UAV-to-Satellite Communication for 6G IoV Networks Using Beamforming Orthogonal Time Frequency Space: A Deep Q-Learning Approach

Abstract

In the paper, we propose a novel beamforming-based Orthogonal Time Frequency Space (OTFS) transmission framework for UAV-to-Satellite Communication (U2SC) tailored for 6G-Enabled Internet of Vehicles (IoV) networks. To address the unique challenges of high Doppler shifts, long-range line-of-sight (LoS) links, and fast-moving Low Earth Orbit (LEO) satellites, we adopt OTFS modulation due to its inherent robustness against doubly dispersive channels. A Uniform Linear Array (ULA) is equipped on the UAV to enable highly directional transmission. Furthermore, we propose a Deep Q-Learning (DQL) framework for adaptive beamforming, in which the beam control problem is formulated as a Markov Decision Process (MDP). By leveraging DQL, the agent learns to dynamically steer the beam to align with the satellite’s trajectory, optimizing both link quality and energy efficiency while minimizing misalignment. Simulation results demonstrate significant gains in signal robustness and beam alignment accuracy compared to conventional methods. In addition, future work will focus on building a hardware-in-the-loop (HIL) testbed using a UAV platform with phased-array antennas to validate the proposed model under real orbital satellite trajectories and Doppler conditions.