Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits
This work addresses connectivity and energy issues for UAVs in mobile cellular networks, but it is incremental as it builds on existing MAB methods with application-specific tuning.
The paper tackled connectivity and energy efficiency challenges in cellular-connected UAVs by proposing a novel multi-armed bandit algorithm, resulting in improvements such as a 50% reduction in handover rate compared to a blind strategy.
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.