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）架构通过实现大规模空间复用提供高数据速率，是未来6G无线通信网络的关键使能技术。本文采用基于Ising机（IMs）的物理启发非传统计算方法处理此类问题。对于二进制调制，概率Ising机（PIMs）和振荡器Ising机在系统规模达2048×2048天线时，仅需100次迭代即可实现最优最大似然检测，其性能在计算可处理规模下与最优球形译码器相当，并优于工业标准最小均方误差（MMSE）算法。对于最高256阶M-QAM，本文提出的基于直接编码QAM符号的d维概率变量（p-dits）的广义PIM框架，在规模达256×256天线的系统中展现出低误码率性能，以更低的算法复杂度超越或匹敌MMSE。与二进制映射不同，p-dit交互矩阵独立于QAM阶数，从而支持自适应MIMO调制。这些结果展示了面向未来6G网络中XL-MIMO检测的一种极具前景的可扩展范式。