TY - JOUR
T1 - A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network
AU - Zhao, Di
AU - Qin, Hao
AU - Song, Bin
AU - Han, Beichen
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/9/2
Y1 - 2020/9/2
N2 - Cognitive 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.
AB - Cognitive 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.
KW - Cognitive radio
KW - Deep reinforcement learning
KW - Dynamic graph
KW - End-to-end learning model
KW - Graph convolutional network
KW - Interference mitigation
KW - Resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85090793990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090793990&partnerID=8YFLogxK
U2 - 10.3390/s20185216
DO - 10.3390/s20185216
M3 - Article
C2 - 32933114
AN - SCOPUS:85090793990
SN - 1424-8220
VL - 20
SP - 1
EP - 23
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 18
M1 - 5216
ER -