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.
@article{arxiv.2605.07884,
title = {Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems},
author = {Andrea Grimaldi and Christian Duffee and Eleonora Raimondo and Edoardo Piccolo and Deborah Volpe and Filip B. Maciejewski and Mario Carpentieri and Massimo Chiappini and Pedram Khalili Amiri and Davide Venturelli and Giovanni Finocchio},
journal= {arXiv preprint arXiv:2605.07884},
year = {2026}
}
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Submitted to IEEE Transactions on Wireless Communications