Residual-aided CSI-free end-to-end learning for multiuser MIMO

Abstract

A paradigm shift from Channel State Information (CSI)-dependent architectures to intelligent, AI-native air interfaces is required as 6G wireless systems advance. Conventional Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems have substantial pilot overhead and computational complexity since they rely on explicit CSI for beamforming and interference management. This study suggests a novel Deep Unfolding Successive Over-Relaxation (DU-SOR) paradigm to overcome these constraints. In contrast to conventional end-to-end learning techniques that operate as “black boxes,” DU-SOR combines iterative residual refining with a sparse Graph Transformer. The network can intuitively solve the inverse problem without explicit channel matrix inversion thanks to this novel architecture, which uses graph priors to condition the signal estimation. Extensive empirical analyses show that the proposed framework accomplishes three main goals: (i) near-optimal performance, confirmed by a mutual information score of 0.98 at 20 dB SNR; (ii) mathematically proven scalable complexity, reducing the scaling order from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M1"> <mml:mrow> <mml:mi>𝒪</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mi>K</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M2"> <mml:mrow> <mml:mi>𝒪</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>K</mml:mi> <mml:mi>log</mml:mi> <mml:mi>K</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> via sparse attention mechanisms; and (iii) robust generalisation across various channel conditions (Rayleigh, Rician, 3GPP UMi). This work offers a scalable foundation for sustainable AI-native 6G receivers by combining sparse-graph efficiency with CSI-free operation.