… , a core component in cognitive radio. The traditional approaches … the aforementioned
problem is deep reinforcement learning, … model formulation of deep reinforcement learning as an …

… of cognitive radio as a Markov decision process and propose an intelligent anti-jamming
scheme based on deep reinforcement … Specifically, we design Double Deep Q Network (Double …

… 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 …

In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio
system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of …

… Abstract—In this paper, we propose to use deep reinforcement learning (DRL) for the task
of cooperative spectrum sensing (CSS) in a cognitive radio network. We selected a recently …

… 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 …

… • 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 …

Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth
of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can …

… to establish efficient QoS (EQS) routes in cognitive radio mobile ad hoc networks. An EQS
… To tackle this problem, we design a new deep reinforcement learning model which supports …

… We consider a cognitive radio based blockchain network as shown in Fig. 1. The network
consists of one SU, ie, the IoT device, a base station, and the blockchain mining pool to support …