… deep Q-network (DQN), which is a type of deep RL [17]. In our earlier work, a deep RL
approach is introduced for spectrum access in … , we apply deep RL in both spectrum access and …

… Abstract—This paper investigates the use of deep reinforcement learning (DRL) to solve
the dynamic spectrum access problem. Specifically, we examine the scenario where multiple …

… is regarded as a promising technology to support spectral-efficient Internetof-Things (IoT) in
… spectrum access problem for D2D-assisted cellular networks based on deep reinforcement …

… learning algorithm for dynamic spectrum access that can effectively … a deep multiuser
reinforcement learning approach to achieve this goal. Deep reinforcement learning (DRL) (or deep …

… scalable deep reinforcement learning approach to simultaneous routing and spectrum access
… main task, and solve the spectrum access problem along the routing process by training a …

… spectrum access in wireless network. However, directly applying deep Q-network (DQN) to
the spectrum … of the network size and available spectrum, and it consumes much longer time …

… Abstract—Reconfigurable wireless network can flexibly provide efficient spectrum access …
recurrent deep reinforcement learning algorithm for dynamic spectrum access based on …

… of spectrum sensing errors. To be specific, we apply the powerful machine learning tool,
deep reinforcement learning (DRL), for SUs to learn “appropriate” spectrum access strategies in …

… deep reinforcement learning (DRL) theory, we design a double deep Q-network (DDQN)
based D2D spectrum access … to autonomously learn an optimal access strategy to achieve the …

… In this paper, we consider a dynamic spectrum access scenario with a mesh network of
2N primary radio nodes including N transmitters and N receivers. The N primary transmitters …