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Full‐duplex jamming for physical layer security improvement in NOMA‐enabled overlay cognitive radio networks.

  • Published In: Security & Privacy, 2024, v. 7, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hema, P. P.; Babu, A. V. 3 of 3

Abstract

In this paper, we analyze the physical layer security (PLS) performance of nonorthogonal multiple access (NOMA)‐enabled overlay cognitive radio networks (NOMA‐OCRNs) in the presence of an external passive eavesdropper. Here PLS is expressed in terms of the secrecy outage probabilities (SOPs) experienced by the primary user (PU) and secondary user (SU). We obtain approximate expressions for the SOPs of both PU as well as SU assuming a jamming‐free environment, where both primary and secondary destination nodes are half‐duplex devices. To improve the SOP performance, we propose a jamming‐assisted framework, where full‐duplex destination nodes are employed, which are capable of transmitting jamming signals to confound the eavesdropper. Approximate expressions for the SOPs of PU and SU are derived for the jamming‐assisted framework as well. It is demonstrated that the proposed jamming‐assisted framework significantly reduces the SOPs compared to the jamming‐free scenario. We also determine optimal power allocation coefficients (OPACs) for PU and SU at the secondary transmitter that maximizes the total secrecy throughput of the jamming‐assisted NOMA‐OCRN with FD destinations. It is shown that the suggested OPAC significantly enhances the total secrecy throughput, compared to the default selection of the PAC. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Security & Privacy. 2024/05, Vol. 7, Issue 3, p1
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2024
  • ISSN:2475-6725
  • DOI:10.1002/spy2.371
  • Accession Number:176717281
  • Copyright Statement:Copyright of Security & Privacy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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