Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/32666
Title: Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks
Authors: Zhang, J
Ji, M
Wu, Q
Fan, P
Wang, K
Chen, W
Keywords: semantic communication;vehicle edge computing;task offloading;multi-agent reinforcement learning
Issue Date: 25-Dec-2025
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Zhang, J. et al. (2025) 'Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks', IEEE Networking Letters, 0 (early access), pp. 1 - 5. doi: 10.1109/LNET.2025.3648419.
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.
URI: https://bura.brunel.ac.uk/handle/2438/32666
DOI: https://doi.org/10.1109/LNET.2025.3648419
Other Identifiers: ORCiD: Maoxin Ji https://orcid.org/0009-0000-8179-1710
ORCiD: Qiong Wu https://orcid.org/0000-0002-4899-1718
ORCiD: Pingyi Fan https://orcid.org/0000-0002-0658-6079
ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
ORCiD: Wen Chen https://orcid.org/0000-0003-2133-8679
Appears in Collections:Dept of Computer Science Research Papers

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