Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection

TL;DR

This paper introduces a deep-unfolded simulated bifurcation algorithm for massive MIMO signal detection, combining quantum-inspired methods with deep learning to enhance detection accuracy.

Contribution

It proposes a novel deep-unfolded SB algorithm that improves detection performance by eliminating local minima and optimizing internal parameters through deep learning.

Findings

Significant performance improvement in massive MIMO detection.

Effective elimination of local minima in the detection process.

Enhanced detection accuracy compared to traditional methods.

Abstract

Multiple-input multiple-output (MIMO) is a key ingredient of next-generation wireless communications. Recently, various MIMO signal detectors based on deep learning techniques and quantum(-inspired) algorithms have been proposed to improve the detection performance compared with conventional detectors. This paper focuses on the simulated bifurcation (SB) algorithm, a quantum-inspired algorithm. This paper proposes two techniques to improve its detection performance. The first is modifying the algorithm inspired by the Levenberg-Marquardt algorithm to eliminate local minima of maximum likelihood detection. The second is the use of deep unfolding, a deep learning technique to train the internal parameters of an iterative algorithm. We propose a deep-unfolded SB by making the update rule of SB differentiable. The numerical results show that these proposed detectors significantly improve the signal detection performance in massive MIMO systems.