Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks
This work addresses latency and efficiency issues in Internet of Vehicles for highway scenarios, representing an incremental improvement in vehicular edge computing.
The paper tackles efficient task processing in vehicular networks by proposing a Tripartite Cooperative Semantic Communication framework that optimizes semantic task offloading using V2I and V2V communications, resulting in superior performance in simulations compared to other algorithms.
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.