文献类型：期刊文献

英文题名：Incorporating Distributed DRL Into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

作者：Wang, Chao[1];Liu, Lei[2,3];Jiang, Chunxiao[4,5];Wang, Shangguang[6];Zhang, Peiying[1,6];Shen, Shigen[7]

机构：[1]China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China;[2]Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China;[3]Xidian Guangzhou Inst Technol, Guangzhou 510555, Peoples R China;[4]Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China;[5]Tsinghua Univ, Tsinghua Space Ctr, Beijing 100084, Peoples R China;[6]Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China;[7]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China

年份：2022

卷号：16

期号：3

起止页码：434

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING

收录：SCI-EXPANDED(收录号：WOS:000797421100014)、、EI(收录号：20215211398357)、Scopus(收录号：2-s2.0-85121788345)、WOS

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800, in part by the National Natural Science Foundation of China under Grant 61922050 and Grant 62001357, in part by the Shandong Provincial Natural Science Foundation, China under Grant ZR2020MF006, in part by the Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant SKLNST-2021-1-17, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515110079, in part by the China Postdoctoral Science Foundation under Grant 2021M692501, and in part by the Graduate Student Innovation Project Funding Project of China University of Petroleum (East China) under Grant YCX2021127. The guest editor coordinating the reviewof this manuscript and approving it for publication was Dr. Ming Xiao.

语种：英文

外文关键词：Signal processing algorithms; Servers; Network resource management; Heuristic algorithms; Computer architecture; Dynamic scheduling; Training; Distributed learning; deep reinforcement learning; space-air-ground integrated network; wireless communication network; storage resource management

外文摘要：Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The effective management of SAGIN resources is a prerequisite for high-reliability communication. However, the storage capacity of space-air network segment is extremely limited. The air servers also do not have sufficient storage resources to centrally accommodate the information uploaded by each edge server. So the problem of how to coordinate the storage resources of SAGIN has arisen. This paper proposes a SAGIN storage resource management algorithm based on distributed deep reinforcement learning (DRL). The resource management process is modeled as a Markov decision model. In each edge physical domain, we extract the network attributes represented by storage resources for the agent to build a training environment, so as to realize the distributed training. In addition, we propose a SAGIN resource management framework based on distributed DRL. Simulation results show that the agent has an ideal training effect. Compared with other algorithms, the resource allocation revenue and user request acceptance rate of the proposed algorithm are increased by about 18.15% and 8.35% respectively. Besides, the proposed algorithm has good flexibility in dealing with the changes of resource conditions.