Velocity-Adaptive Access Scheme for Semantic-Aware Vehicular Networks: Joint Fairness and AoI Optimization

2025-12-01

AI Summary

In 5G NR V2X Mode 2, heterogeneous vehicle speeds cause uneven RSU communication durations, imbalanced data exchange, and heightened safety risks. To address this, we propose a speed-aware joint optimization framework. It introduces a dynamic speed-based fairness metric to adaptively adjust access windows and jointly optimizes access fairness and Age of Information (AoI) by integrating image semantic communication with large language model–assisted decision-making. AoI evolution is modeled as a stochastic hybrid system, and the non-convex joint optimization problem is transformed into a tractable convex form via sequential convex approximation. Simulation results demonstrate that the proposed scheme significantly improves access fairness (+32.7%) and reduces average AoI (−41.5%), outperforming conventional mechanisms while maintaining resource efficiency and communication security.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV
• Multiagent Systems: Agent Communication

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.

Problem

• Optimizes fairness and Age of Information in 5G V2X networks
• Addresses varying communication durations due to different vehicle speeds
• Reduces latency using semantic-aware image communication systems

Innovation

• Velocity-adaptive access scheme for fairness and AoI optimization
• Joint optimization using SHS analysis and sequential convex approximation
• LLM-based algorithm for semantic-aware vehicular network efficiency