Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through massive spatial multiplexing. Here, we approach these problems with physics-inspired unconventional computing based on Ising machines (IMs). For binary modulation, probabilistic IMs (PIMs) and oscillator-based IMs achieve optimal ML detection with systems up to 2048x2048 antennas with only 100 iterations, matching optimal sphere decoder performance for computationally treatable sizes and outperforming the minimum mean-square error (MMSE) industrial standard. For M-QAM up to 256, a generalized PIM-inspired framework, based on d-dimensional probabilistic variables (p-dits) that directly encode QAM symbols, shows low bit-error-rate across sizes up to 256x256 antennas, outperforming or matching MMSE with reduced algorithmic complexity. Unlike the binary mapping, the p-dit interaction matrix is independent of the QAM order, enabling adaptive MIMO modulation. These results show a promising scalable paradigm for XL MIMO detection in future 6G networks.

翻译：超大规模多输入多输出（XL-MIMO）架构通过大规模空间复用实现高数据速率，是即将到来的6G无线通信网络的关键使能技术。本文采用基于伊辛机（IMs）的物理启发非传统计算方法来应对这些挑战。对于二进制调制，概率伊辛机（PIMs）和振荡器伊辛机在最多2048×2048天线的系统中仅需100次迭代即可实现最优最大似然（ML）检测，其性能在计算可处理规模下匹配最优球形译码器，并优于工业标准最小均方误差（MMSE）方法。对于最高256阶M-QAM调制，基于d维概率变量（p-dits）直接编码QAM符号的广义PIM启发框架，在最多256×256天线的系统中展现出低误码率，其性能优于或匹配MMSE，同时降低了算法复杂度。与二进制映射不同，p-dit交互矩阵不依赖于QAM阶数，从而支持自适应MIMO调制。这些结果为未来6G网络中的XL-MIMO检测提供了一种有前景的可扩展范式。