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AuthorZhao, Di
AuthorQin, Hao
AuthorSong, Bin
AuthorHan, Beichen
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2020-11-05T10:34:17Z
Publication Date2020-09-13
Publication NameSensors (Switzerland)en_US
Identifierhttp://dx.doi.org/10.3390/s20185216
CitationZhao, D.; Qin, H.; Song, B.; Han, B.; Du, X.; Guizani, M. A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network. Sensors 2020, 20, 5216.
ISSN14248220
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090793990&origin=inward
URIhttp://hdl.handle.net/10576/16937
AbstractCognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.
Languageen
PublisherMDPI
SubjectCognitive radio
SubjectDeep reinforcement learning
SubjectDynamic graph
SubjectEnd-to-end learning model
SubjectGraph convolutional network
SubjectInterference mitigation
SubjectResource allocation
TitleA graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network
TypeArticle
Issue Number18
Volume Number20
elsevier.identifier.scopusid SCOPUS_ID:85090793990


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