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

TL;DR

The paper studies ARIS-assisted multi-UAV networks enabling uplink NOMA from ground users to mobile UAVs. It introduces a dual-mode UAV capability and an optimization-driven hierarchical DRL framework (O-HDRL) that decomposes trajectory and mode decisions (via MADDPG) from passive beamforming and user association (via SDR/SCA optimization). A dual-mode switching scheme allows UAVs to adapt between passive reflection and active transmission to better respond to traffic and channel variations, yielding higher throughput than fixed configurations. Numerical results demonstrate improved learning stability and throughput, especially in dense networks, validating both the O-HDRL approach and the benefits of mode switching for dynamic UAV-enabled wireless networks.

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.

Figures (11)

• Figure 1: ARIS-assisted multi-UAV wireless networks.
• Figure 2: The proposed O-HDRL framework.
• Figure 3: Dual-mode UAV-assisted wireless networks.
• Figure 4: An example for the dual-mode switching scheme.
• Figure 5: The O-HDRL for dual-mode switching scheme.