On edge deep learning implementation: approach to achieve 5G

Dhritiman Mukherjee, Aman Anand

Abstract: Through 5G networks, mobile edge computing (MEC) brings the power of cloud computing, storage, and analysis closer to the end user. Innovative inventions in the domain of multimedia and others such as connected cars, large-scale IoT, video streaming, and industry robotics are made possible by improved speeds and reduced delays. On the other hand, in mobile edge computing, machine learning (ML) is leveraged to predict demand changes based on cultural events, natural disasters, or daily travel patterns, and it prepares the network by automatically scaling up network resources as required. Mobile edge computing and ML together allow seamless network management automation to decrease operating costs and boost user experience. In this paper, we discuss the state of art with in mobile edge computing with deep learning to server low-latency, real time application by providing application specific resource allocation. The experimental results have indicated significant amount of improvement in respond time while executing in low latency.