Sparse Array Design for Near-Field MU-MIMO: Reconfigurable Array Thinning Approach

TL;DR

This paper introduces a reconfigurable array thinning method for near-field MU-MIMO systems that optimizes antenna activation to improve performance without mechanical reconfiguration.

Contribution

It proposes a novel reconfigurable array thinning approach using particle swarm optimization to enhance near-field MU-MIMO performance.

Findings

GTA effectively suppresses grating lobes in sparse arrays.

STA achieves near-movable antenna performance with fewer hardware complexities.

Reconfigurable array design improves performance and flexibility in dynamic scenarios.

Abstract

Future wireless networks, deploying thousands of antenna elements, may operate in the radiative near-field (NF), enabling spatial multiplexing across both angle and range domains. Sparse arrays have the potential to achieve comparable performance with fewer antenna elements. However, fixed sparse array designs are generally suboptimal under dynamic user distributions, while movable antenna architectures rely on mechanically reconfigurable elements, introducing latency and increased hardware complexity. To address these limitations, we propose a reconfigurable array thinning approach that selectively activates a subset of antennas to form a flexible sparse array design without physical repositioning. We first analyze grating lobes for uniform sparse arrays in the angle and range domains, showing their absence along the range dimension. Based on the analysis, we develop two particle swarm optimization-based strategies: a grating-lobe-based thinned array (GTA) for grating-lobe suppression and a sum-rate-based thinned array (STA) for multiuser sum-rate maximization. Simulation results demonstrate that GTA outperforms conventional uniform sparse arrays, while STA achieves performance comparable to movable antennas, thereby offering a practical and efficient array deployment strategy without the associated mechanical complexity.