Low-Complexity Tensor Beamforming for RIS-Aided Multiuser Multistream MIMO Systems

TL;DR

This paper introduces a low-complexity tensor-based optimization method for joint active and passive beamforming in RIS-assisted multi-user MIMO systems, improving scalability and computational efficiency.

Contribution

It proposes a novel tensor alternating optimization approach leveraging multilinear structure for efficient beamforming in RIS-aided MIMO systems.

Findings

Approaches benchmark performance with reduced complexity.

Achieves near-benchmark performance in simulations.

Enhances large-RIS system scalability.

Abstract

We address joint active and passive beamforming for uplink RIS-assisted multi-user multi-stream MIMO systems with joint detection. The coupled design of the receive combiner, block-diagonal user precoders, and RIS phase vector is formulated through a third-order composite channel tensor. Exploiting this multilinear structure, we propose a multi-stream tensor alternating optimization method that updates the combiner, user precoders, and RIS coefficients via low-dimensional tensor projections. Simulations show that the proposed method approaches a multi-start alternating-optimization benchmark while reducing computational complexity and improving large-RIS scaling.