Deep Learning in Mobile and Wireless Networking: A Survey

By Chaoyun Zhang, Paul Patras, Hamed Haddadi. IEEE Communications Surveys and Tutorials, 2018

1,535 citations recorded by the source corpus.

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, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. 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, in order to help manage 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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8a5d0579590465494c9aba58a857af43b190b6a6

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Citations in the corpus

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “Deep Learning (DL): DL is a breed of the ML techniques which is based on Artificial Neural Networks (ANN) with multiple hidden layers between the input and output layers.”
Adversarial Attacks on Deep-Learning Based Radio Signal Classification (2018, methodology): “This revolution has also sparked interest in applying DL in many other disciplines, including algorithm design for wireless communication systems [2]–[6].”
A Survey on Deep Learning for Multimodal Data Fusion (2020, background): “Recently, many heterogeneous networks have been successfully deployed in both low-layer and high-layer applications, including Internet of Things, vehicular networks, and social networks (Zhang, Patras, & Haddadi, 2019; Meng, Li, Zhang, & Zhu, 2019; Qiu, Chen, Li, Atiquzzaman, & Zhao, 2018).”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “Machine and deep learning techniques allow managing large and heterogeneous data volumes and applications in complex mobile architectures [307], such as MCS systems.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “On the other hand, recent advancements in computational capability of tiny devices and in several ML/DL implementation platforms can result in successful implementation of these methods in onboard devices, such as smartphones [266] or in fog and edge computing platforms [266].”
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey (2020, background): “Processing Units (GPUs) to enhance performance, and access to big-data scale datasets and storage enables deep learning researchers to research, test, and deploy ML algorithms at scale in small edge devices [19], mobile phones [20], and provide AIbased web-services using Application Programming Inte”
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access (2017, background): “Other related works on the general topic of deep learning in mobile and wireless networking can be found in a very recent comprehensive survey [21].”
DarkneTZ: towards model privacy at the edge using trusted execution environments (2020, background): “, the model converging to an optimal solution), a DNN model “memorizes" features of the input training data [44, 57] (see [32, 64] for more details on deep learning), which it can then use to recognize unseen data exhibiting similar patterns.”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, methodology): “For example, DL can be used for representation learning of network conditions [29] whereas Deep Reinforcement”
Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks (2019, background): “Beyond 5G systems are expected to leverage crossfertilizations between wireless systems, core networking, Machine Learning (ML) and Artificial Intelligence (AI) techniques, targeting not only communication and networking tasks, but also augmented environmental perception services [1].”
Machine Learning in the Air (2019, background): “We refer the readers to excellent survey and overview papers on various aspects of ML in wireless communications to gather a more complete picture of its recent applications in different settings [7]– [12].”
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, background): “in [186] and [187], where dedicated surveys are presented.”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, background): “4) Open Issues of Machine Learning for Massive IoT in 6G: Though there has been a ﬂurry of research activities dedicated to machine learning, these works only scratch the surface, and the potential of machine learning to tackle problems for massive IoT in 6G remains largely unexplored [127].”