# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 1 Introduction  
By 2024, 5G mobile edge computing (MEC) is expected to be a multi-million-dollar industry with enterprise deployments reaching $73M [1]. Each year, the complexity of data continues to grow. The rise of network complexity systems stems from the increase of on-demand and customizable services. Internet service providers must accommodate traffic for web browsing, connected vehicles, video streaming, online gaming, voice over IP and always-on Internet of Things (IoT) device transmissions. New constraints introduced by on-demand services as listed above require a radical transformation of fixed and mobile access networks. Fifth-generation (5G) mobile networks are being developed to serve the increasing levels of traffic demand and diversity. To cope with the complex traffic demanded by modern users, network operators are adopting cloud-computing techniques. 5G networks will use software-defined networks (SDN) and network function virtualization (NFV) to reduce the operational cost of growing mobile networks to provide on-demand services. Long-term, end users can expect performance enhancements because 5G is optimized to provide low-latency, high-availability, and high-bandwidth communication for multiple use cases, including delay-sensitive applications, such as autonomous vehicles and automated Industry 4.0 robotics. The stringent functional requirements for 5G networks have forced designers to rethink the backbone and access network architectures to better support core functions and dynamic network services. The introduction of mobile edge computing disrupts the traditional separation between the access network (secure and reliable transport between end users) and the core network (information computing and storage). The combination of mobile edge computing and cloud computing blendsthe control and management planes to enable an extension of virtualized network resources and the creation of end-to-end services based on SDN, NFV, and slicing techniques in 5G. mobile edge computing can then complement the goals of the access network to solve existing challenges including quality of service/experience, security, and power consumption as part of the necessary network transformation. Alongside NFV and SDN, mobile edge computing was recognized by the European 5 GPPP (Public Private Partnership) as a key enabling technology that will help satisfy the demanding requirements for throughput, latency, scalability and automation in 5G [2]. Mobile edge computing places computational processing power closer to the end user. This proximity alleviates the amount of traffic delivered across the core network to large data centers, improves response speed with latencies below ten milliseconds [3], and coordinates with data centers to offload some computational tasks, such as online inference from the main cloud. Mobile edge computing can enable real-time analysis through cloud-computing capabilities in a secure and context-aware manner with collaboration between network operators and application providers [2]. Managing thousands of heterogeneous connections under strict response constraints for applications, service creation, and network administration presents a complex challenge to 5G networks using mobile edge computing. To realize the benefits of mobile edge computing, there is a need to develop automated procedures to provide, orchestrate, and manage network services and applications under conditions that change over time and across locality. A promising solution is introducing machine learning (ML) to network operations to meet this new set of demands that are beyond the limitations of traditional optimization techniques. The development of 5G core network and mobile edge computing division of labor depends on automated network management that is powered by efficient machine learning (ML) techniques. Traditional optimization techniques are not adaptable enough to handle the complex, real-time analysis required in 5G networks. In the past 20 years, machine learning has become widely known for pattern recognition.