Deep Learning in Mobile and Wireless Networking: A Survey

TL;DR

This survey explores how deep learning techniques are applied to mobile and wireless networking to address challenges like data management, resource allocation, and analytics in evolving 5G systems.

Contribution

It provides a comprehensive review of deep learning applications in mobile networking, categorizes research domains, and discusses deployment strategies and future challenges.

Findings

Deep learning enhances network management and analytics.

Various platforms enable efficient deployment of deep learning on mobile devices.

Identifies open challenges and future research directions in the field.

Abstract

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.