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Intelligent and Converged Networks 2020, 1(1): 99-114 https://doi.org/10.23919/ICN.2020.0003
Open Access | Issue | Published: 30 June 2020

Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective

Show Author's Information Kagiso Rapetswa,  Ling Cheng( )
∙ School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2050, South Africa.
Keywords:
cognitive radio, distributed networks, spectrum sensing and sharing, next generation networks
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Rapetswa K, Cheng L. Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective. Intelligent and Converged Networks, 2020, 1(1): 99-114. https://doi.org/10.23919/ICN.2020.0003
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Abstract Full text About this article

With next generation networks driving the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are positioned as a preferred paradigm to address spectrum capacity challenges. CRs address these issues through dynamic spectrum access. However, the main challenges faced by the CR pertain to achieving spectrum efficiency. As a result, spectrum efficiency improvement models based on spectrum sensing and sharing models have attracted a lot of research attention in recent years, including CR learning models, network densification architectures, and massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper provides a survey of recent CR spectrum efficiency improvement models and techniques, developed to support ultra-reliable low latency communications that are resilient to surges in traffic and competition for spectrum. These models and techniques, broadly speaking, enable a wide range of functionality ranging from enhanced mobile broadband to large scale Internet of Things (IoT) type communications. In addition and given the strong correlation between the typical size of a spectrum block and the achievable data rate, the models studied in this paper are applicable in ultra-high frequency band. This study therefore provides a good review of CRs and direction for future investigations into newly identified 5G research areas, applicable in industry and in academia.


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About this article

Convergence of mobile broadband and broadcast services: A cognitive radio sensing and sharing perspective

Show Author's information Kagiso Rapetswa, Ling Cheng( )
∙ School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2050, South Africa.

Abstract

With next generation networks driving the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are positioned as a preferred paradigm to address spectrum capacity challenges. CRs address these issues through dynamic spectrum access. However, the main challenges faced by the CR pertain to achieving spectrum efficiency. As a result, spectrum efficiency improvement models based on spectrum sensing and sharing models have attracted a lot of research attention in recent years, including CR learning models, network densification architectures, and massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper provides a survey of recent CR spectrum efficiency improvement models and techniques, developed to support ultra-reliable low latency communications that are resilient to surges in traffic and competition for spectrum. These models and techniques, broadly speaking, enable a wide range of functionality ranging from enhanced mobile broadband to large scale Internet of Things (IoT) type communications. In addition and given the strong correlation between the typical size of a spectrum block and the achievable data rate, the models studied in this paper are applicable in ultra-high frequency band. This study therefore provides a good review of CRs and direction for future investigations into newly identified 5G research areas, applicable in industry and in academia.

Keywords: cognitive radio, distributed networks, spectrum sensing and sharing, next generation networks

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Received: 02 March 2020
Accepted: 05 March 2020
Published: 30 June 2020
Issue date: June 2020

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