Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection

• URL: http://arxiv.org/abs/2306.16264v2
• Date: Mon, 24 Jul 2023 07:30:53 GMT
• Title: Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection
• Authors: Satoshi Takabe
• Abstract summary: Various signal detectors based on deep learning techniques and quantum(-inspired) algorithms have been proposed to improve the detection performance. This paper focuses on the simulated bifurcation (SB) algorithm, a quantum-inspired algorithm.
• Score: 7.969977930633441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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