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Spectrum inference in cognitive radio networks: Algorithms and applications

Spectrum inference in cognitive radio networks: Algorithms and applications
Spectrum inference in cognitive radio networks: Algorithms and applications
Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the fifth-generation mobile communications and next generation high frequency communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.
1553-877X
150-182
Ding, Guoru
34417431-183d-45e1-98f8-7a452dee8685
Jiao, Yutao
c7bace24-b54a-481e-99e9-07f878ccb0ff
Wang, Jinlong
4eda64b6-5ecb-4904-9f4a-c18dab7a2d39
Zou, Yulong
0359c94b-b989-448a-8164-da4047c4823f
Wu,, Qihui
f5943503-6dd9-4c3b-8ed6-7618ee587d7d
Yao, Yu-Dong
129cd864-4b62-4b02-bb39-208694cc1027
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Ding, Guoru
34417431-183d-45e1-98f8-7a452dee8685
Jiao, Yutao
c7bace24-b54a-481e-99e9-07f878ccb0ff
Wang, Jinlong
4eda64b6-5ecb-4904-9f4a-c18dab7a2d39
Zou, Yulong
0359c94b-b989-448a-8164-da4047c4823f
Wu,, Qihui
f5943503-6dd9-4c3b-8ed6-7618ee587d7d
Yao, Yu-Dong
129cd864-4b62-4b02-bb39-208694cc1027
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Ding, Guoru, Jiao, Yutao, Wang, Jinlong, Zou, Yulong, Wu,, Qihui, Yao, Yu-Dong and Hanzo, Lajos (2018) Spectrum inference in cognitive radio networks: Algorithms and applications. IEEE Communications Surveys & Tutorials, 20 (1), 150-182. (doi:10.1109/COMST.2017.2751058).

Record type: Article

Abstract

Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the fifth-generation mobile communications and next generation high frequency communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

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COMST-00269-2016.R2-final - Accepted Manuscript
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Accepted/In Press date: 28 August 2017
e-pub ahead of print date: 11 September 2017
Published date: 2018

Identifiers

Local EPrints ID: 413928
URI: http://eprints.soton.ac.uk/id/eprint/413928
ISSN: 1553-877X
PURE UUID: fc928651-c101-4931-88fb-324e68bac4ef
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 11 Sep 2017 16:31
Last modified: 07 Oct 2020 05:52

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