Abstract
We optimize the deployment of an aerial reconfigurable intelligent surface (ARIS) to assist the high altitude platform (HAP) downlink transmission when the direct link is blocked. Specifically, we maximize the received signal-to-noise ratio (SNR) of the ground users by jointly optimizing the trajectory and the phase-shift of the ARIS with the consideration of the unknown movement of HAP, which is caused by the changes in the stratospheric wind and air density. Due to the non-convex nature of the formulated optimization problem, we decouple the optimization problem and propose an alternative two-stage optimization. By proving that the movement of the HAP follows a finite state Markov stochastic process, we first learn the optimal ARIS trajectory via model-free reinforcement learning, and then adjust the optimal phase-shift of the ARIS, alternately. Next, the convergence of the proposed algorithm is analyzed based on the obtained upper bound of the accumulated reward. The numerical results show substantial performance gain of HAP communications with the optimized ARIS assistance.
Highlights
D UE TO its high mobility, deployment flexibility, and strong line-of-sight (LoS) link to terrestrial users, unmanned aerial vehicle (UAV) communication is regarded as one of the most promising technologies in beyond 5G/6G networks
By regarding the unknown movement of the high altitude platform (HAP) as a part of the radio environment, we prove that the HAP movement follows a finite state Markov stochastic process and the deployment of aerial reconfigurable intelligent surface (ARIS) is a Markov decision process (MDP)
We evaluate the performance of the ARISassisted HAP communications via simulations
Summary
D UE TO its high mobility, deployment flexibility, and strong line-of-sight (LoS) link to terrestrial users, unmanned aerial vehicle (UAV) communication is regarded as one of the most promising technologies in beyond 5G/6G networks. The integration of HAPs for enhancing the coverage and robustness of terrestrial communication networks have been studied in some works [1], [2]. Reference [4] considered RIS-assisted UAV communications, where the trajectory of the UAV and the phase shifts of a RIS were jointly optimized to maximize the average achievable rate. A RIS-assisted UAV system was optimized by jointly designing the trajectory, power and phase-shift of the RIS [6]. We jointly optimize the trajectory and the phase-shift of the ARIS under the unknown and dynamic radio environment. The convergence of the proposed algorithm is analyzed and some insightful remarks are given based on our theoretical analysis and simulations
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