Deep Reinforcement Learning Based Online Network Selection in CRNs with Multiple Primary Networks

Network selection is one of the important techniques in cognitive radio networks (CRNs). With the development of network convergence technology and the popularity of heterogeneous networks, multiple primary CRNs interacting with multiple authorized networks are becoming possible, which can provide secondary users with more spectrum resources by network selection. Network selection is the key to spectrum sharing between CRNs and multiple primary networks. However, the spectrum sensing results, highly complex system state, and unsystematic research framework make the research of network selection very challenging. Traditional network selection algorithms are offline selection methods that are based on prior knowledge of primary networks. However, in the complex network environment, it is impossible to get prior knowledge from multiple primary networks, because the offline network selection methods lack efficiency. In order to meet these challenges, this article aims at improving the quality of service of cognitive users, and based on reinforcement learning method and the achievements of dynamic spectrum access of cognitive radio in single primary network environment, proposed a deep reinforcement learning based online network selection method of CRNs with multiple primary networks.