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

TL;DR

This paper introduces a subspace nulling technique for massive MU-MIMO long-term beamforming that improves matrix inversion stability and reduces computational complexity by mitigating dominant interference effects.

Contribution

It proposes a novel subspace nulling approach that enhances LTBF performance and efficiency without requiring real-time channel updates or high computational costs.

Findings

Reduces eigenvalue spread of the covariance matrix.

Decreases CG iterations needed for near-optimal performance.

Maintains low overhead of LTBF in realistic 5G scenarios.

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.