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Beam Self-Adjusting Algorithm for UAV Swarms Using Deep Reinforcement Learning
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DOI：10.1049/ell2.70420.png)
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Abstract
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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.
Keywords:
ad hoc networks
autonomous aerial vehicles
directive antennas
learning (artificial intelligence)
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