用于简单可训练MIMO信号检测的Hubbard-Stratonovich检测器
Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection
AI总结：
本文提出基于Hubbard-Stratonovich变换与深度展开的可训练HS检测器，仅需O(1)个可训练参数和每次迭代O(n^2)成本，在保持低复杂度的同时取得优于同复杂度算法的检测性能。
摘要：
大规模多输入多输出（MIMO）是第五代及未来无线通信网络中的一项关键技术。近年来，基于深度学习的各种MIMO信号检测器相继被提出。特别是深度展开（DU），它通过展开现有迭代算法并嵌入可训练参数，已展现出显著的检测性能。尽管DU的可训练参数数量少于传统深度神经网络，但由于基于DU的MIMO检测器通常利用矩阵求逆来提升检测性能，其训练和执行相关的计算复杂度一直存在问题。在本研究中，我们尝试构建一种结构最简单的基于DU的可训练MIMO检测器。所提出的检测器基于Hubbard-Stratonovich（HS）变换和DU，被称为可训练HS（THS）检测器。它仅需O(1)个可训练参数，且每次迭代的训练和执行成本为O(n^2)，其中n为发射天线数。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，并接近一个训练和执行成本高于THS检测器的基于DU的检测器。
英文摘要：
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.
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