42 research outputs found
Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection
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.Comment: 5pages, 4 figure