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Energy efficiency performance in RIS-based integrated satellite-aerial-terrestrial relay networks with deep reinforcement learning  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Energy efficiency performance in RIS-based integrated satellite-aerial-terrestrial relay networks with deep reinforcement learning

作者:Li, Jiao[1];Xue, Huajian[2];Wu, Min[3];Wang, Fucheng[2];Gao, Tieliang[1];Zhou, Feng[4]

第一作者:李敬

通讯作者:Xue, HJ[1];Wu, M[2]

机构:[1]Xinxiang Univ, Key Lab Data Anal & Financial Risk Predict, Xinxiang 453000, Peoples R China;[2]Tongling Univ, Tongling 244000, Peoples R China;[3]Space Engn Univ, Sch Space Informat, Beijing 101400, Peoples R China;[4]Yancheng Inst Technol, Sch Informat Technol, Yancheng 224000, Peoples R China

第一机构:新乡学院

通讯机构:[1]corresponding author), Tongling Univ, Tongling 244000, Peoples R China;[2]corresponding author), Space Engn Univ, Sch Space Informat, Beijing 101400, Peoples R China.

年份:2023

卷号:2023

期号:1

外文期刊名:EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING

收录:;EI(收录号:20234815125075);Scopus(收录号:2-s2.0-85178154123);WOS:【SCI-EXPANDED(收录号:WOS:001114095100001)】;

基金:The authors would like to extend their gratitude to the anonymous reviewers for their valuable and constructive comments, which have largely improved and clarified this paper.

语种:英文

外文关键词:Satellite-aerial-ground integrated networks (SAGINs); Hybrid FSO/RF links; Reconfigurable intelligent surface (RIS); Long short-term memory-deep deterministic policy gradient (LSTM-DDPG); Deep reinforcement learning (DRL)

摘要:Integrated satellite-aerial-terrestrial relay networks (ISATRNs) play a vital role in next-gen networks, particularly those with high-altitude platforms (HAP). This study introduces a new model for hybrid optical/RF-based HAP-enabled ISATRNs, incorporating reconfigurable intelligent surfaces (RIS) on unmanned aerial vehicles (UAVs) to optimize access in dense urban areas. Non-orthogonal multiple access is employed for improved spectrum efficiency. The objective is to jointly optimize UAV trajectory, RIS phase shift, and active transmit beamforming while considering energy consumption. A deep reinforcement learning approach using LSTM-DDQN framework is proposed. Numerical results show the effectiveness of our algorithm over traditional DDQN, with higher single-step exploration reward and evaluation metrics.

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