Unmanned Aerial Vehicles (UAVs) Have Been Adopted as Aerial Base Stations (ABSs) to Provide Wireless Connectivity to Ground Users in Events of Increased Network Demand, and Points-Of-Failure Infrastructure (Such as in Disasters). However, with the Existing Crowded Radio Frequency (RF) Spectrum, UAV ABSs Cannot Provide High-Data-Rate Communication Required in 5G and beyond. to Address This Challenge, Visible Light Communication (VLC) is Proposed to Be Equipped on UAVs to Take Advantage of the Flexible and On-Demand Deployment Feature of the UAV, and the High-Data-Rate Communication of the VLC. However, VLC Has Strong Alignment Requirements between Transceivers, Therefore, How to Determine the Position and Orientation of the UAV is Critically Important for Sum-Throughput Improvement. in This Paper, We Propose Two Q-Learning based Methods to Maximize the Sum throughput of the Wireless Visible-Light UAV Network by Jointly Controlling the Position and Orientation of the UAV. the Results Show that the Proposed Approaches Can Achieve a Network-Wide Data Rate Very Close to the Optimal Solution Obtained by Exhaustive Search and Outperform Up to 18% Compared with the Intuitive Centroid-Based Method. Computation Complexity is Also Evaluated, Where Results Showing that the Proposed Two Q-Learning based Methods Can Both Consume Less Computational Time, I.e., Approximately 9 Times and 210 Times Less on Average Than that of the Exhaustive Search Approach.
Y. Long and N. Cen, "Q-Learning for Sum-Throughput Optimization in Wireless Visible-Light UAV Networks," IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10225783
Keywords and Phrases
Q-learning; Throughput Optimization; Unmanned Aerial Vehicles; Visible Light Networking
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Jan 2023