Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

Andrea Grimaldi; Christian Duffee; Eleonora Raimondo; Edoardo Piccolo; Deborah Volpe; Filip B. Maciejewski; Mario Carpentieri; Massimo Chiappini; Pedram Khalili Amiri; Davide Venturelli; Giovanni Finocchio

arXiv · 2605.07884

Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

Andrea Grimaldi·Christian Duffee·Eleonora Raimondo·Edoardo Piccolo·Deborah Volpe·Filip B. Maciejewski·Mario Carpentieri·Massimo Chiappini·Pedram Khalili Amiri·Davide Venturelli·Giovanni Finocchio

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Abstract

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.

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Andrea Grimaldi, Christian Duffee, Eleonora Raimondo, Edoardo Piccolo, Deborah Volpe, Filip B. Maciejewski, Mario Carpentieri, Massimo Chiappini, Pedram Khalili Amiri, Davide Venturelli, Giovanni Finocchio. 2026-05-11. Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems. https://arxiv.org/abs/2605.07884

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