This paper aims to design an appropriate power control policy of the secondary user (SU) to
share the spectrum with the primary user without harmful interference. With dynamic …

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 …

Cognitive radio attracts researchers' attention recently in radio resource management due to
its ability to exploit environment awareness in configuring radio system parameters …

Network selection is one of the important techniques in cognitive radio networks (CRNs).
With the development of network convergence technology and the popularity of …

Generally speaking, artificial intelligent (AI) models are trained under special learning
hypotheses, especially the one that statistics of the training data are static during the training …

This paper details the practical design of a Cognitive Radio network which uses multi-agent
Deep Reinforcement Learning for dynamic spectrum access. Each network node evaluates …

The development of wireless communication has made spectrum resources increasingly
scarce. Existing spectrum resources, however, are not currently used in an efficient way …

Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …

Cognitive Radio Network (CNR) is a dynamic network where the users can adjust spectrum
usage dynamically in accordance to the operational environment to minimize interference …

Deep reinforcement learning is a successful learning model in many application domains. A
Cognitive radio network (CRN) is a promising solution for spectrum scarcity and sharing …