZF ) 作为案例研究,均衡器与自动重复请求合作被显示通过理论分析有明显的表演改进。并且数字结果被介绍验证增加非最优的察觉者的表演并且同时为实际现实拥有更低的实现复杂性的建议计划的有效性。
Although multiple-input-multiple-output (MIMO) detection has received much research attention in the past years, to the author's knowledge, few detection methods demonstrate optimal/near-optimal performance with low complexity. This paper proposes to incorporate automatic retransmission request (ARQ) with sub-optimal MIMO detectors so as to achieve both favorable performance and low complexity. In the study, retransmission delay induced by ARQ is exploited as a source of improving the detection performance of low complexity algorithms. In particular, the detection performance of sub-optimal algorithms improved by introducing ARQ is analyzed theoretically. A sufficient condition for such scheme to achieve full-diversity performance is also derived which relates detection performance with number of transmission times. Moreover, throughput cost by retransmission is deduced as well as its lower bound. The zero-forcing (ZF) equalizer cooperating with ARQ, as a case study, is shown to have evident performance improvement through theoretical analysis. And numerical results are presented to verify the effectiveness of the proposed scheme which boosts the performance of sub-optimal detector and possesses lower implementation complexity for practical reality simultaneously.