RSMA-Assisted Distributed Computation Offloading in Vehicular Networks Based on Stochastic Geometry

The proliferation of intelligent connected vehicles and related applications with computation-intensive tasks has boosted a pivotal requirement for the unprecedented low latency and high throughput of the Internet-of-Vehicle (IoV). Rate-splitting multiple access (RSMA) has emerged as a promising technology for achieving high spectral efficiency and maintaining reliable connectivity in mobile scenes. Inspired by the idea of distributed computing, an RSMA-assisted distributed computation offloading scheme (RDCOS) in vehicular networks is designed, where various computation auxiliary nodes (CANs) are introduced in a stochastic geometry approach to alleviate the computation dilemma of the task vehicle. After analyzing the CAN pairing and the successful accessing probability (SAP), the total offloaded tasks maximization problem of the task vehicle is investigated. Then a successive convex approximation (SCA)-based computation offloading optimization algorithm (SCOOA) is developed to obtain the solution. The results of the performance evaluation not only verify the plausibility of the proposed model but also indicate that the RSMA can significantly promote the total offloaded tasks under moving conditions, which confirms the superiority of the proposed scheme through simulations.