Semantic communication (SemCom) 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 vehicular networks, which normally consume a tremendous amount of resources to achieve stringent requirements on high reliability and low latency. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to realize efficient vehicle-to-vehicle service provisioning for multiple users at the same time. 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. 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. Simulation results demonstrate the superiority of S$^{\text{4}}$ in terms of average queuing latency, semantic data packet throughput, and user knowledge preference satisfaction compared with two different benchmarks.

翻译：语义通信（SemCom）近来被视为应对通信资源短缺危机的有前景方案。这一趋势促使我们探索将语义通信应用于车联网的可能性——该网络通常需消耗大量资源才能满足高可靠性与低延迟的严苛要求。然而，语义通信中独特的背景知识匹配机制使得同时为多位用户提供高效的车对车服务变得具有挑战性。为此，本文识别并联合解决了语义通信赋能车联网中固有的两个基本问题：知识库构建（KBC）与车辆服务配对（VSP）。具体而言，我们首先推导了语义数据包特有的基于知识匹配的排队延迟，随后构建了一个受KBC与VSP相关可靠性约束的延迟最小化问题。在此基础上，我们提出了一种语义通信赋能的服务供给方案（S$^{\text{4}}$），并对其最优性保证进行了理论分析。仿真结果表明，与两种不同基准方案相比，S$^{\text{4}}$在平均排队延迟、语义数据包吞吐量及用户知识偏好满意度方面均具有优越性。