Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

TL;DR

This paper introduces a semantic-aware cooperative framework for vehicular networks that optimizes task offloading using advanced algorithms, improving efficiency in highway scenarios with vehicle-to-infrastructure and vehicle-to-vehicle communications.

Contribution

It proposes a novel Tripartite Cooperative Semantic Communication framework and a multi-agent optimization method for semantic task offloading in vehicular edge computing.

Findings

Superior performance over existing algorithms in simulations

Effective optimization of semantic task offloading

Enhanced vehicular network communication efficiency

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 solve offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.