Deep Neural Mobile Networking

TL;DR

This paper explores the application of deep neural networks to address the increasing complexity of mobile networks, enabling automatic feature extraction and improved management of network data.

Contribution

It introduces the use of deep learning techniques to automate feature extraction and enhance network monitoring and management in complex mobile environments.

Findings

Deep learning enables automatic feature extraction from raw network data.

AI techniques improve network management efficiency.

Deep neural networks uncover hidden correlations in mobile data.

Abstract

The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering. In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations that would otherwise have been too difficult to extract by human experts. In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. The performance of artificial intelligence (AI) has achieved in other domains draws unprecedented interest from both academia and industry in employing deep learning approaches to address technical challenges in mobile networks. This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks.