# A Survey of Deep Learning for Data Caching in Edge Network
## 4 Deep Learning for Data Caching
### Rnn  
Considering RNN is superior in dealing with sequential tasks, the work [67] applies a bidirectional RNN for online content popularity prediction in mobile edge network. Simple RNN's output depends on previous and current storage, but the bidirectional can also take future information into account. The forecast model consists of three blocks cascadingly: a CNN reads user requests and extracts features; bidirectional LTSM learns association of requests over time step; FNN is added in the end to improve the prediction performance. Then, content eviction is based on the popularity prediction.  
The authors in Reference [65] utilize ESN to predict both content request distribution and end user mobility pattern. The user's preference is viewed as a context which links with personal informationcombining gender, age, job, location, etc. For the request prediction, the input of ESN is user's information vector and the output represent the probability distribution of content. For mobility prediction, the input includes historical and present user's location and the output is the expected position for next time duration. Eventually, the prediction influences the caching content decisions in BBUs and RRHs for the purpose of minimizing traffic load and delay in CRAN. The authors extend their work in Reference [66] by introducing concept-based ESN which can split users' context into different patterns and learn them independently. Therefore, a more accurate prediction is achieved.  
In Reference [68], a caching decision policy named PA-Cache is proposed to predict time-variant video popularity for cache eviction when the space is full. The temporal content popularity is exploited by attaching every hidden layer representation of RNN to an output regression. In order to improve the accuracy, hedge backpropagation is introduced during the training process which decides when and how to adapt the depth of the DNN in an evolving manner. Similarly, the work in Reference [69] also considers caching replacement of video content. A deep LSTM network is utilized for popularity prediction consisting of stacking multiple LSTM layers and one softmax layer, where the input of the network is request sequence data (device, timestamp, location, video's title) without any prepossessing and the output is estimated content popularity. Another work concentrates on prediction, and interactions between user mobility and content popularity can be found [70].  
The work of Reference [71] recognizes the popularity prediction as a Seq2 Seq modeling problem and proposes an LSTM Encoder-Decoder model. The input vector consists of past probabilities where each vector is calculated during a predefined time window. In Reference [72], the authors focus on caching content delivery with the aim of minimizing BSs to cover all requested users, i.e., set cover problem, via coded caching. Unlike Reference [71], an auto encoder is introduced in coded caching stage for file conversion to reduce transmission load. Additionally, an RNN model is employed to select BSs for broadcasting.  
Reference [100] shows the potential of RNN in solving where to cache problem. In Reference [100], a task allocation model is formulated as a knapsack problem and the decision variables represent the task is processed locally in mobile devices (MDs) or remotely in edge servers (ESs). The authors design a multi-pointer network structure of 3 RNNs, where 2 encoders encode MDs and ESs, respectively, 1 decoder demonstrates ES and MD pairing. Considering the similarity of where to cache optimization model and knapsack problem, the multi-pointer network can be transferred for caching location decision after according parameter modifications.