Joint Sparsity and Beamforming Design for RDARS-Aided Systems

TL;DR

This paper proposes a joint sparsity and beamforming design for RDARS-assisted systems, optimizing array configuration and signal processing to enhance communication performance with low complexity.

Contribution

It introduces a novel sparse array formation for RDARS, deriving closed-form solutions for simple cases and an AO algorithm for general scenarios.

Findings

Optimized sparsity significantly improves sum rate.

Low-complexity sparsity optimization is effective.

Closed-form solutions are available for single and dual UE cases.

Abstract

Reconfigurable distributed antennas and reflecting surface (RDARS) has emerged as a promising architecture for communication and sensing performance enhancement. In particular, the new selection gain can be achieved by leveraging the dynamic working mode selection between connection and reflection modes, whereas low-complexity element configuration remains an open issue. In this paper, we consider a RDARS-assisted communication system, where the connected elements are formed as a uniform sparse array for simplified mode configuration while achieving enlarged physical array aperture. The sum rate maximization problem is then formulated by jointly optimizing the active and passive beamforming matrices and sparsity of connected element array. For the special cases of a single user equipment (UE) and two UEs, the optimal sparsity designs are derived in closed-form. Then, for an arbitrary number of UEs, a weighted minimum mean-square error-based alternating optimization (AO) algorithm is proposed to tackle the non-convex optimization problem. Numerical results demonstrate the importance of optimizing the sparsity and the effectiveness of low-complexity sparsity optimization.