In this paper, we propose a novel low-complexity, near-optimal soft-input soft-output detector for $\text {N}\times \text {M}$ multiple-input multiple-output (MIMO) systems. Our algorithm is based on the combination of minimum mean square error decision feedback equalization (MMSE-DFE) and conditional optimization. In a round-robin fashion, one symbol is detected using exhaustive search in such a way that all $\text {N}\times (\text {M}-1)$ submatrices of the baseband channel matrix are considered and the one with the best metric is chosen. This search over all columns of the channel matrix, which can be performed in parallel, has the advantages of improving the performance of the hard-output version of the detector, and refining the list of candidates for efficient implementation of the soft-output detector for MIMO systems with error correcting codes. In particular, it is shown that the error performance of the soft-output system is comparable to that of the list sphere decoder (LSD) but with much smaller list size, and hence smaller complexity than the latter. We also analyze the performance and complexity of the proposed algorithm and discuss different techniques to further reduce its complexity without affecting the performance. Finally, the obtained theoretical results are validated via simulations.

中文翻译：

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弥合 MMSE-DFE 与 MIMO 系统最佳检测之间的差距

在本文中，我们为 $\text {N}\times \text {M}$ 多输入多输出 (MIMO) 系统提出了一种新颖的低复杂度、近乎最优的软输入软输出检测器。我们的算法基于最小均方误差决策反馈均衡（MMSE-DFE）和条件优化的组合。以循环方式，使用穷举搜索以这样一种方式检测一个符号，即考虑基带信道矩阵的所有 $\text {N}\times (\text {M}-1)$ 子矩阵，并且具有选择最佳指标。这种对通道矩阵所有列的搜索可以并行执行，具有提高检测器硬输出版本性能的优点，并细化候选列表，以有效实现具有纠错码的 MIMO 系统的软输出检测器。特别是，它表明软输出系统的错误性能与列表球解码器 (LSD) 的错误性能相当，但列表大小要小得多，因此复杂度低于后者。我们还分析了所提出算法的性能和复杂度，并讨论了不同的技术，以在不影响性能的情况下进一步降低其复杂度。最后，通过仿真验证了所获得的理论结果。我们还分析了所提出算法的性能和复杂度，并讨论了不同的技术，以在不影响性能的情况下进一步降低其复杂度。最后，通过仿真验证了所获得的理论结果。我们还分析了所提出算法的性能和复杂度，并讨论了不同的技术，以在不影响性能的情况下进一步降低其复杂度。最后，通过仿真验证了所获得的理论结果。