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Knowledge Base Aware Semantic Communication in Vehicular Networks

Le Xia, Yao Sun, Dusit Niyato, Kairong Ma, Jiawen Kang, Muhammad Ali Imran

TL;DR

A SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee and results demonstrate the superiority of S4 in terms of average queuing latency, semantic data packet throughput, and user knowledge preference satisfaction compared with two different benchmarks.

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.

Knowledge Base Aware Semantic Communication in Vehicular Networks

TL;DR

A SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee and results demonstrate the superiority of S4 in terms of average queuing latency, semantic data packet throughput, and user knowledge preference satisfaction compared with two different benchmarks.

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) is proposed along with the theoretical analysis of its optimality guarantee. Simulation results demonstrate the superiority of S in terms of average queuing latency, semantic data packet throughput, and user knowledge preference satisfaction compared with two different benchmarks.
Paper Structure (10 sections, 18 equations, 5 figures)

This paper contains 10 sections, 18 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Semantic Service Provisioning Model
  4. Knowledge Base Aware Queuing Model
  5. Problem Formulation
  6. Proposed SemCom-Empowered Service Supplying Solution
  7. Primal-Dual Problem Transformation
  8. Two-Stage Method Based on KBC and VSP
  9. Numerical Results and Discussions
  10. Conclusions

Figures (5)

  • Figure 1: The SCVN scenario and the knowledge base aware queuing model for semantic data packets transmitted between VUEs.
  • Figure 2: Average queuing latency of a VUE pair vs. varying numbers of VUEs.
  • Figure 3: Average TSP of a VUE pair vs. varying numbers of VUEs.
  • Figure 4: Average queuing latency of a VUE pair vs. varying numbers of KBs.
  • Figure 5: Average knowledge preference satisfaction vs. varying numbers of KBs.