Physical layer security analysis using radio frequency-fingerprinting in cellular-V2X for 6G communication

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-05-12 DOI:10.1049/sil2.12225
Hina Ayaz, Ghulam Abbas, Muhammad Waqas, Ziaul Haq Abbas, Muhammad Bilal, Ali Nauman, Muhammad Ali Jamshed
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引用次数: 1

Abstract

It is anticipated that sixth-generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context-aware security protocol. Physical layer security solutions can compete for low-complexity, low-delay, low-footprint, adaptable, extensible, and context-aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF-FP) for location estimation is proposed, wherein the RF-FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road-side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF-FP scheme increases SC by up to 25% for the same signal-to-noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF-FP-based location estimation provides much better results as compared to the existing physical layer security schemes.

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用于6G通信的蜂窝V2X中使用射频指纹的物理层安全分析
预计第六代(6G)系统将带来新的安全挑战,同时为车辆通信提供改进的功能和新的安全方向,这可能导致出现一种新的自适应和上下文感知安全协议。通过利用物理层和引入安全控制,物理层安全解决方案可以竞争低复杂性、低延迟、低占用空间、可适应、可扩展和上下文感知的安全方案。提出了一种新的物理层安全方案,该方案采用射频指纹(RF-FP)的概念进行位置估计,其中射频指纹值在小区内的不同点收集。然后,根据估计的位置,定位最近的可能发送信息信号的路边单元。在此基础上,分析了单位时间内多个窃听者存在对保密容量(SC)和保密中断概率(SOP)的影响。仿真结果表明,在信噪比(SNR)相同的情况下,所提出的RF-FP方案可将SC提高25%,而在信噪比相同的情况下,SOP往往会比基准方案降低30%。因此,与现有的物理层安全方案相比,所提出的基于rf - fp的位置估计提供了更好的结果。
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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