A Glimpse of Physical Layer Security in Internet of Vehicles: Joint Design of the Transmission Power and Sensing Power

The urgent need for higher sensing accuracy and transmission reliability in the Internet of Vehicles (IoV), along with the limited availability of spectrum resources, integrated sensing and communication (ISAC) tends to operate in higher frequency bands. This shift, however, introduces severe sensing-communication coupling interference. Due to the openness nature of the wireless medium, ISAC enabled IoV faces more significant security concerns. Centered at the communication interference, traditional physical layer security (PLS) schemes aim to maximize the channel quality difference between legitimate and eavesdropping channels to ensure the perfect secrecy. However, transforming directly current PLS methods into secure communication in ISAC-assisted IoV systems faces two significant challenges, because of sensing-communication coupling interference and strong directional beamforming caused by higher frequency bands. To address these challenges, this paper formulates an optimization problem aimed at maximizing the average secrecy rate by jointly designing the transmission power allocation and radar sensing power allocation, while ensuring the desired sensing accuracy and transmission reliability. To solve this non-convex optimization problem, we introduce the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Experimental results demonstrate that, compared to optimizing transmission power or radar sensing power individually, joint optimization significantly improves the average secrecy rate upon convergence, with increases of at least 265% and 441%, respectively.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2025-03-26
DOI
https://doi.org/10.1109/tvt.2025.3545665
Citations
4
Primary Topic
Big Data and Digital Economy
Type
article
Field-Weighted Citation Impact
12.98
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article

A Glimpse of Physical Layer Security in Internet of Vehicles: Joint Design of the Transmission Power and Sensing Power

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4 citations
IEEE Transactions on Vehicular Technology
Big Data and Digital Economy
12.98
article

A Glimpse of Physical Layer Security in Internet of Vehicles: Joint Design of the Transmission Power and Sensing Power

Dingyou Ma, Zhiyong Feng, Kan Yu, Dong Li, Kaixuan Li, Xiaowu Liu, Ruinian Wang
article en
4 citations

Abstract

The urgent need for higher sensing accuracy and transmission reliability in the Internet of Vehicles (IoV), along with the limited availability of spectrum resources, integrated sensing and communication (ISAC) tends to operate in higher frequency bands. This shift, however, introduces severe sensing-communication coupling interference. Due to the openness nature of the wireless medium, ISAC enabled IoV faces more significant security concerns. Centered at the communication interference, traditional physical layer security (PLS) schemes aim to maximize the channel quality difference between legitimate and eavesdropping channels to ensure the perfect secrecy. However, transforming directly current PLS methods into secure communication in ISAC-assisted IoV systems faces two significant challenges, because of sensing-communication coupling interference and strong directional beamforming caused by higher frequency bands. To address these challenges, this paper formulates an optimization problem aimed at maximizing the average secrecy rate by jointly designing the transmission power allocation and radar sensing power allocation, while ensuring the desired sensing accuracy and transmission reliability. To solve this non-convex optimization problem, we introduce the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Experimental results demonstrate that, compared to optimizing transmission power or radar sensing power individually, joint optimization significantly improves the average secrecy rate upon convergence, with increases of at least 265% and 441%, respectively.

IEEE Transactions on Vehicular TechnologyVol. 74(7)
Macau University of Science and Technology (MO), Beijing University of Posts and Telecommunications (CN), Qufu Normal University (CN)
Affordable and clean energy
Openalex Percentile: Top 2%
Big Data and Digital Economy
12.98
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