# A Survey of Deep Learning for Data Caching in Edge Network
## 4 Deep Learning for Data Caching
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** &
\begin{tabular}{c} **Caching** \\ **Problem** \\ \end{tabular} & **DL Objective** & **DL Architecture** & **Main Conclusions** \\ \hline
[84] & what to cache & decide cache placement & external memory-based DQN & The proposed algorithm can achieve an improvement in cache hit rate and long-term reward in a single BS scenario. \\ \hline
[85] & what to cache & decide cache placement & A3C & The IoT data transiency and dynamic context characteristics are jointly considered under data freshness and communication cost. \\ \hline
[86] & what to cache & decide cache placement & hyper DQN & This paper tackle a two-timescale caching task with employing DQNs in parallel. \\ \hline
[87] & what to cache & decide cache placement & DQN & A chunk-based caching scheme in data processing network considers network performance and processing efficiency. \\ \hline
[88] & what to cache & decide cache replacement\& power & DQN & This paper tries to minimize latency in a downlink of F-RAN in a centralized mode. \\ \hline
[89] & what to cache & \begin{tabular}{c} predict popularity\& \\ \end{tabular} & \
\begin{tabular}{c} CNN, LSTM, \\ Convolutional RNN\& \\ Q Learning \\ \end{tabular} &
\begin{tabular}{c} The DNN model is proposed to \\ predict request demand and \\ content popularity with Q \\ learning for model selection. \\ \end{tabular} \\ \hline
[90] & where to &
\begin{tabular}{c} service allocation, \\ computation \\ offloading\& cache \\ placement \\ \end{tabular} & dueling DQN & The authors provide a social trust scheme with both direct and indirect observation in mobile social networks for edge computing, caching, and D2D. \\ \hline
[91] & content & users grouping & dueling DQN & This paper considers both caching and interference alignment under time-varying channel. \\ \hline
[92] & where to & BS connection, \\ cache & computation & &
\begin{tabular}{c} the authors propose an \\ integrated scheme for dynamic \\ orchestration of networking, \\ caching and computing in \\ vehicle networks. \\ \end{tabular} \\ \hline
[93] & \begin{tabular}{c} what to \\ cache, \\ content \\ delivery \\ \end{tabular} & \
\begin{tabular}{c} decide caching \& \\ bandwidth allocation \\ \end{tabular} & DDPG &
\begin{tabular}{c} A cooperative caching policy \\ among base station, roadside \\ units and vehicles is proposed. \\ \end{tabular} \\ \hline
[94] & \begin{tabular}{c} what to \\ cache, \\ content \\ delivery \\ \end{tabular} & \begin{tabular}{c} caching, computing \\ offloading\& radio \\ allocation \\ \end{tabular} & \
\begin{tabular}{c} actor-critic model \\ with natural policy \\ gradient \\ \end{tabular} &
\begin{tabular}{c} This article provides a joint \\ method for caching, computing \\ and radio allocation in the \\ fog-enabled IoT to minimum \\ average latency for all requests. \\ \end{tabular} \\ \hline
[95] & \begin{tabular}{c} what to \\ cache, \\ content \\ delivery \\ \end{tabular} & \begin{tabular}{c} schedule multicast\& \\ replace cache \\ \end{tabular} & \
\begin{tabular}{c} variational \\ auto-encoder\& \\ double DQN \\ \end{tabular} &
\begin{tabular}{c} A double coded caching \\ algorithm is proposed to increase \\ the robustness of \\ wireless transmission. \\ \end{tabular} \\ \hline
[96] & \begin{tabular}{c} where \& \\ what to cache \\ offloading \\ \end{tabular} & \
\begin{tabular}{c} predict popularity, \\ decide caching \& task \\ DQN \\ \end{tabular} &
\begin{tabular}{c} GRU\& multi-agent \\ in multi-user non-orthogonal \\ multiple access mobile edge \\ computing system. \\ \end{tabular} \\ \hline
[97] & \begin{tabular}{c} where\&what \\ to cache, \\ content \\ delivery \\ \end{tabular} & \begin{tabular}{c} predict user mobility\& \\ content popularity, \\ D2D link \\ \end{tabular} & \
\begin{tabular}{c} ESN, LSTM\&actor- \\ critic model \\ \end{tabular} &
\begin{tabular}{c} A joint content placement and \\ delivery scheme is proposed to \\ solve the medium access \\ contention in the cache-enabled \\ D2D network. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}