# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Slicing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topic** & **Paper** & **DL Model** & **Purpose/Methods** \\ \hline \multirow{5}{*}{Slicing} & [27] & DNN & Uses spatio-temporal relationships between stations to predict future traffic demand patterns \\  & [50] & RNN & Predicts base station pairing for highly mobile users to prepare for future demand \\  & [35] & RNN & Minimizes signaling overhead, latency, call dropping, and radio resource usage using predictive handover \\  & [26] & RNN & Predicts traffic demand by exploiting space and time patterns between base stations \\  & [28] & DNN & Identifies real-time traffic and assigns to relevant network slice \\  & [54] & RL & Offers slicing strategy based on predictions for traffic and resource requirements \\  & [56] & RL & Maximizes network provider’s revenue through automated admission and allocation for slices \\  & [57] & RL & Provides automated priority-based radio resource slicing and allocation \\  & [53] & RL & Constructs network services on demand based on resource use to lower costs \\  & [103] & DNN & Selects slice for resilient and efficient balancing of network load \\ \hline \multirow{5}{*}{Resource Allocation} & [32] & DNN & Foresual resource capacity and demands per slice using network probes \\  & [58] & DNN & Predicts traffic demand and distributes radio resources between slices \\  & [60] & RL & Minimizes cost of delay and energy consumption for multi-user wireless system \\  & [61] & RL & Minimizes end-to-end delay for caching, offloading, and radio resources for IoT \\ \hline \multirow{5}{*}{Caching} & [62] & DNN & Reduces computational time and energy consumption for cache policy at edge \\  & [63] & RL & Jointly optimizes caching and computation for vehicular networks \\  & [64] & RNN & Forecasts user movement and service type to cache and offload tasks in advance \\  & [65] & RL & Predicts optimal cache state for radio networks \\  & [66] & RL & Optimizes cost for caching, computation, and offloading using vehicle mobility and service deadlines restraints \\  & [67] & DNN & Updates cache placement dynamically based on station and content popularity \\  & [68] & DNN & Identifies communities of mobile users and predictive device-to-device caching \\ \hline \multirow{2}{*}{Offloading} & [70] & RL & Optimizes scheduling of offloaded tasks for vehicular networks \\  & [71] & RL & Minimizes energy, computation, and delay cost for multiple task offloading \\ \hline \multirow{2}{*}{QoS} & [59] & RL & Dynamically meets QoS requirements for users while maximizing profits \\  & [73] & DNN & Automates management of network resources for video streaming within QoS range \\ \hline \multirow{3}{*}{Energy} & [82] & DNN & Uses an NLP model to demonstrate DL energy requirements \\  & [83] & DNN & Creates energy model to compare consumption of cloud architectures \\  & [84] & RL & Minimizes energy usage for task offloading from mobile devices \\ \hline \multirow{3}{*}{Security} & [86] & RL & Optimizes MEC security policies to protect against unknown attacks \\  & [87] & DNN & Protects against various types of denial-of-service attacks \\ \cline{1-1}  & [88] & DNN & Develops a federated learning system with differential privacy guarantees \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some relevant applications of deep learning techniques used in network operations discussed in this paper. Some papers could belong in multiple categories because the themes in automated network management overlap, but for simplicity are only listed once. prediction, it is necessary to predict user mobility and, the demand for network resources, as well as be able to classify traffic origin in real time to assign to the correct slice. The research conducted in [26] set the groundwork for this field by using the LSTM model and RNN to analyze real-world cellular data from China to understand the relationship between traffic at separate cell towers.