Velocity-Adaptive Access Scheme for Semantic-Aware Vehicular Networks: Joint Fairness and AoI Optimization
Xiao Xu, Qiong Wu, Pingyi Fan, Kezhi Wang, Nan Cheng, Wen Chen, Khaled B. Letaief
TL;DR
This work tackles fair access and timely information exchange in semantic image communications for 5G NR V2X Mode 2 by introducing velocity-adaptive selection windows and a semantic fairness index. It models AoI with a Stochastic Hybrid System and solves a multi-objective optimization that balances fairness and AoI using Sequential Convex Approximation and an LLM-guided MOEA/D. The proposed framework demonstrates improved fairness and lower AoI across speeds and under high vehicular density, validating the approach via numerical simulations. The findings highlight the practical potential of velocity-aware scheduling for reliable, low-latency V2X communications with semantic data.
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.