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A Hybrid Proactive Caching System in Vehicular Networks based on Contextual Multi-armed Bandit Learning
  • Qiao Wang ,
  • David Grace
Qiao Wang
University of York, University of York, University of York

Corresponding Author:[email protected]

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David Grace
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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 roadside unit (RSU)) problem in vehicular networks by mobility prediction, i.e., next RSU prediction. Specifically, the paper proposes a \textit{Hybrid cMAB Proactive Caching System} that implements two parallel online reinforcement learning-based mobility prediction algorithms and allows RSUs to adaptively finalize their predictions to identify as proactive caching nodes. 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%.
2023Published in IEEE Access volume 11 on pages 29074-29090. 10.1109/ACCESS.2023.3259547