Interference Suppression for Massive MU-MIMO Long-Term Beamforming with Matrix Inversion Approximation

2026-04-07

AI Summary

This work addresses the severe performance degradation of polynomial- or conjugate gradient (CG)-based approximate matrix inversion in large-scale MU-MIMO long-term beamforming, caused by ill-conditioned covariance matrices due to dominant interference. To mitigate this issue, the paper proposes a subspace nulling mechanism that relies solely on long-term channel statistics, projecting the received signal onto the orthogonal complement of the dominant interference subspace. This approach implicitly preconditiones the covariance matrix without incurring additional system overhead, substantially improving its numerical conditioning and significantly reducing the number of CG iterations required for convergence. The method achieves near-optimal performance in both floating-point and fixed-point implementations. Extensive simulations based on realistic 5G ray-tracing scenarios confirm its high efficiency and robustness.

Abstract

Long-term beamforming (LTBF) is a widely-used scalable alternative to instantaneous multi-user MIMO processing that leverages slowly varying spatial channel statistics. VLSI implementations require matrix inversion that become computationally challenging for massive MIMO systems with large number of antennas. In this work, we show that dominant interferers significantly degrade the numerical conditioning of the LTBF covariance matrix, leading to severe performance loss in finite-precision implementations of polynomial and conjugate gradient (CG) based inversion methods. To address this issue, we propose a subspace nulling approach that operates solely on long-term channel statistics and acts as an implicit preconditioning step for LTBF. By projecting the received signal onto the orthogonal complement of the dominant interference subspace, the proposed method reduces the eigenvalue spread of the covariance matrix and improves numerical stability. Through ray-tracing simulations in a realistic 5G scenario, we demonstrate that the proposed method substantially reduces the number of CG iterations required to achieve near-optimal performance across floating-point and fixed-point implementations while preserving the low-overhead nature of LTBF.

Problem

• Massive MU-MIMO
• Long-term beamforming
• Interference suppression
• Matrix inversion
• Numerical conditioning

Innovation

• long-term beamforming
• massive MU-MIMO
• matrix inversion approximation
• subspace nulling
• numerical conditioning