  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.
