mlsanityData to 14 Oct 2026

Deep Learning in Mobile and Wireless Networking: A Survey

arXiv 1803.04311 ↗First posted 12 Mar 2018cs.NI cs.LGcited by 118

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Abstract (arXiv)

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.

Key facts. Cited by 118 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Mar 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 37; 2022: 23; 2023: 19; 2024: 16; 2025: 15; 2026: 8.

118 papers.

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  11. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  12. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
38 more
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  11. 2022Decentralized Low-Latency Collaborative Inference via Ensembles on the Edgecited by 10
  12. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 10
  13. 2021Personalized Semi-Supervised Federated Learning for Human Activity Recognitioncited by 9
  14. 2021Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniquescited by 9
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  17. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  18. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  19. 2022Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Reviewcited by 8
  20. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  21. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
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  23. 2022A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimizationcited by 7
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  26. 2022Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature reviewcited by 6
  27. 2022Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learningcited by 6
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  35. 2024Fair Resource Allocation in Virtualized O-RAN Platformscited by 4
  36. 2024Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Studycited by 4
  37. 2022Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learningcited by 3
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