Joint Trajectory and Resource Optimization for Aerial RIS-assisted Integrated TNT Networks

2026-04-14

AI Summary

This study addresses the joint optimization of communication resources and aerial platform trajectories in space-air-ground integrated networks, aiming to maximize the system’s average sum rate with the assistance of dual airborne reconfigurable intelligent surfaces (RISs). The work innovatively integrates the deployment of dual airborne RISs with a unified communication-mobility optimization framework. To efficiently solve the resulting strongly coupled non-convex problem, a novel approach is proposed based on a block coordinate descent (BCD) architecture, which jointly employs weighted minimum mean square error (WMMSE) precoding, Riemannian conjugate gradient methods for optimizing RIS phase shifts, and successive convex approximation (SCA) for refining the three-dimensional trajectories of unmanned aerial vehicles and high-altitude platforms. Simulation results demonstrate that the proposed method achieves approximately a 7.05% improvement in average sum rate over random RIS configurations, confirming its effectiveness and superiority.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Integrated terrestrial and non-terrestrial networks (ITNTNs) are regarded as a key architectural paradigm for sixth-generation (6G) wireless systems. This paper investigates a dual-aerial reconfigurable intelligent surface (RIS)-assisted ITNTN, where a terrestrial base station (TBS) and a satellite (SAT) jointly serve terrestrial and satellite users with the aid of an unmanned aerial vehicle (UAV)-mounted RIS and a high-altitude platform (HAP)-mounted RIS. We formulate an average sum-rate maximization problem by jointly optimizing the TBS and SAT precoders, the RIS phase shift matrices, and the three-dimensional trajectories of the UAV and the HAP, subject to transmit power, unit-modulus, and mobility constraints. The resulting optimization problem is highly non-convex due to the strong coupling among the transmit precoders, RIS phase shifts, and aerial platform mobility. To efficiently address this challenge, we propose a block coordinate descent (BCD) framework that integrates weighted minimum mean square error (WMMSE) optimization for precoder design, a manifold-based Riemannian conjugate gradient (RCG) method for RIS phase-shift optimization, and successive convex approximation (SCA) for trajectory optimization. The proposed algorithm is shown to converge to a stationary point. The simulation results show that the proposed joint design achieves an approximately $7.05 \%$ higher average sum-rate compared to the random RIS scheme, highlighting the effectiveness of dual-aerial RIS deployment and joint communication-mobility optimization in ITNTNs.

Problem

• Integrated Terrestrial and Non-Terrestrial Networks
• Reconfigurable Intelligent Surface
• Trajectory Optimization
• Sum-Rate Maximization
• Aerial Platforms

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Integrated Terrestrial and Non-Terrestrial Networks (ITNTN)
• Joint Trajectory and Resource Optimization
• Block Coordinate Descent (BCD)
• Riemannian Conjugate Gradient (RCG)