Exploiting NOMA Transmissions in Multi-UAV-assisted Wireless Networks: From Aerial-RIS to Mode-switching UAVs

2024-12-29

AI Summary

To address severe interference and limited capacity in UAV-assisted NOMA networks, this paper proposes a multi-UAV cooperative architecture leveraging aerial reconfigurable intelligent surfaces (ARIS), introducing for the first time UAV nodes with integrated reflective/transmissive dual-mode capabilities. A hierarchical deep reinforcement learning framework—optimization-driven H-DRL (O-HDRL)—is developed to jointly optimize UAV trajectories, passive ARIS beamforming, NOMA power allocation, and dynamic dual-mode switching, thereby decoupling multi-agent decision-making from conventional subproblems. By integrating MADDPG with a hybrid optimization-learning coordination mechanism, the framework ensures training stability while significantly improving convergence efficiency. Experimental results demonstrate that the proposed scheme achieves a 23.6% throughput gain over fixed-ARIS baselines and consistently outperforms state-of-the-art alternatives across all evaluated metrics.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

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

Abstract

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs' trajectory planning and operating modes, the ARIS's passive beamforming, and the GUs' transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs' trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.

Problem

• Information Transmission Efficiency
• Signal Interference Reduction
• Network Capacity Enhancement

Innovation

• NOMA-ARIS Integration
• O-HDRL Learning Method
• Drone-Assisted Wireless Networks