Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits

• URL: http://arxiv.org/abs/2009.09815v1
• Date: Mon, 21 Sep 2020 12:35:23 GMT
• Title: Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits
• Authors: M. Mahdi Azari, Atefeh Hajijamali Arani, Fernando Rosas
• Abstract summary: We propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV. We show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter.
• Score: 71.28712804110974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A cellular-connected unmanned aerial vehicle (UAV)faces several key challenges concerning connectivity and energy efficiency. Through a learning-based strategy, we propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV by taking into account its time of task completion. By formulating the problem as a function of UAV's velocity, we show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter, e.g. 50% reduction in HO rate as compared to a blind strategy. However, results reveal that the optimal combination of the learning parameters depends critically on any specific application and the weights of PIs on the final objective function.

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