# A Survey of Deep Learning for Data Caching in Edge Network
## 4 Deep Learning for Data Caching
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Caching Problem** & **DL Objective** & **DL Architecture** & **Main Conclusions** \\ \hline
[65] & what to cache & predict requested content \& user mobility & \multirow{2}{*}{ESN} & The proposed method combine the ESN framework with sublinear algorithms and the simulation is based on real data from content provider YouKu, as well as university BUT. \\ \hline
[66] & what to cache & predict requested content \& user mobility & \multirow{2}{*}{ESN} & A conceptor-based ESN approach is proposed for users’ behavior prediction and this paper analyze the caching application at the level of UAVs. \\ \hline
[67] & what to cache & predict content popularity & CNN, Bidirectional LSTM\& FNN & This paper tracks the association of temporal requests and achieves above 60\% prediction accuracy. \\ \hline
[68] & what to cache & predict content popularity & GRU & The proposed framework combines RNN with the hedge strategy and the training data is from content provider iQYi. \\ \hline
[69] & what to cache & predict content popularity & LSTM & The proposed scheme can learn the content popularity at long and short time scales. The experiments on iQYi and Movielens dataset show it improves the hit ratio by 20\(\sim\)32\%. \\ \hline
[70] & what to cache & predict content popularity & LSTM & The paper proposes a LSTM model to predict content popularity and user mobility. \\ \hline
[71] & what to cache & predict content popularity & LSTM Seq2 Seq & The content popularity prediction is recognized as a seq2 seq model and a general framework for end-to-end cache making is created. \\ \hline
[72] & content delivery & reduce traffic load, select optimal BS subset & Auto Encoder\& Bidirectional GRU Seq2 Seq & A new coded caching approach using auto encoder to reduce network load and a learning model to solve the cover problem with attention scheme and beam search. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of recurrent neural network (RNN) for data caching. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Caching Problem** & **DL Objective** & **DL Architecture** & **Main Conclusions** \\ \hline
[57] & content delivery & reduce feasible region of time slot allocation & CNN & The proposed method combine the prediction from CNN with the optimal branch \& bound algorithm. \\ \hline
[58] & where to cache, content delivery & determine MBSs for caching \& delivery duration & FNN & A well-trained FNN can achieve around 90\% approximation to the optimum. \\ \hline
[59] & where to cache, content delivery & nominate proper CRs for caching & parallel CNN & The optimization model is transformed to a grayscale image and a cluster of CNNs  are trained to capture spatial features. \\ \hline
[60] & where to cache, content delivery & reduce feasible region for caching & parallel CNN & The proposed framework provides a speed up by 75 times with an additional cost of less than 5\% in specific cases. \\ \hline
[61] & what to cache & extract video features & 3D CNN & Both published and unpublished videos are considered in the popularity predicting scheme. \\ \hline
[62] & what to cache & predict requested content \& frequency & FNN & The historical visiting records in a metropolis-scale bus WiFi network help to predict future events. \\ \hline
[63] & what to cache & predict requested content & CNN & The users’ interests are predicted by CNN analysis on tweets. \\ \hline
[64] & what to cache & predict content popularity & FNN & This paper questions the role of DNN in caching applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of fully-connected neural network (FNN) and convolutiuonal neural network (CNN) for data caching. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & \begin{tabular}{c} **Caching** \\ **Problem** \\ \end{tabular}