Leveraging UAVs for Coverage in Cell-Free Vehicular Networks

Abstract

The success in transitioning towards smart cities relies on the availability of information and communication technologies that meet the demands of this transformation. The terrestrial infrastructure presents itself as a preeminent component in this change. UAVs empowered with artificial intelligence are expected to become an integral component of future smart cities that provide seamless coverage for vehicles on highways with poor cellular infrastructure. We introduce UAVs cell-free network for providing coverage to vehicles entering a highway that is not covered; however, UAVs have limited energy resources and cannot serve the entire highway all the time. Furthermore, the deployed UAVs have insufficient knowledge about the environment; therefore, it is challenging to control a swarm of UAVs to achieve efficient communication coverage. To address these challenges, we formulate the trajectories decisions making as a Markov decision process where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. Finally, simulations results are provided to verify our findings.