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

Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection