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Cellular-connected unmanned aerial vehicle (UAV) is a promising technology tounlock the full potential of UAVs in the future. However, how to achieveubiquitous three-dimensional (3D) communication coverage for the UAVs in thesky is a new challenge. In this paper, we tackle this challenge by a newcoverage-aware navigation approach, which exploits the UAV's controllablemobility to design its navigation/trajectory to avoid the cellular BSs'coverage holes while accomplishing their missions. We formulate an UAVtrajectory optimization problem to minimize the weighted sum of its missioncompletion time and expected communication outage duration, and propose a newsolution approach based on the technique of deep reinforcement learning (DRL).To further improve the performance, we propose a new framework calledsimultaneous navigation and radio mapping (SNARM), where the UAV's signalmeasurement is used not only for training the deep Q network (DQN) directly,but also to create a radio map that is able to predict the outage probabilitiesat all locations in the area of interest. This thus enables the generation ofsimulated UAV trajectories and predicting their expected returns, which arethen used to further train the DQN via Dyna technique, thus greatly improvingthe learning efficiency.

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