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A New Search Algorithm for Allocating Pilots in Asymptotic TDD Massive MIMO Systems

机译:渐近TDD大规模MIMO系统中分配导频的新搜索算法

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This paper proposes a novel heuristic algorithm that mitigates pilot contamination in Massive MIMO (multiple-input, multiple-output) systems by optimizing the allocation of pilots, which are a shared resource among base stations (BSs). The goal is to minimize the effect of pilot contamination caused by interference from reusing the same pilot sequence from multiple user equipment (UE) in different cells. Specifically, we aim to maximize the minimum uplink signal to interference ratio (SIR) through the network by assigning the same pilot to the UEs in different cells that cause the least amount of interference. For this purpose, the algorithm starts with two cells that have the worst arithmetic mean of the potential SIR between their users. Then the algorithm continues the assignment procedure for the rest of the cells depending on the arithmetic mean of the worst potential SIR of the users of a cell and the users of the already assigned cells. The simulation results showed that our results are very close to the optimal solution where the exhaustive search is feasible, and outperforms the results obtained from recently published related work which served as a benchmark when the exhaustive search could not be applied to the search space size.
机译:本文提出了一种新颖的启发式算法,该算法可通过优化导频分配来减轻Massive MIMO(多输入,多输出)系统中的导频污染,而导频是基站(BS)之间的共享资源。目的是最大程度地减少因重复使用来自不同小区中多个用户设备(UE)的相同导频序列而产生的干扰所造成的导频污染影响。具体而言,我们旨在通过将相同的导频分配给引起最小干扰量的不同小区中的UE,来最大化通过网络的最小上行链路信噪比(SIR)。为此,该算法从两个单元格开始,这两个单元格在其用户之间的潜在SIR的算术平均值最差。然后,该算法根据小区用户和已分配小区用户的最差潜在SIR的算术平均值,继续进行其余小区的分配过程。仿真结果表明,我们的结果与穷举搜索可行的最佳解决方案非常接近,并且胜过了最近发表的相关工作所获得的结果,这些工作在无法将穷举搜索应用于搜索空间大小时作为基准。

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