… learning, which integrates several layers of neural networks … model formulation of deep
reinforcement learning as an … future advances in cognitive radio networks. The discussion …

… 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) …

… • We propose a new method of distributed multi-user dynamic spectrum access in a cognitive
radio network through combining deep reinforcement learning with evolutionary game …

… attack in cognitive radio network and develop an antijamming scheme based on deep
reinforcement learning techniques. As shown in Fig. 1, the cognitive radio network is composed of …

… to use the deep reinforcement … Deep-Q Network (DDQN) that enables the gateway to learn
the optimal policy. The simulation results clearly show that the proposed deep reinforcement …

… spectrum efficiency of the cognitive network and the QoE of … a cognitive radio network model
combined with network slicing. Secondly, we introduce the basic concepts of reinforcement …

… the use of deep reinforcement learning, instead of the conventional reinforcement learning,
is … In Section III, we develop a deep reinforcement learning algorithm for power control for the …

… The present work explores the Deep Reinforcement Learning algorithm (DRL) for adapting
the behavioural changes of SUs during CSS to overcome the issue mentioned earlier. The …

… Abstract— This paper presents a deep reinforcement learningbased technique for
cognitive radio underlay dynamic spectrum access (DSA) that performs distributed joint multi-resource …

… use deep reinforcement learning (DRL) for the task of cooperative spectrum sensing (CSS) in
a cognitive radio network… We consider a cognitive radio network that consists of N SUs. The …