Multiple-Input-Multiple-Output~(MIMO) signal detection is central to every state-of-the-art communication system, and enhancements in error performance and computation complexity of MIMO detection would significantly enhance data rate and latency experienced by the users. Theoretically, the optimal MIMO detector is the maximum-likelihood (ML) MIMO detector; however, due to its extremely high complexity, it is not feasible for large real-world communication systems. Over the past few years, algorithms based on physics-inspired Ising solvers, like Coherent Ising machines and Quantum Annealers, have shown significant performance improvements for the MIMO detection problem. However, the current state-of-the-art is limited to low-order modulations or systems with few users. In this paper, we propose an adaptive multi-stage Ising machine-based MIMO detector that extends the performance gains of physics-inspired computation to Large and Massive MIMO systems with a large number of users and very high modulation schemes~(up to 256-QAM). We enhance our previously proposed delta Ising formulation and develop a heuristic that adaptively optimizes the performance and complexity of our proposed method. We perform extensive micro-benchmarking to optimize several free parameters of the system and evaluate our methods' BER and spectral efficiency for Large and Massive MIMO systems (up to 32 users and 256 QAM modulation).

翻译：多输入多输出（MIMO）信号检测是当前所有先进通信系统的核心，其误码性能和计算复杂度的提升将显著改善用户的数据速率和延迟。理论上，最优的MIMO检测器是最大似然（ML）MIMO检测器，但由于其极高的计算复杂度，难以应用于大规模的实际通信系统。近年来，基于物理学启发的伊辛求解器（如相干伊辛机和量子退火器）的算法在MIMO检测问题上展现出显著的性能提升。然而，当前最先进的方法仅限于低阶调制或少量用户的系统。本文提出了一种自适应多级伊辛机MIMO检测器，将物理学启发计算的性能优势扩展至大规模和超大规模MIMO系统，支持大量用户及极高调制阶数（最高256-QAM）。我们对先前提出的Delta伊辛公式进行了改进，并开发了一种启发式算法，能够自适应地优化所提方法的性能与复杂度。通过广泛的微基准测试，我们优化了系统的多个自由参数，并在大规模和超大规模MIMO系统（最多32用户、256-QAM调制）下评估了所提方法的误码率和频谱效率。