Knowledge Base Aware Semantic Communication in Vehicular Networks

TL;DR

This paper proposes a novel semantic communication framework for vehicular networks that optimizes knowledge base construction and service pairing to reduce latency and improve data throughput.

Contribution

It introduces a joint solution addressing knowledge base and service pairing challenges in SemCom-enabled vehicular networks, with theoretical analysis and simulation validation.

Findings

S$^{ ext{4}}$ reduces average queuing latency

S$^{ ext{4}}$ improves semantic data throughput

S$^{ ext{4}}$ enhances user knowledge satisfaction

Abstract

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.