# xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective

## TL;DR

This paper proposes a semantic communication framework for vehicular networks that optimizes resource use and latency by addressing knowledge base construction and service pairing, enhancing ultra-reliable low-latency communication.

## Contribution

It introduces a novel semantic communication-based service provisioning method for vehicular networks, jointly optimizing knowledge matching and service pairing under strict latency and reliability constraints.

## Key findings

- S$^{ ext{4}}$ reduces average queuing latency significantly.
- The proposed method improves semantic data throughput.
- It achieves higher knowledge matching and satisfaction levels.

## Abstract

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S$^{\text{4}}$) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S$^{\text{4}}$ in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.