Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits

TL;DR

This paper introduces a reinforcement learning-based multi-armed bandit algorithm to enhance connectivity and energy efficiency in cellular-connected UAVs, demonstrating significant improvements in handover rate and other performance indicators.

Contribution

It presents a novel learning-based approach tailored for UAV connectivity management, optimizing multiple performance metrics simultaneously.

Findings

50% reduction in handover rate compared to blind strategies

Performance improvements depend on application-specific parameter tuning

The proposed method effectively balances connectivity and energy efficiency

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.