A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning

Abstract

Proactive edge caching has been regarded as an effective approach to satisfy user experience in mobile networks by providing seamless content transmission and reducing network delay. This is particularly useful in rapidly changing vehicular networks. This paper addresses the proactive edge caching (at the roadside unit (RSU)) problem in vehicular networks by mobility prediction, i.e., the next RSU prediction. Specifically, the paper proposes a distributed Hybrid cMAB Proactive Caching System where RSUs act as independent learners that implement two parallel online reinforcement learning-based mobility prediction algorithms between which they can adaptively finalize their predictions for the next RSU. The two parallel prediction algorithms are based on Contextual Multi-armed bandit (cMAB) learning, called Dual-context cMAB and Single-context cMAB. The hybrid system is further developed into two variants: Vehicle-Centric and RSU-Centric. In addition, the paper also conducts comprehensive simulation experiments to evaluate the prediction performance of the proposed hybrid system. They include three traffic scenarios: Commuting traffic, Random traffic and Mixed traffic in Las Vegas, USA and Manchester, UK. With the different road layouts in the two urban areas, the paper aims to generalize the application of the system. Simulation results show that the hybrid Vehicle-Centric system can reach nearly 95% cumulative prediction accuracy in the Commuting traffic scenario and outperform the other methods used for comparison by reaching nearly 80% accuracy in Mixed traffic scenario. Even in the completely Random traffic scenario, it also guarantees a minimum accuracy of nearly 60%.

Keywords

• Proactive edge caching
• reinforcement learning
• multi-armed bandit learning
• mobility prediction
• vehicular networks
• roadside units (RSUs)