Massive multiple-input multiple-output (MIMO) is a key technology used in fifth-generation wireless communication networks and beyond. Recently, various MIMO signal detectors based on deep learning have been proposed. Especially, deep unfolding (DU), which involves unrolling of an existing iterative algorithm and embedding of trainable parameters, has been applied with remarkable detection performance. Although DU has a lesser number of trainable parameters than conventional deep neural networks, the computational complexities related to training and execution have been problematic because DU-based MIMO detectors usually utilize matrix inversion to improve their detection performance. In this study, we attempted to construct a DU-based trainable MIMO detector with the simplest structure. The proposed detector based on the Hubbard--Stratonovich (HS) transformation and DU is called the trainable HS (THS) detector. It requires only $O(1)$ trainable parameters and its training and execution cost is $O(n^2)$ per iteration, where $n$ is the number of transmitting antennas. Numerical results show that the detection performance of the THS detector is better than that of existing algorithms of the same complexity and close to that of a DU-based detector, which has higher training and execution costs than the THS detector.

翻译：大规模多输入多输出（MIMO）是第五代无线通信网络及未来技术中的关键组成部分。近年来，基于深度学习的各类MIMO信号检测器被广泛提出。特别是深度展开（DU）技术——通过将现有迭代算法展开并嵌入可训练参数——已展现出卓越的检测性能。尽管DU的可训练参数数量少于传统深度神经网络，但基于DU的MIMO检测器通常利用矩阵求逆来提升检测性能，导致训练与执行的计算复杂度成为突出问题。本研究尝试构建结构最简单的基于DU的可训练MIMO检测器。所提出的检测器基于Hubbard-Stratonovich（HS）变换与DU技术，称为可训练HS（THS）检测器。该检测器仅需$O(1)$个可训练参数，且每次迭代的训练与执行成本为$O(n^2)$，其中$n$为发射天线数量。数值结果表明，THS检测器的检测性能优于相同复杂度的现有算法，并接近于训练与执行成本高于THS检测器的基于DU的检测器。