Will 5G lead to more spectrum sharing? Discussing recent developments of the LSA and the CBRS spectrum sharing frameworks

Massaro, Maria; Beltrán, Fernando

doi:10.1016/j.telpol.2020.101973

Cited by 23 publications

(13 citation statements)

References 31 publications

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“…The main objective of LSA is to allow new users to work in already occupied frequency bands while maintaining existing incumbent services on a long-term basis. The LSA framework is currently switched to explore the 3.4-3.8 GHz band from the 2.3-2.4 GHz band, enabling coexistence between incumbents and 5G applications [5] [6].…”

Section: A Ss: a Brief Historymentioning

confidence: 99%

“…The third is General Authorized Access (GAA) users, who have sensing-assisted unlicensed access in the absence of the incumbent and PAL users. SAS determines the maximum allowable transmission power level and the available frequencies at a given location to be assigned to PAL and GAA users [5] [6].…”

Section: A Ss: a Brief Historymentioning

confidence: 99%

“…There have been several literature surveys that focus on SS techniques and security issues [4], [6]- [9], [11]- [19]. However, most of those works only covered one framework of the SS networks.…”

Section: Motivation and Contributionmentioning

confidence: 99%

“…In [4], the authors studied the main concepts of dynamic SS and major challenges associated with sharing of licensed bands. [6] discussed two data-based SS frameworks: LSA and CBRS and the existing work related to their management approach. [7] presented existing work in spectrum prediction and learning, summarized the related applications, techniques, main metrics, as well as computational complexity, and provided practical examples.…”

Section: Motivation and Contributionmentioning

confidence: 99%

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When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

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Section: A Ss: a Brief Historymentioning

confidence: 99%

Section: A Ss: a Brief Historymentioning

confidence: 99%

Section: Motivation and Contributionmentioning

confidence: 99%

Section: Motivation and Contributionmentioning

confidence: 99%

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When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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“…It is clear that the frequency spectrum is a scarce and limited resource that constitutes an important factor in mobile communication systems, as well as the related cost for the Mobile Network Operator (MNO). In this context, new spectrum explorations [ 11 , 12 ], higher energy efficiency [ 13 ], and dynamic spectrum usage [ 14 , 15 , 16 , 17 , 18 ] have become the new features of communication networks.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spectrum Sharing for Future LTE-NR Networks

Barb

Alexa

Oteşteanu

2021

Sensors

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5G is the next mobile generation, already being deployed in some countries. It is expected to revolutionize our society, having extremely high target requirements. The use of spectrum is, therefore, tremendously important, as it is a limited and expensive resource. A solution for the spectrum efficiency consists of the use of dynamic spectrum sharing, where an operator can share the spectrum between two different technologies. In this paper, we studied the concept of dynamic spectrum sharing between LTE and 5G New Radio. We presented a solution that allows operators to offer both LTE and New Radio services using the same frequency bands, although in an interleaved mode. We evaluated the performance, in terms of throughput, of a communication system using the dynamic spectrum sharing feature. The results obtained led to the conclusion that using the dynamic spectrum sharing comes with a compromise of a maximum 25% loss on throughput. Nevertheless, the decrease is not that substantial, as the mobile network operator does not need to buy an additional 15 MHz of bandwidth, using the already existing bandwidth of LTE to offer 5G services, leading to cost reduction and an increase in spectrum efficiency.

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Bibliography

2023

5G and Beyond Wireless Communication Networks

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Will 5G lead to more spectrum sharing? Discussing recent developments of the LSA and the CBRS spectrum sharing frameworks

Cited by 23 publications

References 31 publications

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Dynamic Spectrum Sharing for Future LTE-NR Networks

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