Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection

TL;DR

This paper introduces a simple, trainable MIMO detector called the THS detector, based on Hubbard--Stratonovich transformation and deep unfolding, achieving high detection performance with minimal parameters and low computational complexity.

Contribution

The paper proposes a novel DU-based MIMO detector with only $O(1)$ trainable parameters and $O(n^2)$ complexity, simplifying training and execution while maintaining high performance.

Findings

Outperforms existing algorithms of similar complexity

Achieves detection performance close to more complex DU-based detectors

Requires only $O(1)$ trainable parameters

Abstract

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.