For K-best multiple-input multiple-output (MIMO) detection using real-valued decomposition (RVD), we need to obtain the $K$ surviving candidates from $K \sqrt {M}$ candidates, where $M$ is the modulation order. This paper presents a sorter-free detection algorithm, where the $K$ surviving nodes can be obtained in ${\mathrm {log_{2}}} {K}$ iterations, which is independent of modulation size. The $K \sqrt {M}$ candidates are arranged into a multiple-layer table using the proposed path metric discretization. A bisection-based search algorithm is used to obtain the locations of the $K$ surviving candidates. A low-complexity fully-pipelined architecture is devised in order to implement the proposed MIMO detection without the need to use any dividers. In addition, an efficient method for storing information from child nodes is proposed, which requires significantly less storage space compared to the conventional Schnorr Euchner (SE) enumeration approach. Implementation results show that the proposed K-best MIMO detector supports a 6.4Gb/s throughput that has a $0.32~\boldsymbol{\mu }\text{s}$ latency in a 90 nm process for a 256-quadrature amplitude modulation (QAM) 4 $\times $ 4 MIMO system. In addition, compared to the sorter-based baseline detector, the proposed detector improves the hardware efficiency by 77%.

中文翻译：

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适用于大型调制星座的高效 K-Best MIMO 检测器

对于使用实值分解 (RVD) 的 K 最佳多输入多输出 (MIMO) 检测，我们需要获得 $K$幸存候选人 $K \sqrt {M}$候选人，在哪里 $M$是调制阶数。本文提出了一种无排序器的检测算法，其中 $K$可以得到存活节点 ${\mathrm {log_{2}}} {K}$迭代次数，与调制大小无关。这 $K \sqrt {M}$使用所提出的路径度量离散化将候选者排列成多层表。基于二分的搜索算法用于获取 $K$幸存的候选人。设计了一种低复杂度的全流水线架构，以便在不需要使用任何分频器的情况下实现所提出的 MIMO 检测。此外，还提出了一种存储子节点信息的有效方法，与传统的 Schnorr Euchner (SE) 枚举方法相比，该方法需要的存储空间显着减少。实施结果表明，所提出的 K-best MIMO 检测器支持 6.4Gb/s 的吞吐量，具有 $0.32~\boldsymbol{\mu }\text{s}$256 正交幅度调制 (QAM) 4 的 90 nm 工艺中的延迟 $\次$ 4 MIMO系统。此外，与基于分类器的基线检测器相比，所提出的检测器将硬件效率提高了 77%。