Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection

Satoshi Takabe, Takashi Abe

Abstract: Recently, various multiple-input multiple-output (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. In this letter, we attempted to construct a simple DU-based trainable MIMO detector with the simplest structure without using matrix inversion. The proposed detector based on the Hubbard–Stratonovich (HS) transformation and DU is called the trainable HS (THS) detector. It requires a constant number of trainable parameters and only quadratic training and execution costs per iteration. 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.