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Full title: A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints
Unmanned Aerial Vehicles (UAVs) are gaining popularity in many aspects of wireless communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed.
However, UAV-BSs have their limitations and complications, for instance, limited available energy. In addition, when several UAV-BSs are deployed to provide coverage to a specific area, the possibility of inter-UAV collisions and the interference to ground users increase.
We propose Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) based methods to deploy UAV-BSs under energy constraints to provide efficient and fair coverage to the ground users, while minimizing inter-UAV collisions and interference to ground users.
The proposed methods outperform the baseline methods by an average increase of 38.94% in system fairness, 42.54% in individual user coverage, and 15.04% in total system coverage, in comparison with the baseline methods.
Full Article: IEEE Open Journal of Vehicular Technology, Volume 1
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