Deep Reinforcement Learning-Based Automated Network Selection in Heterogenous CRNs
Jiang Xie,
Jing Zhang,
Xiangcheng He
et al.
Abstract:The development of technology that enables network convergence and the rising acceptance of heterogeneous network architectures have made it possible for many of the most important cognitive radio networks to communicate with a broad variety of authorized networks. Traditional algorithms for network selection make use of selection approaches that are dependent on prior information about the network under consideration. We use the proposed algorithm to choose different networks and integrate them into computers… Show more
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