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Haowei MENG, Ning XIN, Hao QIN, et al., “A Recursive DRL-based Resource Allocation Method for Multibeam Satellite Communication Systems,” Chinese Journal of Electronics, vol. 33, no. 2, article no. , 2024 doi: 10.23919/cje.2022.00.135
Citation: Haowei MENG, Ning XIN, Hao QIN, et al., “A Recursive DRL-based Resource Allocation Method for Multibeam Satellite Communication Systems,” Chinese Journal of Electronics, vol. 33, no. 2, article no. , 2024 doi: 10.23919/cje.2022.00.135

A Recursive DRL-based Resource Allocation Method for Multibeam Satellite Communication Systems

doi: 10.23919/cje.2022.00.135
Funds:  This work has been supported by the National Natural Science Foundation of China (62071354), the Key Research and Development Program of Shaanxi (2022ZDLGY05-08), and the ISN State Key Laboratory
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  • Author Bio:

    Haowei MENG received the B.S. degree on Communication Engineering from Zheng Zhou University, Henan, China, 2020. He is currently working towards his master’s degree from Xidian University, Xi’an, China. His research interests include wireless resource management and reinforcement learning. (Email: 20011210418@stu.xidian.edu.cn)

    Ning XIN received the Ph.D. degree from CAS in 2014, M.S. degree from Naval Aeronautical Engineering Institute in 2007, and B.S. degree from Yantai University in 2004. He is a researcher in the Institute of Telecommunication Satellite, China Academy of Space Technology, Beijing, China. His research interests are spacecraft design and satellite payload design. (Email: xinning7@sina.com)

    Hao QIN (corresponding author) received the B.S., M.S., and Ph.D. degrees in Communication and Information systems from Xidian University, Xi’an, China, in 1996, 1999, and 2004, respectively. In 2004, he joined the School of Telecommunications Engineering, Xidian University, where he is currently an Associate Professor of communications and information systems. His research interests include wireless communications and satellite communications. (Email: hqin@mail.xidian.edu.cn)

    Di ZHAO received the B.S. degree on Communication Engineering from Shandong Normal University, Jinan, China, 2018. She is currently working towards her Ph.D. degree from Xidian University, Xi’an, China. Her research interests include wireless resource management, satellite communications and reinforcement learning in wireless networks. (Email: dzhao_1@stu.xidian.edu.cn)

  • Received Date: 2022-05-17
  • Accepted Date: 2023-06-20
  • Available Online: 2023-07-20
  • Optimization-based radio resource management (RRM) has shown significant performance gains on high-throughput satellites (HTSs). However, as the number of allocable on-board resources increases, traditional RRM are difficult to apply in real satellite systems due to its intense computational complexity. DRL is a promising solution for the resource allocation problem due to its model-free advantages. Nevertheless, the action space faced by DRL increases exponentially with the increase of communication scale, which leads to an excessive exploration cost of the algorithm. In this paper, we propose a recursive frequency resource allocation algorithm based on long-short term memory (LSTM) and proximal policy optimization (PPO), called PPO-RA-LOOP, where RA means resource allocation and LOOP means the algorithm outputs actions in a recursive manner. Specifically, the PPO algorithm uses LSTM network to recursively generate sub-actions about frequency resource allocation for each beam, which significantly cut down the action space. In addition, the LSTM-based recursive architecture allows PPO to better allocate the next frequency resource by using the generated sub-actions information as a prior knowledge, which reduces the complexity of the neural network. The simulation results show that PPO-RA-LOOP achieved higher spectral efficiency and system satisfaction compared with other frequency allocation algorithms.
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