Upward Spatial Coverage Recovery via Movable Antenna in Low-Altitude Communications

2026-03-13

AI Summary

This work addresses the challenge of maintaining continuous and reliable communication in low-altitude three-dimensional dynamic environments, where conventional fixed antennas suffer from limited mechanical degrees of freedom and static structures. To overcome these constraints, the paper proposes a mobile antenna architecture that jointly optimizes the three-dimensional antenna placement and beamforming matrix to maximize the received signal-to-noise ratio within a discretized voxel space. It is the first to introduce mobile antennas for enhancing low-altitude communication coverage, integrating mechanical tilt adjustment to enable flexible electromagnetic radiation pattern reconfiguration and transcend traditional deployment limitations. A hybrid particle swarm optimization and simulated annealing algorithm is employed to efficiently solve the resulting non-convex, high-dimensional optimization problem, achieving coverage improvements of 26.8% and 29.65% in airspace below 300 m and 600 m, respectively—significantly outperforming fixed-antenna solutions.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The rapid proliferation of unmanned aerial vehicle (UAV) applications imposes stringent requirements on continuous and reliable communication coverage in low-altitude airspace. Conventional cellular systems built upon fixed-position antennas (FPAs) are inherently constrained by static array geometries and limited mechanical degrees of freedom, which severely restrict their ability to adapt to highly dynamic three-dimensional (3D) propagation environments. Movable antenna (MA) technology has recently emerged as a promising paradigm to overcome these limitations by actively reconfiguring electromagnetic radiation characteristics through controllable antenna positioning and array orientation, thereby enabling flexible spatial coverage adaptation. To systematically quantify the airspace coverage capability of MA-enabled systems, this paper formulates a spatial coverage maximization problem over a discretized 3D voxel space. For each voxel, the received signal-to-noise ratio (SNR) is maximized via joint optimization of the MA's 3D positions and beamforming matrices. To efficiently solve the resulting non-convex problem, a hybrid particle swarm optimization and simulated annealing framework is developed to search for high-quality antenna configurations. Simulation results demonstrate that the proposed MA design framework substantially outperforms conventional FPA-based schemes in terms of spatial coverage, achieving coverage rates of 26.8% and 29.65% for airspace below 300m and 600m, respectively. Moreover, further coverage enhancement can be attained by incorporating mechanical tilt adjustment, highlighting the strong potential of MA technology for reliable low-altitude communication coverage.

Problem

• low-altitude communications
• spatial coverage
• movable antenna
• 3D propagation environment
• coverage recovery

Innovation

• Movable Antenna
• Spatial Coverage Maximization
• Low-Altitude Communications
• 3D Beamforming
• Hybrid Optimization