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In this paper, we present a novel distributed UAVs beam reforming approach todynamically form and reform a space-selective beam path in addressing thecoexistence with satellite and terrestrial communications. Despite the uniqueadvantage to support wider coverage in UAV-enabled cellular communications, thechallenges reside in the array responses' sensitivity to random rotationalmotion and the hovering nature of the UAVs. A model-free reinforcement learning(RL) based unified UAV beam selection and tracking approach is presented toeffectively realize the dynamic distributed and collaborative beamforming. Thecombined impact of the UAVs' hovering and rotational motions is consideredwhile addressing the impairment due to the interference from the orbitingsatellites and neighboring networks. The main objectives of this work aretwo-fold: first, to acquire the channel awareness to uncover its impairments;second, to overcome the beam distortion to meet the quality of service (QoS)requirements. To overcome the impact of the interference and to maximize thebeamforming gain, we define and apply a new optimal UAV selection algorithmbased on the brute force criteria. Results demonstrate that the detrimentaleffects of the channel fading and the interference from the orbiting satellitesand neighboring networks can be overcome using the proposed approach.Subsequently, an RL algorithm based on Deep Q-Network (DQN) is developed forreal-time beam tracking. By augmenting the system with the impairments due tohovering and rotational motion, we show that the proposed DQN algorithm canreform the beam in real-time with negligible error. It is demonstrated that theproposed DQN algorithm attains an exceptional performance improvement. We showthat it requires a few iterations only for fine-tuning its parameters withoutobserving any plateaus irrespective of the hovering tolerance.

Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication  over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground  Integrated Networks