Secrecy Analysis of Cognitive Radio Networks over Generalized Fading Channels

At present, the fifth generation (5G) communication networks are in the time of large-scale deployment principally because its characteristics consists of large bandwidth, fast response, and high stability. As a partner of 5G, the Internet of Things (IoT) involves billions of devices around the worl...

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Veröffentlicht in:Security and communication networks 2020-09, Vol.2020 (2020), p.1-9
Hauptverfasser: He, Pengfei, Bie, Hongxia, Bie, Zhisong, Sun, Jiangfeng, Jin, Machao
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container_end_page 9
container_issue 2020
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container_title Security and communication networks
container_volume 2020
creator He, Pengfei
Bie, Hongxia
Bie, Zhisong
Sun, Jiangfeng
Jin, Machao
description At present, the fifth generation (5G) communication networks are in the time of large-scale deployment principally because its characteristics consists of large bandwidth, fast response, and high stability. As a partner of 5G, the Internet of Things (IoT) involves billions of devices around the world, which can make the wireless communication environment more intelligent and convenient. However, the problem that cannot be ignored is the physical layer security of 5G-IoT networks. Based on this, we perform a security analysis of cognitive radio networks (CRN) for IoT, where the CRN is the single-input multiple-output (SIMO) model experiencing κ-μ shadowed fading with multiple eavesdroppers. To analyze the confidentiality of the system under consideration, we analyze the security performance for the considered IoT systems with the help of the derived secure outage probability (SOP) and probability of strictly positive secrecy capacity (SPSC). As a verification of the theoretical formula, Monte Carlo simulation is also provided. The results of great interest are the factors that can produce better security performance in high SNRs region which consist of smaller M, smaller k, and larger N, and larger μ, smaller IP, and smaller Rth.
doi_str_mv 10.1155/2020/8842012
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title Secrecy Analysis of Cognitive Radio Networks over Generalized Fading Channels
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