Channel puncturing transforms a multiple-input multiple-output (MIMO) channel into a sparse lower-triangular form using the so-called WL decomposition scheme in order to reduce tree-based detection complexity. We propose computationally efficient soft-output detectors based on two forms of channel puncturing: augmented and two-sided. The augmented WL detector (AWLD) employs a punctured channel derived by triangularizing the true channel in augmented form, followed by leftsided Gaussian elimination. The two-sided WL detector (dubbed WLZ) employs right-sided reduction and left-sided elimination to puncture the channel. We prove that augmented channel puncturing is optimal in maximizing the lower-bound on the achievable information rate (AIR) based on a new mismatched detection model. We show that the AWLD decomposes into an MMSE prefilter and channel gain compensation stages, followed by a regular WL detector (WLD) that computes least-squares softdecision estimates. Similarly, WLZ decomposes into a pre-processing reduction step followed by WLD. AWLD attains the same performance as the existing AIR-based partial marginalization (PM) detector, but with less computational complexity. We empirically show that WLZ attains the best complexityperformance tradeoff among tree-based detectors.

中文翻译：

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基于最优信道打孔的低复杂度软输出 MIMO 检测器

信道穿孔使用所谓的 WL 分解方案将多输入多输出 (MIMO) 信道转换为稀疏的下三角形式，以降低基于树的检测复杂度。我们提出了基于两种形式的信道穿孔的计算高效的软输出检测器：增强型和两侧。增强的 WL 检测器 (AWLD) 采用了一个穿孔的信道，它是通过对增强形式的真实通道进行三角化，然后进行左侧高斯消除而导出的。两侧 WL 检测器（称为 WLZ）采用右侧减少和左侧消除来刺穿通道。我们证明，基于新的失配检测模型，增强信道打孔在最大化可实现信息速率 (AIR) 的下限方面是最佳的。我们展示了 AWLD 分解为 MMSE 前置滤波器和通道增益补偿级，然后是计算最小二乘软决策估计的常规 WL 检测器 (WLD)。类似地，WLZ 分解为预处理减少步骤，然后是 WLD。AWLD 获得与现有基于 AIR 的部分边缘化 (PM) 检测器相同的性能，但计算复杂度更低。我们凭经验表明，WLZ 在基于树的检测器中获得了最佳的复杂性性能折衷。