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

Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Emil Björnson, Ahmed M. Eltawil

TL;DR

This work develops 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.

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.

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

TL;DR

This work develops 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.

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.
Paper Structure (10 sections, 19 equations, 4 figures, 1 algorithm)

This paper contains 10 sections, 19 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Grating Lobes in the Near-field
  4. Grating Lobes in the Angle Domain
  5. Absence of Grating Lobes in the Range Domain
  6. Proposed Optimization Methods
  7. PSO-based Array Thinning for Grating-Lobe Suppression
  8. PSO-based Array Thinning for Sum-Rate Maximization
  9. Simulation Results
  10. Conclusion

Figures (4)

  • Figure 1: Beam pattern in angle and range domain: grating lobes appear only in the angular domain. Here we set $f_c = \unit[15]{GHz}$, $N = 256$, $d=2\lambda$, $r_{\mathrm{\hbox{0}}} = \unit[346]{m}$ and $r_{\mathrm{\hbox{RD} }}=\unit[10.3]{km}$.
  • Figure 2: Sum-rate for SULA when UE are distributed only along the range.
  • Figure 3: Cumulative distribution function of the sum-rate across different sparse arrays.
  • Figure 4: Average sum-rate vs. number of users for different sparse arrays.