# A Survey of Deep Learning for Data Caching in Edge Network
## 4 Deep Learning for Data Caching
### Fnn and Cnn  
In Reference [57], the content delivery problem in wireless network is formulated as two MILP optimization models with the aims of minimum delivery time slot and energy consumption, respectively. Both models consider the data rate for content delivery. Considering the computational complexity of solving MILP, CNN is introduced to reduce the feasible region of decision variables, where the input is a channel coefficients matrix. The FNN in Reference [58] plays a similar role as in Reference [57] to simplify the searching space of the content delivery optimization model.  
For resource allocation problems, the authors of Reference [98] model it as linear sum assignment problems then utilize CNN and FNN to solve the model. The idea is extended in Reference [59; 60], where the authors consider where to cache problem among potential CRs and content delivery jointly, which is modeled as MILP for the sake of balancing caching and transmission cost by considering the user mobility, space utilization and bandwidth limitations. The cache allocation is viewed as a multi-label classification problem and is decomposed into several independent sub-problems, where each one correlates with a CNN to predict assignment. The input of CNN is a grey-scale image which combines the information of user mobility, space and link utilization level. In Reference [59], a hill-climbing local search algorithm is provided to improve the performance of CNN, while, in Reference [60], the prediction of CNN is used to feed a smaller MILP model.  
For these above works [57; 58; 59; 60; 98], the FNN or CNN input is extracted from the optimization model. The work in Reference [99] trains a CNN via original graph instead of parameters matrix/image, which makes the process human recognizable and interpretable. Though the authors take the traveling salesman problem not data caching as an example, the method can be viewed as a potential research direction.  
In Reference [61], an ILP model is proposed to minimize the backhaul video-data type load by determining the portion of cached content in BSs. Since the mobile users covered by a BS change frequently, therefore predicting user preference is unnecessary. Instead, the authors concentrate on popular content in general. In the beginning, a 3D CNN is introduced to extract spatio-temporal features of videos. The popularity of new contents without historical information is determined via comparing similar video features. The authors of Reference [62] also considers the spatio-temporal features among visiting contents in a mobile bus WiFi environment. By exploiting the previous 9 days collecting data, the content that the user may visit on the last day and corresponding visiting frequency can be forecast. The social property is taken into account in Reference [63]. By observing users interests on tweets during the 2016 U.S. election, a CNN based predicted model can foresee the content category that is most likely to be requested. Such kind of content would be cached in MBSs and FBSs.  
The work of Reference [64] examines the role of DNN in caching from another aspect. The authors propose an FNN to predict content popularity as a regression problem. The results show that FNN outperforms RNN, though the later is believed to be effective to solve sequential predictions. Moreover, replacing the FNN by a linear estimator does not devalue the performance significantly. The author provides an explanation that FNN would work better than linear predictor in the case of incomplete information, and RNN has more advantages to model the popularity prediction as a classification rather than a regression problem.