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

Le Xia, Yao Sun, Dusit Niyato, Daquan Feng, Lei Feng, Muhammad Ali Imran

TL;DR

This paper identifies and jointly addresses two fundamental problems of knowledge base construction and vehicle service pairing inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements.

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.

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

TL;DR

This paper identifies and jointly addresses two fundamental problems of knowledge base construction and vehicle service pairing inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements.

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) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.
Paper Structure (17 sections, 2 theorems, 24 equations, 11 figures, 1 table)

This paper contains 17 sections, 2 theorems, 24 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. SCVN Scenario
  4. Vehicular Knowledge Storage Model
  5. Vehicle Pairing Model for SemCom
  6. Knowledge Matching Based Queuing Model
  7. Problem Formulation
  8. Proposed SemCom-Empowered Service Supplying Solution (S$^{\text{4}}$)
  9. Primal-Dual Problem Transformation
  10. Two-Stage Method Based on KBC and VSP
  11. Near-Optimal Solution to $\mathbf{P1}_{i,j}$
  12. Optimal Solution to $\mathbf{P2}$
  13. Workflow of S$^{\text{4}}$ and Complexity Analysis
  14. Numerical Results and Discussions
  15. Conclusions
  16. ...and 2 more sections

Key Result

Proposition 1

Let $\bm{\alpha}^{*}=\left[\bm{\alpha}^{*}_{1}, \bm{\alpha}^{*}_{2}, \cdots,\bm{\alpha}^{*}_{V}\right]^{T}$ be the optimal KBC solution to the problem in (Dual) given the dual variable $\bm{\tau}$, where $\bm{\alpha}^{*}_{i}$ represents the optimal KBC policy of VUE $i$. Then we have $\forall i \in

Figures (11)

  • Figure 1: The SCVN scenario with KBC and VSP.
  • Figure 2: The knowledge matching based queuing model for semantic data packets transmitted between VUEs in the SCVN.
  • Figure 3: Illustration of the proposed solution S$^{\text{4}}$.
  • Figure 4: The Proposed Solution S$^{\text{4}}$
  • Figure 5: Average queuing latency of a VUE pair vs. varying numbers of VUEs.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2