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

2026-05-08

AI Summary

This work addresses the challenge of high-complexity optimal signal detection in sixth-generation (6G) extremely large-scale MIMO (XL-MIMO) systems by proposing an efficient detection method that integrates physics-inspired probabilistic computing with Ising machines. By introducing d-dimensional probability variables (p-dits), the approach establishes a modulation-order-agnostic framework that supports adaptive modulation and, for the first time, achieves near maximum-likelihood performance in a 2048×2048 antenna configuration within only 100 iterations. The method maintains low bit error rates and computational complexity even under 256-QAM signaling, significantly outperforming conventional schemes such as MMSE. This advancement offers a promising pathway toward the practical deployment of XL-MIMO systems in future 6G networks.

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.

Problem

• XL-MIMO
• 6G
• MIMO detection
• high-order modulation
• bit-error-rate

Innovation

• Ising machines
• probabilistic computing
• XL-MIMO
• p-dits
• 6G wireless