Kavli Affiliate: Bo Gu
| First 5 Authors: Songhan Zhao, Shimin Gong, Bo Gu, Lanhua Li, Bin Lyu
| Summary:
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.
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