Physical Layer Security Enhancement Using Artificial Noise in Cellular Vehicle-to-Everything (C-V2X) Networks
Chao Wang, Zan Li, Gen Xiang Xia, Jia Shi, Jiangbo Si, Yulong Zou

TL;DR
This paper explores the use of artificial noise and secure beamforming techniques to improve physical layer security in cellular V2X networks, using stochastic geometry to model network elements and derive security performance metrics.
Contribution
It introduces a stochastic geometry-based analysis of artificial noise-assisted physical layer security in C-V2X networks, including new bounds and analytical expressions for secrecy performance.
Findings
Larger antenna arrays enhance security robustness.
Optimal power allocation ratio remains stable across different antenna counts.
Analytical bounds on secrecy probability are validated by simulations.
Abstract
The secure transmission of confidential information in cellular vehicle-to-everything (C-V2X) communication networks is vitally important for user's personal safety. However, for C-V2X there have not been much studies on the physical layer security (PLS). Since artificial noise (AN) and secure beamforming are popular PLS techniques for cellular communications, in this paper we investigate the potential of these PLS techniques for enhancing the security of C-V2X networks. In particular, leveraging stochastic geometry, we study the PLS of an AN assisted C-V2X network, where the locations of legitimate vehicular nodes, malicious vehicular nodes and road side units (RSUs) are modeled by Cox processes driven by a common Poisson line process (PLP), and the locations of cellular base stations (BSs) are modeled by a two-dimensional (2D) Poisson point process (PPP). Based on the maximum…
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Taxonomy
TopicsWireless Communication Security Techniques · Vehicular Ad Hoc Networks (VANETs) · Advanced MIMO Systems Optimization
