A Survey on Machine Learning Algorithms for Applications in Cognitive Radio Networks

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 of the major challenges in CRNs, and mention the ML architectures and algorithms that can be used to alleviate them. I...

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Bibliographic Details
Main Authors: Upadhye, Akshay, Saravanan, Purushothaman, Chandra, Shreeram Suresh, Gurugopinath, Sanjeev
Format: Conference Proceeding
Language:English
Subjects:
Cognitive radios
Conferences
Dynamic spectrum access
dynamic spectrum allocation
Heuristic algorithms
Machine learning
Machine learning algorithms
Prediction algorithms
Real-time systems
spectrum auction
spectrum sensing
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Summary: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 of the major challenges in CRNs, and mention the ML architectures and algorithms that can be used to alleviate them. In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing - with non-cooperative and cooperative scenarios, and dynamic spectrum access - with spectrum auction and prediction. We present a detailed study of recent advancements in the field of ML in CRNs for these applications, and briefly discuss the set of challenges in real-time implementation of ML techniques for CRNs.
ISSN:2766-2101
DOI:10.1109/CONECCT52877.2021.9622610