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Cited by 1,768

The DOI matches its Crossref record; 520 of the 520 references it cites were found in a registry or an index.

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang, Chaoyun, Patras, Paul, Haddadi, Hamed·2019·IEEE Communications Surveys & Tutorials
DOI 10.1109/comst.2019.2904897
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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, 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.

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Research works printed but not linked: 73

  1. klambauer, 2017, Self-normalizing neural networks, Proc Adv Neural Inf Process Syst, 971
  2. 2017, Deep Learning for Siri’s Voice On-Device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis
  3. song, 2016, DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level, Proc Int Joint Conf Artif Intell, 2618
  4. 10.1515/eletel-2017-0006 · doi:10.1515/eletel-2017-0006
  5. subramanian, 2014, Implementation of artificial neural network for mobile movement prediction, Indian J of Sci and Technol, 7, 858, 10.17485/ijst/2014/v7i6.12 · doi:10.17485/ijst/2014/v7i6.12
  6. akopyan, 2016, Design and tool flow of IBM’s TrueNorth: An ultra-low power programmable neurosynaptic chip with 1 million neurons, Proc ACM Int Symp Phys Design, 59, 10.1145/2872334.2878629 · doi:10.1145/2872334.2878629
  7. zhou, 2018, Stable gradient descent, Proc Conf Uncertainty Artif Intell, 1
  8. 10.1109/PDP.2017.98 · doi:10.1109/pdp.2017.98
  9. qi, 2017, PointNet++: Deep hierarchical feature learning on point sets in a metric space, Proc Adv Neural Inf Process Syst, 5099
  10. 2019, Alphastar Mastering the real-time strategy game StarCraft II
  11. 10.12783/ijcsa.2015.0401.02 · doi:10.12783/ijcsa.2015.0401.02
  12. wang, 2015, The applications of deep learning on traffic identification, Proc BlackHat USA, 21
  13. yin, 0, Deep generative models of urban mobility, IEEE Trans Intell Transp Syst
  14. 10.1109/BigDataCongress.2017.10 · doi:10.1109/bigdatacongress.2017.10
  15. 2017, Core ml Integrate machine learning models into your app
  16. 2017, Huawei Announces the Kirin 970–New Flagship SoC With AI Capabilities
  17. 10.1109/ICC.2018.8422106 · doi:10.1109/icc.2018.8422106
  18. moritz, 2018, Ray: A distributed framework for emerging AI applications, Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI), 561
  19. paszke, 2017, On Automatic Differentiation
  20. andrychowicz, 2016, Learning to learn by gradient descent by gradient descent, Proc Adv Neural Inf Process Syst, 3981
  21. 10.1109/MWC.2015.7306536 · doi:10.1109/mwc.2015.7306536
  22. 2017, Cisco Visual Networking Index Forecast and Methodology 2016–2021
  23. kipf, 2017, Semi-supervised classification with graph convolutional networks, Proc of the Int Conf on Learning Representations (ICLR), 1
  24. hsieh, 2017, Gaia: Geo-distributed machine learning approaching LAN speeds, Proc 1st USENIX Symp Netw Syst Design Implement (NSDI '04), 629
  25. xiao, 2017, Tux2: Distributed graph computation for machine learning, Proc 1st USENIX Symp Netw Syst Design Implement (NSDI '04), 669
  26. paolini, 2017, Mastering Analytics How to Benefit From Big Data and Network Complexity An Analyst Report
  27. 2017, IEEE Network Exploring Deep Learning for Efficient and Reliable Mobile Sensing
  28. goodfellow, 2016, Deep Learning
  29. 2016, Theano: A Python framework for fast computation of mathematical expressions, arXiv e prints abs/1605 02688
  30. 2017, NCNN—A High-Performance Neural Network Inference Framework Optimized for the Mobile Platform
  31. 10.1109/INFOCOM.2018.8486327 · doi:10.1109/infocom.2018.8486327
  32. vinyals, 2016, Matching networks for one shot learning, Proc Adv Neural Inf Process Syst, 3630
  33. mismar, 2017, Deep reinforcement learning for improving downlink mmWave communication performance, arXiv preprint arXiv 1707 00408
  34. 10.1145/2938559.2938592 · doi:10.1145/2938559.2938592
  35. 10.1145/3143361.3143369 · doi:10.1145/3143361.3143369
