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

TL;DR

This paper proposes a velocity-adaptive access scheme for semantic-aware vehicular networks that jointly optimizes fairness and Age of Information (AoI) using stochastic analysis and convex optimization, validated by simulations.

Contribution

It introduces a novel joint fairness and AoI optimization framework for V2X communications considering vehicle speed variations and semantic communication.

Findings

The proposed scheme improves fairness among vehicles.

It effectively reduces AoI in high-speed vehicular environments.

Simulation results confirm the scheme's superiority over baseline methods.

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.