Machine learning (ML) techniques have gained significant attention in the field of cognitive
radio networks (CRNs) due to their ability to learn and adapt to changing environments. In …

Cognitive radio technology (CRNs) will be the fundamental driving force behind the next
generation (5G) mobile communication systems as it provides the optimal solution for the …

In cognitive radio (CR) networks, the cognition cycle, ie, the ability of wireless transceivers to
learn the optimal configuration meeting environmental and application requirements, is …

The next frontier in wireless connectivity lies at the intersection of cognitive radio (CR)
technology and machine learning (ML), where intelligent networks can provide pervasive …

The utilization of Artificial Intelligence (AI) in leveraging Cognitive Radio Networks (CRNs)
represents an emerging field of study. This surge is primarily driven by operational …

Due to exponential growth in demand for radio spectrum for wireless communication
networking, the radio spectrum has become over-crowded. The fixed spectrum allocation …

For years now, the cognitive radio networks (CRN) has continued to evolve as a leading
technology to help drive modern and near-future wireless communication possibilities. With …

In this paper, we present a survey on the utility of machine learning (ML) algorithms for
applications in cognitive radio networks (CRN). We start with a high-level overview of some …

Cognitive radio (CR) is the solution for the current spectral underutilized problems, Context
awareness and environment awareness are the key functions of CR nowadays. A software …

Cognitive radios are expected to play a major role towards meeting the exploding traffic
demand over wireless systems. A cognitive radio node senses the environment, analyzes …