Deep Learning in Mobile and Wireless Networking: A Survey

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.

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• Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs
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• A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
• Realtime Scheduling and Power Allocation Using Deep Neural Networks
• Running Neural Networks on the NIC
• Intelligent Network Slicing for V2X Services Towards 5G
• Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward