# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 2 Caching in Future Cellular Networks  
We have seen a tremendous growth in cellular networks in the last decade. This growth is mainly driven by the exponentially growing demands of a high data rate by the end users. Various techniques have been employed to achieve a high data rate as well as spectral efficiency. Spectral efficiency can be achieved by efficiently managing cellular spectrum. These techniques range from control data split architecture (CDSA) [19], a cloud-radio access network (CRAN) to ultra-dense cellular networks (UDCN) [20] and D2D communication [21]. Almost all of these technologies are considered as enabling technologies for an upcoming fifth-generation (5G) cellular network. Among these, multi-tier architecture and D2D communication are well thought out as key enablers for providing access to a massive number of users as well as high data rate connectivity. In multi-tier architecture, there is an overlaid macro-cell (MC) comprising multiple micro-cells (mCs) operating in its coverage area. MC provides an overall coverage service and mobility management, whereas mCs can provide local area services and high data rate connectivity. On the other hand, caching in cellular networks has been introduced to exploit storage capacity of diverse network devices. These devices range from user side (user equipment (UE), edge devices) to the network side (mCs, MC and extended packet core). In the following, we briefly describe how caching can be done on these diverse network devices. _Edge Caching_: When an UE generates a request for specific content, first it will search for that content in its own memory and if that content is cached locally (edge), the UE will access that without any delay. This kind of caching can drastically reduce the backhaul traffic as well as access delay at the cost of large UE memory requirements. _Cluster-Head Caching_: If the requested content is not available at UE itself, it will then ask its peers for that content through D2D communication. One way is to search for the content sequentially in every peer device. The other way could be to search for an influential user where it is highly probable that content accessed or generated by this influential user will become popular due to its relativity higher connectivity [22]. D2D communication will enable this kind of caching in the network. This mechanism also reduces backhaul traffic and access delay. _Micro-Cell Caching_: If the requested content is not found using D2D communication, then mC will provide that content to the user if it is cached there. mC will utilize a radio access network (RAN) for delivering that content and not affect the backhaul channel. _Macro-Cell Caching_: If the content can not be accessed using the above-mentioned ways, then MC will provide that content by downloading it from EPC (Evolved Packet Core) or from the cloud itself. It will ensure that content is delivered successfully. This mechanism can cause large access delays and an increase in backhaul traffic. In the above, we answer the question _where to cache?_; however, in order to understand _how to cache?_, we might have two different approaches such as reactive and proactive. In the following, we describe these approaches in the context of cellular communication. _Reactive Caching_: In the reactive caching approach, files are stored in cache if they were repeatedly requested in the past based on the history of the files accessed. In this caching, any content that remains in high demand for a specific period of time can be cached--for example, viral videos, popular tweets and other highly requested social media content. _Proactive Caching_: It is a mechanism to predict future content that might be requested by the users. Based on previous content and user histories, certain content might become popular in the near future, which will be cached before they are actually requested, e.g., popular entities in various societies might trigger similar kinds of trends such as top trends on Twitter, etc.