Joint Trajectory and Resource Optimization for Dual-aerial ARIS-assisted NOMA-TNT Networks

2026-04-14

AI Summary

This work addresses the integration of dual airborne active reconfigurable intelligent surfaces (ARIS) into non-orthogonal multiple access (NOMA) systems for 6G integrated space-air-ground networks, aiming to maximize the average sum rate through joint optimization of ARIS three-dimensional trajectories and communication resources. It presents the first framework that combines dual airborne ARIS with NOMA, overcoming the performance limitations of conventional passive RIS by jointly optimizing communication and mobility. The proposed solution employs a block coordinate descent approach, alternating between weighted minimum mean square error (WMMSE)-based beamforming design, manifold optimization for ARIS phase shifts, successive convex approximation (SCA) for amplification factors, and first-order trajectory optimization. Under identical power constraints, the proposed scheme achieves an 8.44% higher average sum rate compared to a passive RIS baseline and significantly outperforms several existing methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Integrated terrestrial and non-terrestrial networks (ITNTNs) are envisioned as a key paradigm for sixth-generation (6G) wireless systems, enabling seamless global connectivity. In this paper, we investigate a dual-aerial active reconfigurable intelligent surface (ARIS)-assisted non-orthogonal multiple access (NOMA)-based ITNTN, where a terrestrial base station (TBS) and a satellite (SAT) simultaneously serve terrestrial and satellite users with the aid of a UAV-mounted ARIS and a HAP-mounted ARIS. Users are multiplexed via power-domain NOMA with a predefined SIC decoding order. We formulate an average sum-rate maximization problem by jointly optimizing transmit beamforming, ARIS coefficients, and the 3D trajectories of the UAV and HAP, subject to power, unit-modulus, ARIS power, and mobility constraints. The problem is highly non-convex due to coupled variables, nonlinear SINR expressions, ARIS amplification, and trajectory-dependent channels. To address this, a block coordinate descent (BCD)-based framework is proposed. Specifically, beamforming is optimized via WMMSE, ARIS phase shifts via a manifold-based RCG method, amplification factors via SCA, and trajectories via first-order approximations. The proposed algorithm is guaranteed to converge to a stationary point. Simulation results demonstrate that the proposed design achieves significant performance gains over benchmark schemes. In particular, it provides an average sum-rate improvement of approximately $8.44\%$ over passive RIS under given power constraints, highlighting the benefits of dual-aerial ARIS and joint communication-mobility optimization.

Problem

• ARIS
• NOMA
• ITNTN
• trajectory optimization
• sum-rate maximization

Innovation

• Active RIS
• NOMA
• Integrated Terrestrial and Non-Terrestrial Networks
• Trajectory Optimization
• Block Coordinate Descent