Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

Semantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway scenarios, this paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables Vehicle Users (VUs) to perform semantic task offloading via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Nonlinear Programming (MINLP) problem, which is transformed into two subproblems. First, we innovatively propose a multi-agent proximal policy optimization task offloading optimization method based on parametric distribution noise (MAPPO-PDN) to solve the optimization problem of the number of semantic symbols; second, linear programming (LP) is used to optimize the offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.

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Publication Details

Journal
IEEE Networking Letters
Published
2025-12-25
DOI
https://doi.org/10.1109/lnet.2025.3648419
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
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article

Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

Kezhi Wang, Qiong Wu, Maoxin Ji, Pingyi Fan et al.
IEEE Networking Letters
Vehicular Ad Hoc Networks (VANETs)
article

Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

Kezhi Wang, Qiong Wu, Maoxin Ji, Pingyi Fan, Jingbo Zhang, Wen Chen
article en

Abstract

Semantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway scenarios, this paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables Vehicle Users (VUs) to perform semantic task offloading via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Nonlinear Programming (MINLP) problem, which is transformed into two subproblems. First, we innovatively propose a multi-agent proximal policy optimization task offloading optimization method based on parametric distribution noise (MAPPO-PDN) to solve the optimization problem of the number of semantic symbols; second, linear programming (LP) is used to optimize the offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.

IEEE Networking LettersVol. 8
Jiangnan University (CN), Shanghai Jiao Tong University (CN), Brunel University of London (GB), Middlesex University London (GB), Tsinghua University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 42%
Vehicular Ad Hoc Networks (VANETs)
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