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

TL;DR

This paper introduces physics-inspired probabilistic computing methods, specifically Ising machines, for efficient large-scale MIMO detection in future 6G wireless systems, achieving high accuracy with reduced complexity.

Contribution

It develops a generalized PIM-inspired framework using p-dits for scalable, adaptive MIMO detection across various modulation schemes and system sizes.

Findings

PIMs achieve optimal ML detection for 2048x2048 systems in 100 iterations.

The framework performs well up to 256x256 MIMO with low BER.

The p-dit interaction matrix is modulation-independent, enabling adaptability.

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.