# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 1 Introduction  
With the rapid development of the Internet of Things (IoT) [1] and Artificial Intelligence (AI) [2] technology, intelligent connected vehicles (ICV) are on the rise. Gartner predicts that in 2023, most of the vehicles on the road will be connected to the Internet, and connected vehicles will be the largest market for 5G in 2025. Traditional vehicle networks are extensions of mobile networks that provide services for a variety of applications such as autonomous driving, traffic management, and driving safety [3]. In addition, most users of autonomous vehicles are also more focused on entertainment services. Emerging services include video, news, road inquiries, and other entertainment and leisure information services, which enhance the convenience and experience of users' travel. Due to the high popularity of intelligent infotainment services, these contents will be frequently requested, so a large number of users and demand data will bring challenges to the performance of the wireless network, which may greatly affect the user experience.  
Edge caching [4, 5] is an effective way to cope with these problems. By arranging caching resources and computing resources at the edge servers, various computing and request services for ICVs can be satisfied at the edge servers, thus allowing users to obtain a better quality of experience (QoE). Therefore, edge caching networks for vehicles have recently attracted widespread attention in the academic community. For example, the authors in [6] explore active caching algorithms for high-capacity content. There are also caching schemes that use mobility prediction [7, 8] and cooperative caching [9] to reduce network latency andincrease network throughput. Most of these works use Zipf distribution models to model the popularity of content. However, in a real vehicle scenario, where the requested content is time-local and variable, modeling popularity using only the Zipf distribution model is not optimal. Therefore, building an efficient edge caching strategy for ICVs is challenging.  
To solve the above problem, we formalize the cooperative caching process between edge nodes as an optimization problem, using our proposed cooperative caching algorithm to allocate edge cache resources reasonably to minimize the latency of content delivery. For this purpose, we creatively propose a hybrid cooperative caching strategy based on temporal convolutional networks, which mainly consists of three parts. Firstly, according to the mobility of vehicles, a dynamic vehicle cluster is established in this paper, then we select the cluster head (CH) and obtain the optimal cache location through the CH. Secondly, we have constructed a model that can improve the accuracy of content popularity prediction, which is based on a temporal convolutional network. TCN has the advantages of high parallelism and fast convergence speed. Then, considering that there are other factors that affect the caching performance of edge nodes, we use Hybrid Content Value to measure the value of the cached content. Finally, a dynamic programming scheme is designed to optimize the strategy. Our main contributions are as follows:  
* We predict content popularity based on TCN, and then a hybrid content value is proposed to measure the cached content;
* Dynamic programming is proposed to make the best cache decision and maximize the overall HCV of the cache strategy;
* Simulation experiments verify the excellent caching performance of THCS algorithm.  
The structure of the remaining parts is as follows. In Section 2, we discuss related work. Section 3 briefly introduces the system model. In Section 4, we detail the design of the THCS scheme and then propose dynamic programming for caching decisions. The performance of our proposed THCS strategy is verified by simulation in Section 5 and then summarized in Section 6.