Online dynamic resource allocation in interference temperature constrained cognitive radio network using reinforcement learning

2012 ◽  
Author(s):  
Soumya Maulik ◽  
Rajarshi Roy ◽  
Arijit De ◽  
Amitabha Bhatttacharya
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Network ◽  
Dynamic Resource Allocation ◽  
Radio Network ◽  
Interference Temperature ◽  
Dynamic Resource

scholarly journals A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5216
Author(s):  
Di Zhao ◽  
Hao Qin ◽  
Bin Song ◽  
Beichen Han ◽  
Xiaojiang Du ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Spectrum Sharing ◽  
Cognitive Radio Network ◽  
Radio Network ◽  
Model Learning ◽  
Secondary Users ◽  
Convolutional Network ◽  
Optimal Resource

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.

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