On the Sum Secrecy Rate Maximisation for Wireless Vehicular Networks

TL;DR

This work investigates physical-layer security in wireless vehicular networks by maximizing the sum secrecy rate across VUE pairs under bandwidth and power constraints. It introduces two solution approaches: a successive convex approximation (SCA) method with SOC-based convexifications and a fast first-order FISTA method with gradient and projection steps, including a linesearch variant. The study demonstrates that SCA achieves higher convergence accuracy while FISTA delivers substantial runtime reductions (up to ~300×) with near-equivalent secrecy performance; it also analyzes the impact of Eve antenna count and vehicle speed on secrecy. The results suggest practical implications for real-time secure resource allocation in VANETs, and point to future extensions to multiuser MIMO and imperfect CSI scenarios.

Abstract

Wireless communications form the backbone of future vehicular networks, playing a critical role in applications ranging from traffic control to vehicular road safety. However, the dynamic structure of these networks creates security vulnerabilities, making security considerations an integral part of network design. We address these security concerns from a physical layer security aspect by investigating achievable secrecy rates in wireless vehicular networks. Specifically, we aim to maximize the sum secrecy rate from all vehicular pairs subject to bandwidth and power resource constraints. For the considered problem, we first propose a solution based on the successive convex approximation (SCA) method, which has not been applied in this context before. To further reduce the complexity of the SCA-based method, we also propose a low-complexity solution based on a fast iterative shrinkage-thresholding algorithm (FISTA). Our simulation results for SCA and FISTA show a trade-off between convergence and runtime. While the SCA method achieves better convergence, the FISTA-based approach is at least 300 times faster than the SCA method.

Figures (4)

• Figure 1: System Model
• Figure 2: $M=K=4$, $N_{t}=4$, and $N_{e}=2$.
• Figure 3: $M=K=8$, $N_{t}=8$, and $N_{e}=4$.
• Figure 4: $M=4$ and $N_{t}=4$.