基于DRL的RIS輔助空地一體化網(wǎng)絡(luò)多目標優(yōu)化

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中圖分類號：TN927.2 文獻標志碼：B

Abstract：Aiming at the multi-objective optimization problem in reconfigurable intelligence surface （RIS）-assted integrated aerial-terrestrial networks（IATNs），an algorithmic framework is proposed to jointly optimize the active transmit beamforming matrix，passive RIS，reflect beamforming matrix and unmanned aerial vehicle（UAV） trajectory using deep reinforcement learning（DRL）.An algorithmic framework using DRL is proposed to jointly optimize the active transmit beamforming matrix，passive RIS， and UAV trajectory. A multi-objective constrained optimization model for system and rate maximization is established using the base station active beamforming technique and non-orthogonal multiple access （NOMA） technique. The DRL-based deep deterministic policy gradient （DDPG）framework is used to optimize the base station active transmit beamforming matrix，RIS passve reflective beamforming matrix and UAV trajectory. The results show that the DDPG framework integrating adaptive operator mechanism outperforms the traditional iterative optimization standard scheme in terms of system performance， execution time，and higher computational speed，and the system and rate can be improved by about 18% ：

Key words： reconfigurable intellgent surface（RIS）； integrated aerial-terrestrial networks（IATNs）;non-orthogonal multiple access（NOMA）； deep reinforcement learning（DRL）； deep deterministic policygradient（DDPG）；unmanned aerial vehicle（UAV）

0 引言

隨著物聯(lián)網(wǎng)（internetofthings，IoT）技術(shù)的廣泛部署與深入應用，空地一體化網(wǎng)絡(luò)（integratedaerial-terrestrialnetworks，IATNs）已成為下一代應急通信場景中的核心解決方案。（剩余10210字）