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September 10, 2025Open Access

Beam Self‐Adjusting Algorithm for UAV Swarms Using Deep Reinforcement Learning

Puntos clave

The BSA algorithm enables UAVs to adapt beam direction and signal power effectively, enhancing coverage.
Simulation results demonstrate a reduction in beam arc length while achieving complete beam coverage in UAV swarms.
The use of deep reinforcement learning significantly enhances communication reliability in unmanned aerial systems.
Improvements in energy efficiency are indicated with the implementation of the BSA algorithm in UAV operations.

Resumen

ABSTRACT This letter proposes a beam self‐adjusting algorithm (BSA) based on deep reinforcement learning, for narrow‐beam transmissions in unmanned aerial vehicle (UAV) swarms with directional antennas. The proposed BSA algorithm enables UAVs to adapt their transmitted beam direction, beam arc length, and signal power through learning motion characteristics of UAVs. Simulation results show that the BSA algorithm achieves complete beam coverage while reducing beam arc length, thereby improving both communication reliability and energy efficiency.

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Cite This Study

Wang et al. (Wed,) studied this question.

synapsesocial.com/papers/68c198cd9b7b07f3a061ad07 https://doi.org/https://doi.org/10.1049/ell2.70420

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