Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection
This work addresses computational efficiency for massive MIMO systems in wireless communications, representing an incremental improvement in detector design.
The authors tackled the high computational complexity of deep unfolding-based MIMO signal detectors by proposing a trainable Hubbard-Stratonovich (THS) detector with only O(1) trainable parameters and O(n^2) per iteration cost, achieving better performance than existing algorithms of similar complexity and close to more costly detectors.
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.