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Robust Security Analysis Based on Random Geometry Theory for Satellite-Terrestrial-Vehicle Network

Information Theory 2022-07-15 v2 Computation and Language math.IT

Abstract

Driven by B5G and 6G technologies, multi-network fusion is an indispensable tendency for future communications. In this paper, we focus on and analyze the \emph{security performance} (SP) of the \emph{satellite-terrestrial downlink transmission} (STDT). Here, the STDT is composed of a satellite network and a vehicular network with a legitimate mobile receiver and an mobile eavesdropper distributing. To theoretically analyze the SP of this system from the perspective of mobile terminals better, the random geometry theory is adopted, which assumes that both terrestrial vehicles are distributed stochastically in one beam of the satellite. Furthermore, based on this theory, the closed-form analytical expressions for two crucial and specific indicators in the STDT are derived, respectively, the secrecy outage probability and the ergodic secrecy capacity. Additionally, several related variables restricting the SP of the STDT are discussed, and specific schemes are presented to enhance the SP. Then, the asymptotic property is investigated in the high signal-to-noise ratio scenario, and accurate and asymptotic closed-form expressions are given. Finally, simulation results show that, under the precondition of guaranteeing the reliability of the STDT, the asymptotic solutions outperform the corresponding accurate results significantly in the effectiveness.

Keywords

Cite

@article{arxiv.2112.14192,
  title  = {Robust Security Analysis Based on Random Geometry Theory for Satellite-Terrestrial-Vehicle Network},
  author = {Xudong Li and Ye Fan and Rugui Yao and Peng Wang and Nan Qi and Xiaoya Zuo},
  journal= {arXiv preprint arXiv:2112.14192},
  year   = {2022}
}

Comments

The theoretical analysis in the original manuscript is insufficient, and the system model is not convincing. With the consideration of these flaws, we decide to withdraw our work for further improvement

R2 v1 2026-06-24T08:33:46.728Z