# A Survey of Deep Learning for Data Caching in Edge Network
## 4 Deep Learning for Data Caching  
We divide the studies regarding deep learning for data caching in edge networks into four categories depending on the DL tools employed: FNN and CNN, RNN, Auto Encoder, and DRL, as summarized in Tables 3-6, respectively. Recently, many works utilize more than one DL techniques for jointly considered caching problems. For instance, at the beginnin, we apply an RNN to predict content popularity and then a DRL to find suboptimal solutions of content placement for the purpose of reducing time complexity. In this case, we classify the related work into DRL since it represents the caching allocation policy. Unless there is mention of the caching location (such as CRs, MBSs, FBSs, EDs, and BBUs), the approaches in this section can be utilized for both Layer 1 and Layer 2 caching. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Training** & **Architecture** & **Pros** & **Cons** \\ \hline \multirow{6}{*}{CNN} & \(\bullet\)any closed and bounded function approximation & \(\bullet\)model training and hyper parameter tuning \\  & \(\bullet\)global spatial feature extraction & \(\bullet\)local spatial feature awareness \\ \cline{2-3}  & \(\bullet\)weight sharing to reduce computation complexity & \(\bullet\)some info missing due to downsampling \\  & \(\bullet\)local spatial feature awareness & \(\bullet\)hyper parameter tuning \\  & \(\bullet\)mainstream deep model in computer vision, also be employed in speech and text processing & \(\bullet\)temporal-sequential task \\ \cline{2-3}  & \(\bullet\)fast training process & \(\bullet\)hyper parameters tuning \\ -vised & \(\bullet\)low dimensional temporal sequence processing & \(\bullet\)high dimensional temporal sequence (like video) processing \\  & \(\bullet\)applied in speech processing, stock price prediction, language modeling, etc. & \\ \cline{2-3}  & \(\bullet\)reasing gradient vanishing & \(\bullet\)computation complexity, not support parallel computing \\ \cline{2-3}  & RNN:LSTM & \(\bullet\)reasing gradient vanishing \(\bullet\)employed in sentiment analysis, machine translation, dialog, etc. & \(\bullet\)computation complexity, not support parallel computing \\ \cline{2-3}  & RNN:GRU & \(\bullet\)reasing gradient vanishing \(\bullet\)faster training than LSTM & \(\bullet\)not support parallel computing \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Unsupervised-vised \\ \end{tabular} } & Auto Encoder & \(\bullet\)feature extraction \(\bullet\)training complexity \\ \cline{2-3}  & Value-based: DQN & \(\bullet\)data visualization & \(\bullet\)the case of continuous actions \(\bullet\) overestimation on reward values \\ \cline{2-3}  & Value-based: Double DQN & \(\bullet\)avoiding overestimation \(\bullet\)faster convergence than DQN & \(\bullet\)the case of continuous actions \(\bullet\)structure complexity \(\bullet\)faster meaningful separation & \(\bullet\)less efficient on small state spaces \\ \cline{2-3}  & \(\bullet\)steady convergence & \(\bullet\)large variance \(\bullet\)high dimensional and continuous tasks \(\bullet\)local optima \\  & Policy-based & \(\bullet\)stochastic policies learning & \(\bullet\)large variance \(\bullet\)local optima \\ \cline{2-3}  & Actor-Critic: DDPG & \(\bullet\)discrete and continuous tasks \(\bullet\)high complexity \(\bullet\)steady convergence & \(\bullet\)high complexity \(\bullet\)slow training \\ \cline{2-3}  & Actor-Critic: A3C & \(\bullet\)efficient learning on continuous tasks \(\bullet\)parallel training & \(\bullet\)high complexity \(\bullet\)intensive requirements on hardware \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of deep learning (DL) architectures.