xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective
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Abstract (arXiv)
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 (S4) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S4 in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.
4 papers.
- 2024Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Drivingcited by 7
- 2024VideoQA-SC: Adaptive Semantic Communication for Video Question Answeringcited by 3
- 2025A Survey on Semantic Communications in Internet of Vehiclescited by 3
- 2023Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inferencecited by 2
- 2025A Survey on Semantic Communications in Internet of Vehicles3 Mar 2025
- 2024VideoQA-SC: Adaptive Semantic Communication for Video Question Answering17 May 2024
- 2024Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving16 Jan 2024
- 2023Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference4 Jun 2023