Journal
2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/GCWkshps50303.2020.9367580
Keywords
Reinforcement learning; multi-armed bandit; unmanned aerial vehicle (UAV); cellular networks; handover rate; energy efficiency
Funding
- Catalan and Spanish grants [2017-SGR-01479, RTI2018-099722-B-I00]
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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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