Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV With Deep Reinforcement Learning

Abstract

Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their air-ground communications. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV's controllable mobility to design its navigation/trajectory to avoid the cellular BSs' coverage holes while accomplishing their missions. To this end, we formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, which, however, cannot be solved by the standard optimization techniques due to the lack of an accurate and tractable end-to-end communication model in practice. To overcome this difficulty, we propose a new solution approach based on the technique of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling double deep Q network (dueling DDQN) with multi-step learning, we first propose a UAV navigation algorithm based on direct RL, where the signal measurement at the UAV is used to directly train the action-value function of the navigation policy. To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV's signal measurement is used not only for training the DQN directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.