车路协同中用语义通信优化任务卸载，降低延迟提升效率
Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks
- 三端协同框架，通过车与车、车与路通信实现语义任务卸载
- 比传统算法延迟更低，语义符号数与卸载比例更优
- 适合高速场景下的智能网联汽车系统设计与优化
语义通信（SC）结合车联网边缘计算（VEC）为智能网联汽车（IoV）提供高效的边缘任务处理范式。针对高速公路场景，本文提出三端协同语义通信（TCSC）框架，使车载用户（VUs）通过车与基础设施（V2I）及车与车（V2V）通信完成语义任务卸载。考虑任务延迟与语义符号数量，构建混合整数非线性规划（MINLP）问题，并分解为两个子问题：首先提出基于参数化分布噪声的多智能体近端策略优化方法（MAPPO-PDN），求解语义符号数量；其次采用线性规划（LP）求解卸载比例。仿真结果表明，该方案性能优于其他算法。
原文摘要 · Abstract (English)
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 solve offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。