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Reinforcement Learning Based Topology Control for UAV Networks

Engineering and Technology

Reinforcement Learning Based Topology Control for UAV Networks

T. Yoo, S. Lee, et al.

This research, conducted by Taehoon Yoo, Sangmin Lee, Kyeonghyun Yoo, and Hwangnam Kim, introduces a cutting-edge reinforcement learning system for optimizing UAV network connectivity. By focusing on UAV positions, interference, and energy use, this study reshapes network structure efficiently using DDPG, showcasing significant effectiveness across various topologies.

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Playback language: English
Abstract
This paper proposes a reinforcement learning (RL)-based topology control system for unmanned aerial vehicle (UAV) networks. The system optimizes network connectivity by analyzing the relative positions of UAVs, considering interference and energy consumption, and reshaping the network's logical structure. Deep deterministic policy gradient (DDPG) is employed to allow each UAV to adapt its connectivity, minimizing learning time by adjusting the number of steps used for parameter learning. Simulations demonstrate the system's effectiveness in various topologies.
Publisher
Sensors
Published On
Jan 13, 2023
Authors
Taehoon Yoo, Sangmin Lee, Kyeonghyun Yoo, Hwangnam Kim
Tags
reinforcement learning
UAV networks
topology control
network connectivity
deep deterministic policy gradient
energy consumption
simulations

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