Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach  

作　　者：Yang Jie He Jingchao Cheng Nan Yin Zhisheng Han Dairu Zhou Conghao Sun Ruijin 

机构地区：[1]School of Telecommunications Engineering,Xidian University,Xi’an 710071,China [2]School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210000,China [3]Department of Electrical and Computer Engineering,University of Waterloo,Waterloo ON N2L 3G1,Canada

出　　处：《China Communications》2024年第11期56-74,共19页中国通信（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant 2020YFB1807700;the National Natural Science Foundation of China(NSFC)under Grant(No.62201414,62201432);the Qinchuangyuan Project(OCYRCXM-2022-362);the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University under Grant YJSJ24017;the Guangzhou Science and Technology Program under Grant 202201011732。

摘　　要：With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

关 键 词：cache placement coded caching graph convolutional network(GCN) mobile edge caching(MEC) multilayer satellite network 

分 类 号：TN929.5[电子电信—通信与信息系统] TN927.2[电子电信—信息与通信工程]