5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

Ahmad, Wan Juliana Wan; Radzi, Nurul Asyikin Mohamed; Samidi, Faris Syahmi; Ismail, Amiruddin; Abdullah, Fairuz; Jamaludin, M. Z.; Zakaria, M. N.

doi:10.1109/access.2020.2966271

Cited by 204 publications

(77 citation statements)

References 125 publications

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“…In the 5G network environment, a large amount of sensors, actuators, electronic appliances, drones, and smart devices are wirelessly connected to the Internet and to one another via mMTC [141]. These devices generate sporadic traffic among many geographically spread equipment, thereby introducing connection density and network energy efficiency issues.…”

Section: F Advancement Of 5g and Future 6gmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Section: F Advancement Of 5g and Future 6gmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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“…Small cells have low transmission power; thus, the coverage areas under such small cells can apply CR for efficient resource optimization and high data rate services [111]. To utilize the CR paradigm in the most efficient way for higher throughput, spectrum sensing is a fundamental element in it.…”

Section: Cognitive Radiomentioning

confidence: 99%

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

et al. 2020

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With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

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“…To that goal, Ye et al [18] present the overview of LTE spectrum sharing techniques, with the focus on three spectrum segments: i) TV white space channels, ii) frequently unused service-dedicated 3.5GHz, and iii) 5GHz unlicensed band [18]. Finally, Ahmad et al present a thorough review of recent advances in spectrum sharing in 5G networks [23]. However, all of the above-mentioned surveys solely tackle the wireless domain.…”

Section: A Comparison With Existing Surveysmentioning

confidence: 99%

Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy

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et al. 2020

IEEE Commun. Surv. Tutorials

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Regardless of the context to which it is applied, sharing resources is well-recognized for its considerable benefits. Since 5G networks will be service-oriented, on-demand, and highly heterogeneous, it is utmost important to approach the design and optimization of the network from an end-to-end perspective. In addition, in order to ensure end-to-end performance, this approach has to entail both wireless and optical domains, altogether with the IoT, edge, and cloud paradigms which are an indispensable part of the 5G network architecture. Shifting from the exclusive ownership of network resources toward sharing enables all participants to cope with stringent service requirements in 5G networks, gaining significant performance improvements and cost savings at the same time. The main objective of this paper is to survey the literature on resource sharing, providing an in-depth and comprehensive perspective of sharing by recognizing the main trends, the techniques which enable sharing, and the challenges that need to be addressed. By providing a taxonomy which brings the relevant features of a comprehensive sharing model into focus, we aim to enable the creation of sharing models for more efficient future communication networks. We also summarize and discuss the relevant issues arising from network sharing, that should be properly tackled in the future.

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5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

Cited by 204 publications

References 125 publications

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy

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