# A Survey of Deep Learning for Data Caching in Edge Network
## 1 Introduction  
Undoubtedly, future 5G and beyond mobile communication networks will have to address stringent requirements of delivering popular content at ultra high speeds and low latency due to the proliferation of advanced mobile devices and data rich applications. In that ecosystem, edge-caching has received significant research attention over the last decade as an efficient technique to reduce delivery latency and network congestion especially during peak-traffic times or during unexpected network congestion episodes by bringing popular data closer to the end users. One of the main reasons for enabling edge caching in the network is to reduce the number of requests that traverse the access and core mobile network, as well as reducing the load at the origin servers that would have to, otherwise, respond to all requests directly in absence of edge caching. In that case, popular content and objects can be stored and served from edge locations, which are closer to the end users. This operation is also beneficial from the end user perspective since edge caching can dramatically reduce the overall latency to access the content and increase in the sense of overall user experience. It is also important to note that the notion of popular content means that the requests of top 10% of video content on the Internet account for almost 80% of all traffic, which relates to multiple requests from different end users of the same content [1]. Recently, deep learning (DL) has attracted significant attention from both academia and industry and has been applied to diverse domains, like self-driving, medical diagnosis, and playing complex games, such as Go [2]. DL has also made their way into communication areas [3]. In this paper, we pay attention to the application of DL in caching policy. Though there are some earlier surveysrelated to machine learning applications, they either focus on general machine learning techniques for caching [4; 5; 6], or concentrate on overall wireless applications [7; 8; 9]. The work [3] provides a big picture of applying machine learning in wireless communications. In Reference [4], the authors consider the machine learning on both caching and routing strategy. A comprehensive survey on machine learning applications for caching content in edge networks is provided in Reference [5]. The researchers in Reference [6] provide a survey about machine learning on mobile edge caching and communication resources. On the other hand, Reference [7] overviews how artificial neural networks can be employed for various wireless network problems. The authors in Reference [8] detail a survey on deep reinforcement learning (DRL) for issues in communications and networking. Reference [9] presents a comprehensive on deep learning applications and edge computing paradigm. Our work can be distinguished from the aforementioned papers based on the fact that we focus on the deep learning techniques on content caching and both wired and wireless caching are taken into account. Our main contributions are listed as follows:  
* We classify the content caching problem into Layer 1 Caching and Layer 2 Caching. Each layer caching consists of four tightly coupled subproblems: where to cache, what to cache, cache dimensioning, and content delivery. Related researches are provided accordingly. * We present the fundamentals of DL techniques which are widely used in content caching, such as convolutional neural network, recurrent neural network, actor-critic model-based deep reinforcement learning, etc. * We analyze a broad range of state-of-the-art literature which use DL to content caching. These papers are compared based on the DL structure, layer caching coupled subproblems, and the objective of DL in each scenario. Then, we discuss research challenges and potential directions for the utilization of DL in caching. The rest of this survey is organized as follows (as illustrated in Figure 1). Section 2 presents the categories of content caching problem. Section 3 reviews typical deep neural network structures.