# A Survey of Deep Learning for Data Caching in Edge Network
## 2 Data Caching Review
### Layer 2 Caching  
Contrast to Layer 1 caching in wired connection, Layer 2 caching considers implementing caching techniques in the wireless network. Though both of them need to solve where to cache, what to cache, cache dimensioning, and content delivery problems, wireless caching is more challenging, and some mature strategies in wired caching cannot be migrated directly to the wireless case. Some reasons come from the listed aspects: the resources in the wireless environment, such as caching storage and spectrum, are limited compared with CRs in Layer 1 Caching; the mobility of end users and dynamic network topologies are also required to be considered during the design of caching strategies; moreover, the wireless channels are uncertain since they can be affected by fading and interference. In wireless caching, where to cache focus on finding the proper candidates among MBS, FBS, ED, even BBU pool and RRU in C-RAN to host the content. Caching at MBS and FBS can alleviate backhaul congestion since end users obtain the requested content from BS directly instead of from CR via backhaul links. Compared with FBS, MBS has a wider coverage and typically, there is no overlap among different MBSs [28]. As mentioned above, the caching space in BSs is limited and it is impractical to cache all popular content. With the aim of improving the cache-hit ratio, a MILP-modeled collaborative caching strategy among MBSs is proposed in Reference [29]. If the accessed MBS does not host the content, the request will be served by a neighbor MBS which caches the file rather than by the data server. For FBS caching, a distributed caching method is presented in Reference [30] and the main idea is that the ED locating in the FBS coverage overlap is able to obtain contents from multiple hosters. Caching at ED can not only ease backhaul congestion but also improve the area spectral efficiency [28]. When the end user requests, he/she would be severed by the local storage if the content is precached in his/her ED or by adjacent ED via Device to Device (D2D) communication if the content is host accordingly. In Reference [31], the authors model the cache-enabled D2D network as a Poisson cluster process, where end users are grouped into several clusters and the collective performance is improved. Individually, caching the interested contents for other users affects personal benefit. In Reference [32], a Stackelberg game model is applied to formulate the conflict among end users and a related incentive mechanism is designed to encourage content sharing. For the case of cache-enabled C-RAN, caching at BBU can ease the traffic congestion in the backhaul, while caching at RRH can reduce the fronthaul communication cost. On the other hand, caching all at BBU raises the signaling overhead of the BBU pool, while at RRH weakens the processing capability. Therefore, where to cache the content in C-RAN makes a substantial contribution to balancing the signal processing capability at the BBU pool and the backhaul/fronthaul costs [28]. The work in Reference [33] investigates caching at RRHs with jointly considering cell outage probability and fronthaul utilization. Due to the end users' mobility, the prediction/awareness of user moving behavior also influences the proper hoster selection. There are some researches exploiting user mobility in cache strategy designing, like Reference [34; 35]. Similar to Layer 1, what to cache decision, as well as eviction policy, of Layer 2 depends on the accurate prediction on content popularity or user preference in the proactive caching method. The content popularity contains the feature of temporal and spatial correlations, which has already been described in Layer 1 Caching. In Layer 2 caching, the proper spatial granularity in popular contents estimation needs to take special attention [36]. For example, the coverage of MBS and FBS are different, which makes the popularity in MBS and FBS are different, as well.