Position Optimization for Two-layer Movable Antenna Systems

2025-11-19

AI Summary

Conventional single-layer movable antenna (SL-MA) systems suffer from high control complexity and hardware cost due to fine-grained, cell-wide antenna mobility. Method: This paper proposes a two-layer movable antenna (TL-MA) architecture: coarse-grained coverage optimization is achieved via large-scale subarray displacement at the upper layer, while fine-grained beam adaptation is realized through small-scale intra-subarray antenna adjustments at the lower layer. This decouples multi-scale mobility, substantially reducing total motor displacement and energy consumption. A joint design framework—integrating alternating optimization with particle swarm optimization—is further developed to co-optimize subarray positions, intra-subarray antenna layouts, and receive beamforming. Results: Simulations demonstrate that TL-MA achieves sum-rate performance comparable to SL-MA while reducing total motor travel distance by over 60%, effectively balancing communication performance and implementation overhead.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Movable antenna (MA) is a promising technology for improving the performance of wireless communication systems by providing new degrees-of-freedom (DoFs) in antenna position optimization. However, existing works on MA systems have mostly considered element-wise single-layer MA (SL-MA) arrays, where all the MAs move within the given movable region, hence inevitably incurring high control complexity and hardware cost in practice. To address this issue, we propose in this letter a new two-layer MA array (TL-MA), where the positions of MAs are jointly determined by the large-scale movement of multiple subarrays and the small-scale fine-tuning of per-subarray MAs. In particular, an optimization problem is formulated to maximize the sum-rate of the TL-MA-aided communication system by jointly optimizing the subarray-positions, per-subarray (relative) MA positions, and receive beamforming. To solve this non-convex problem, we propose an alternating optimization (AO)-based particle swarm optimization (PSO) algorithm, which alternately optimizes the positions of subarrays and per-subarray MAs, given the optimal receive beamforming. Numerical results verify that the proposed TL-MA significantly reduces the sum-displacement of MA motors (i.e., the total moving distances of all motors) of element-wise SL-MA, while achieving comparable rate performance.

Problem

• Optimizing antenna positions to enhance wireless communication performance
• Reducing control complexity and hardware cost in movable antenna systems
• Maximizing sum-rate with two-layer antenna array configuration

Innovation

• Two-layer movable antenna array design
• Alternating optimization with particle swarm algorithm
• Joint subarray and per-element position optimization