Machine learning for cooperative spectrum sensing and sharing: A survey
With the rapid development of next‐generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in C...
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| Published in | Transactions on emerging telecommunications technologies Vol. 33; no. 1 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.01.2022
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| Online Access | Get full text |
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| Abstract | With the rapid development of next‐generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML‐based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next‐generation communication technologies.
With the rapid development of next‐generation wireless communication technologies and increasing the requirement of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in the sensing and sharing of spectrum in cognitive radio networks. The application of ML techniques has added new aspects to these fundamental problems. |
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| AbstractList | With the rapid development of next‐generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML‐based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next‐generation communication technologies.
With the rapid development of next‐generation wireless communication technologies and increasing the requirement of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in the sensing and sharing of spectrum in cognitive radio networks. The application of ML techniques has added new aspects to these fundamental problems. With the rapid development of next‐generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML‐based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next‐generation communication technologies. |
| Author | Kumar, Sandeep Singh, Kuldeep Janu, Dimpal |
| Author_xml | – sequence: 1 givenname: Dimpal surname: Janu fullname: Janu, Dimpal organization: Malaviya National Institute of Technology – sequence: 2 givenname: Kuldeep orcidid: 0000-0002-2350-1700 surname: Singh fullname: Singh, Kuldeep organization: Malaviya National Institute of Technology – sequence: 3 givenname: Sandeep orcidid: 0000-0002-5750-6112 surname: Kumar fullname: Kumar, Sandeep email: sann.kaushik@gmail.com organization: Bharat Electronics Ltd |
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