  36. hordri, 2017, A systematic literature review on features of deep learning in big data analytics, Int J Adv Soft Comput Appl, 9, 32
  37. 10.1109/LWC.2016.2595576 · doi:10.1109/lwc.2016.2595576
  38. 10.1007/s11277-017-4934-4 · doi:10.1007/s11277-017-4934-4
  39. 10.1109/TETC.2017.2671847 · doi:10.1109/tetc.2017.2671847
  40. heydari, 2017, Reduce energy consumption and send secure data wireless multimedia sensor networks using a combination of techniques for multi-layer watermark and deep learning, Int J Comput Sci Netw Security, 17, 98
  41. 10.1007/s10489-016-0761-7 · doi:10.1007/s10489-016-0761-7
  42. 10.1587/transcom.2015EBP3358 · doi:10.1587/transcom.2015ebp3358
  43. 10.1109/VTCFall.2015.7390988 · doi:10.1109/vtcfall.2015.7390988
  44. oda, 2017, Performance evaluation of a deep Q-network based simulation system for actor node mobility control in wireless sensor and actor networks considering three-dimensional environment, Proc Int Conf Int Netw Collaborative Syst, 41
  45. 10.1109/SYSMART.2016.7894539 · doi:10.1109/sysmart.2016.7894539
  46. 10.1109/SOFTCOM.2007.4446113 · doi:10.1109/softcom.2007.4446113
  47. 10.1109/TMC.2015.2404786 · doi:10.1109/tmc.2015.2404786
  48. 10.1016/j.comcom.2010.08.008 · doi:10.1016/j.comcom.2010.08.008
  49. 10.1109/TWC.2017.2718526 · doi:10.1109/twc.2017.2718526
  50. 10.1109/MAES.2018.170179 · doi:10.1109/maes.2018.170179
  51. 10.1145/3123878.3131978 · doi:10.1145/3123878.3131978
  52. 10.1109/ICC.2018.8422182 · doi:10.1109/icc.2018.8422182
  53. 10.1109/PERCOMW.2016.7457118 · doi:10.1109/percomw.2016.7457118
  54. 10.1109/ICASI.2018.8394504 · doi:10.1109/icasi.2018.8394504
  55. jiang, 2018, DeepUrbanMomentum: An online deep-learning system for short-term urban mobility prediction, Proc Nat Conf Artif Intell (AAAI), 784
  56. neumann, 2017, Deep channel estimation, Proc International ITG Workshop on Smart Antennas, 1
  57. 10.1109/IJCNN.2017.7966042 · doi:10.1109/ijcnn.2017.7966042
  58. 10.1109/ICC.2018.8422544 · doi:10.1109/icc.2018.8422544
  59. 10.1504/IJSNET.2017.083532 · doi:10.1504/ijsnet.2017.083532
  60. rasmussen, 2006, Gaussian Processes for Machine Learning, 1
  61. 10.1177/1550147717707896 · doi:10.1177/1550147717707896
  62. 10.1117/1.OE.56.10.106103 · doi:10.1117/1.oe.56.10.106103
  63. 10.1155/2015/403242 · doi:10.1155/2015/403242
  64. 10.1049/iet-wss.2013.0055 · doi:10.1049/iet-wss.2013.0055
  65. ossia, 2017, A hybrid deep learning architecture for privacy-preserving mobile analytics, arXiv preprint arXiv 1703 01641
  66. garnelo, 2018, Neural processes, arXiv preprint arXiv 1807 01622
  67. 10.1109/SAHCN.2018.8397121 · doi:10.1109/sahcn.2018.8397121
  68. 10.14257/ijsia.2016.10.9.05 · doi:10.14257/ijsia.2016.10.9.05
  69. 10.1109/FTC.2016.7821774 · doi:10.1109/ftc.2016.7821774
  70. 10.1109/ICC.2018.8422442 · doi:10.1109/icc.2018.8422442
  71. 10.1109/MNET.2018.1800085 · doi:10.1109/mnet.2018.1800085
  72. 10.1109/WCNC.2018.8377044 · doi:10.1109/wcnc.2018.8377044
  73. zhao, 2018, Deep reinforcement learning for network slicing, arXiv preprint arXiv 1805 06591

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Metadata

Authors
Zhang, C., Patras, P., & Haddadi, H.
Venue
IEEE Communications Surveys & Tutorials
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
21
Issue
3
Pages
2224-2287
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
DOI
10.1109/comst.2019.2904897
OpenAlex
W2962883549
Cited by
1,768
References
520
FWCI
138.25
Type
journal article
Language
en
Open access
closed
Licence

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CoChat. (2026). Citation check: Deep Learning in Mobile and Wireless Networking: A Survey. https://app.cochat.ai/papers/10.1109/comst.2019.2904897
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Zhang, C., Patras, P., & Haddadi, H. (2019). Deep Learning in Mobile and Wireless Networking: A Survey. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2019.2904897

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