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.

翻译：多输入多输出（MIMO）是下一代无线通信的关键技术。近年来，基于深度学习技术和量子（启发式）算法的各种MIMO信号检测器被提出，以提升相较于传统检测器的检测性能。本文聚焦于模拟分叉（SB）算法，这是一种量子启发式算法，并提出两种改进其检测性能的技术。第一种是受Levenberg-Marquardt算法启发对算法进行修改，以消除最大似然检测的局部极小值。第二种是使用深度展开这一深度学习技术，训练迭代算法的内部参数。我们通过使SB的更新规则可微分，提出了深度展开的SB算法。数值结果表明，这些提出的检测器显著提升了大规模MIMO系统中的信号检测性能。