… deep reinforcement learning, which integrates several layers of neural networks for extracting
and learning … model formulation of deep reinforcement learning as an effective method for …

… learn about, as well as to adaptively and dynamically reconfigure its operating parameters,
including the sensing and transmission channels, for network … use of reinforcement learning (…

… networks such as Dynamic Channel Selection (DCS), topology management, congestion
control, and scheduling. In this paper, Reinforcement Learning … research on RL in CR networks. …

… Reinforcement learning, which rapidly analyzes the amount of data in a model-free manner,
… algorithms based on reinforcement learning techniques in cognitive radio networks. The …

… Reinforcement learning has been applied in a wide range of schemes in CR networks for
SU performance enhancements, whilst minimizing interference to PUs. The schemes are listed …

… with the help of a clustering mechanism and reinforcement learning (RL), an artificial intel…
network scalability by reducing the flooding of routing overheads as well as network stability …

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things
(IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, …

… for the deployment of CR networks, which is a challenging … for CR networks through
reinforcement learning, which does … power control game in CR networks. During the repeated …

Research in cognitive radio networks aims at maximized spectrum utilization by giving access
to increased users with the help of dynamic spectrum allocation policy. The unknown and …

… Reinforcement learning has found great success in variety of applications [4], [25]. In this …
multiagent reinforcement learning to optimize the sensing policy in a cognitive radio network. …