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Wireless Networks

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18.09.2021 | Original Paper

Machine learning-based physical layer security: techniques, open challenges, and applications

verfasst von: Anil Kumar Kamboj, Poonam Jindal, Pankaj Verma

Erschienen in: Wireless Networks | Ausgabe 8/2021

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Abstract

Wireless physical layer security (WPLS) is a powerful technology for current and emerging mobile networks. Physical layer authentication (PLA), antenna selection (AS), and relay node selection are the main elements that add diversity and strength to the paradigm of WPLS. However, heterogeneity, ultra-density, and high mobility requirements make the work difficult for the security of wireless networks. Recently, machine learning has emerged as a promising tool to alleviate the increasing complexity of wireless networks. Hence, this paper introduces intelligent WPLS by concentration on PLA, AS, and relay node selection. First, it presents the background and types of WPLS and ML. Then, revisit the three basic methods of WPLS enhancement, i.e., relay node selection, AS, and authentication, and their integration with ML. Furthermore, several key challenges faced by intelligent WPLS were discussed along with the comprehensive investigation of its different applications in the wireless networks such as the internet of things, device-to-device communication, cognitive radio, non-orthogonal multiple access, and unmanned aerial vehicles. Finally, the appendix includes a detailed survey of ML techniques for WPLS. This article proposes to motivate and help interested readers to easily and rapidly understand the state-of-the-art of WPLS and intelligent WPLS.

Vorheriger Artikel Latency-aware content caching and cost-aware migration in SDN based on MEC

Nächster Artikel Joint optimal power splitting and relay selection strategy under SWIPT

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Titel: Machine learning-based physical layer security: techniques, open challenges, and applications
verfasst von: Anil Kumar Kamboj
Poonam Jindal
Pankaj Verma
Publikationsdatum: 18.09.2021
Verlag: Springer US
Erschienen in: Wireless Networks / Ausgabe 8/2021
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI: https://doi.org/10.1007/s11276-021-02781-1

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