Regulation and Standardization Activities Related to Spectrum Sharing

Mueck, Markus; Guirao, M. D. Perez; Yallapragada, Rao; Srikanteswara, Srikathyayani

doi:10.1002/9781119551539.ch2

Cited by 4 publications

(2 citation statements)

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“…As shown in Fig. 6, LSA has two types of users: incumbents and SUs, where incumbents send their spectrum usage information to a database center called LSA Repository [42]. The system then decides whether the SUs can access the spectrum resource with this information or not and no sensing ability is required for those users.…”

Section: B Database Assisted Ssmentioning

confidence: 99%

“…7. The difference is that the IUs in the SAS system may have very sensitive information and do not want to offer it to the database [42]. Instead, to protect the IUs, the Exclusion/Protection Zone (EZ) is applied where the SUs (PAL and GAA users) are banned from accessing the spectrum in these areas to prevent them from harmful interference to IUs.…”

Section: B Database Assisted Ssmentioning

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: B Database Assisted Ssmentioning

confidence: 99%

Section: B Database Assisted Ssmentioning

confidence: 99%