Low-Complexity Tensor Beamforming for RIS-Aided Multiuser Multistream MIMO Systems
This work addresses the high computational cost of joint beamforming design in RIS-assisted MIMO systems, offering a more efficient solution for practical deployment.
The paper proposes a low-complexity tensor-based alternating optimization method for joint active and passive beamforming in RIS-aided multiuser multistream MIMO systems, achieving near-benchmark performance with reduced computational complexity and improved scalability for large RIS arrays.
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.