1 research outputs found
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support
The growing deployment of drones in a myriad of applications relies on
seamless and reliable wireless connectivity for safe control and operation of
drones. Cellular technology is a key enabler for providing essential wireless
services to flying drones in the sky. Existing cellular networks targeting
terrestrial usage can support the initial deployment of low-altitude drone
users, but there are challenges such as mobility support. In this paper, we
propose a novel handover framework for providing efficient mobility support and
reliable wireless connectivity to drones served by a terrestrial cellular
network. Using tools from deep reinforcement learning, we develop a deep
Q-learning algorithm to dynamically optimize handover decisions to ensure
robust connectivity for drone users. Simulation results show that the proposed
framework significantly reduces the number of handovers at the expense of a
small loss in signal strength relative to the baseline case where a drone
always connect to a base station that provides the strongest received signal
strength.Comment: Under revie