# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 7 Conclusions  
Mobile edge computing plays a crucial role in helping 5G networks achieve the goals for eMBB, URRLC, mMTC as demand for network resources steadily increases with the rise of IoT and video streaming devices. Deep learning, a powerful subset of machine learning, can be adapted for use in 5G network operations to predict user behavior and automate the management of dynamic network resources. Using deep learning can both improve user experience and lower operational costs for telecommunication companies in the future.  
This document provides insight into four main ideas in ML for 5G mobile edge computing. First, in Section 2, we show that the mobile edge computing is a prime candidate to implement the new verticals, features, and service categories required to deploy 5G. Second, we provide an overview of the key deep learning concepts and how they can be adapted to work best in mobile edge computing environments with computing and memory limitations. Section 3 explores the suitable deep learning techniques to automate operations required to manage services and applications over the increasingly complex 5G networks. Third, Section 5 develops a taxonomy of the challenges and trade-offs posed by the introduction of a subset of deep learning techniques in the mobile edge computing. 5G networks enable a diverse set of new applications and on-demand services with strict requirements, which substantially increase complexity for which deep learning methods are uniquely suited. Finally, Section 6 presents proofs of concept and the most relevant developments that combine the mobile edge computing with ML in the 5G environment. We also discuss mobile edge computing applications and how newly designed implementation standards for deep learning in 5G networks can enhance various verticals for 5G, including IoT, AR/VR, vehicle networks, and smart cities.  
In conclusion, we hope that this survey will provide information on the use and adaptation of deep learning to improve mobile edge computing. These techniques may stimulate further research and deployment of scenarios that allow for increased automation of the network and services in the future.  
Conceptualization, S.S. and C.C.-P.; Methodology, S.S. and C.C.-P.; Investigation, M.M.; Resources, M.M., C.C.-P. and S.S.; Writing--Original Draft Preparation, M.M.; Writing--Review Editing, M.M., C.C.-P. and S.S.; Supervision, S.S. and C.C.-P. All authors have read and agreed to the published version of the manuscript. This research was funded by a grant from the Fulbright Commission in Spain and by the Ministerio de Ciencia e Innovacion of the Spanish Government under the project PID2019-108713 RB-C51. This research is supported by the Fulbright Commission in Spain and by the Ministerio de Ciencia e Innovacion of the Spanish Government. The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & \"Fifth-Generation\" Mobile Networks \\ MEC & Mobile Edge Computing / Multi-Access Edge Computing \\ SDN & Software-Defined Networks \\ NFV & Network Function Virtualization \\ European 5 GPP & European 5G Public Private Partnership \\ ML & Machine Learning \\ DL & Deep Learning \\ OPEX & Operating Expenditures \\ CAPEX & Capital Expenditures \\ \end{tabular}