Journal
IEEE NETWORK
Volume 35, Issue 3, Pages 168-175Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.011.2000497
Keywords
Energy states; Machine learning; Training; Support vector machines; Kernel; Cognition; Training data
Ask authors/readers for more resources
Cognitive Radio (CR) networks utilize machine learning techniques to enhance performance, with a focus on feature classification and clustering algorithms applied in cooperative CR networks. The article outlines the steps to establishing a learning-based cooperative secondary network, highlighting factors impacting detection performance and discussing key challenges and future directions for intelligent cognitive networks.
Cognitive Radio (CR) technology was first introduced to solve the problem of radio spectrum under-utilization. A cognitive radio network consists of smart radio devices that have the ability to sense radio environment variables and take actions accordingly. To realize their full potential and to become fully cognitive, the CR nodes need to be equipped with learning and reasoning capabilities. Machine learning has been one of the enabling vehicles for intelligent CR networks. Inspired by the cognition cycle of a CR node, over the past years there has been an ever growing interest in using machine learning techniques to enhance the performance of CR networks. In this article, an overview of the various learning techniques currently used in the literature of CR networks is given. We focus on feature classification and clustering algorithms, and their application in cooperative CR networks. We outline the steps to establishing a learning-based cooperative secondary network, highlighting factors that impact detection performance. Additionally, current state-of-the-art learning-based applications in Cognitive Internet of Things (CIoT) are presented. Finally, the key challenges and future directions of intelligent cognitive networks are discussed.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available