Abstract Hard-Output Maximum Likelihood (ML) detection for Generalized Spatial Modulation (GSM) systems involves obtaining the ML solution of a number of different MIMO subproblems, with as many possible antenna configurations as subproblems. Obtaining the ML solution of all of the subproblems has a large computational complexity, especially for large GSM MIMO systems. In this paper, we present two techniques for reducing the computational complexity of GSM ML detection. The first technique is based on computing a box optimization bound for each subproblem. This, together with sequential processing of the subproblems, allows fast discarding of many of these subproblems. The second technique is to use a Sphere Detector that is based on box optimization for the solution of the subproblems. This Sphere Detector reduces the number of partial solutions explored in each subproblem. The experiments show that these techniques are very effective in reducing the computational complexity in large MIMO setups.

中文翻译：

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大型 MIMO 系统的最大似然低复杂度 GSM 检测

摘要 广义空间调制 (GSM) 系统的硬输出最大似然 (ML) 检测涉及获得许多不同 MIMO 子问题的 ML 解决方案，其中有尽可能多的天线配置作为子问题。获得所有子问题的 ML 解具有很大的计算复杂度，特别是对于大型 GSM MIMO 系统。在本文中，我们提出了两种降低 GSM ML 检测计算复杂度的技术。第一种技术基于计算每个子问题的框优化界限。这与子问题的顺序处理一起允许快速丢弃许多这些子问题。第二种技术是使用基于框优化的球体检测器来解决子问题。这个 Sphere Detector 减少了在每个子问题中探索的部分解决方案的数量。实验表明，这些技术在降低大型 MIMO 设置中的计算复杂度方面非常有效。