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Learning-based Localization of Mobile Users for Throughput Maximization in UAV Networks

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Abstract

In this paper, we design a new UAV-assisted communication system relying on the shortest flight path of the UAV while maximizing the amount of data transmitted to mobile devices. In the considered system, we assume that UAV does not have the knowledge of user’s location except their initial position. We propose a framework which is based on the likelihood of mobile users presence in a grid with respect to their probability distribution. Then, a deep reinforcement learning technique is developed for finding the trajectory to maximize the throughput in a specific coverage area. Numerical results are presented to highlight how our technique strike a balance between the throughput achieved, trajectory, and the complexity.
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Dates and versions

hal-03448178 , version 1 (25-11-2021)

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Arzhang Shahbazi, Marco Di Renzo. Learning-based Localization of Mobile Users for Throughput Maximization in UAV Networks. 2021 IEEE 4th 5G World Forum (5GWF), Oct 2021, Montreal, Canada. pp.130-134, ⟨10.1109/5GWF52925.2021.00030⟩. ⟨hal-03448178⟩
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