|Authors||Y. Dai, D. Xu, Y. Lu, S. Maharjan and Y. Zhang|
|Title||Deep Reinforcement Learning for Edge Caching and Content Delivery in Internet of Vehicles|
|Project(s)||Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications|
|Publication Type||Proceedings, refereed|
|Year of Publication||2019|
|Conference Name||IEEE/CIC International Conference on Communications in China (ICCC)|
To enable the emerging vehicular applications and multimedia services in an Internet of Vehicles (IoV) framework, edge caching is a promising paradigm which can cache content in proximity to vehicles, and thus alleviate heavy load on backhaul links and contribute in reducing transmission latency. However, in a multi-access vehicular network, complex content delivery and high mobility of vehicles introduce new challenges to support edge caching in a dynamic environment. Deep Reinforcement Learning (DRL) is an emerging technique to solve the issue with time-varying feature. In this paper, we utilize DRL to design an optimal vehicular edge caching and content delivery strategy for minimizing content delivery latency. We first propose a multiaccess edge caching and content delivery framework in vehicular networks. Then, we formulate the vehicular edge caching and content delivery problem and propose a novel DRL algorithm to solve it. Numerical results demonstrate the effectiveness of proposed DRL-based algorithm, compared to two benchmark solutions.