Joint Trajectory and Resource Optimization for Dual Aerial ARIS-assisted NOMA-ITNTN

Abstract

This paper proposes a dual aerial active reconfigurable intelligent surface (ARIS)-assisted non-orthogonal multiple access (NOMA)-based integrated terrestrial and non-terrestrial networks (ITNTN), where UAV-mounted and HAP-mounted ARISs assist terrestrial and satellite users, respectively. We formulate an average sum-rate maximization problem by jointly optimizing the transmit beamforming, ARIS phase shifts and amplification factors, and the trajectories of UAV-ARIS and HAP-ARIS under mobility constraints. To address the resulting non-convex optimization problem, we develop a block coordinate descent-based joint trajectory and resource optimization framework integrating weighted minimum mean square error, manifold-based Riemannian conjugate gradient, successive convex approximation, and first-order Taylor approximation techniques. The proposed algorithm converges to a stationary point. Simulation results demonstrate that the proposed algorithm achieves an approximately 8.44% higher average sum-rate than passive RIS benchmark.