survey

Where Are the (Cellular) Data?

Authors:

Razvan Stanica,

Catherine RosenbergAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 2

Article No.: 48, Pages 1 - 25

https://doi.org/10.1145/3610402

Published: 15 September 2023 Publication History

Abstract

New generations of cellular networks are data oriented, targeting the integration of machine learning and artificial intelligence solutions. Data availability, required to train and compare machine learning based networking solutions, is therefore becoming an important topic and a significant concern. Operators do collect data, but they rarely share it because of privacy concerns. This article starts by reviewing the few publicly available cellular datasets, which created bursts of innovation with their release. The scarcity of such data is so acute that researchers are collecting network data using their own tools, developed in-house and covered in the second part of this survey.

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Cited By

Korkmaz EAerts SCoesoij RBhatt CVelghe MColussi LLand DPetroulakis NSpirito MBolte J(2024)A comprehensive review of 5G NR RF-EMF exposure assessment technologies: fundamentals, advancements, challenges, niches, and implicationsEnvironmental Research10.1016/j.envres.2024.119524260(119524)Online publication date: Nov-2024
https://doi.org/10.1016/j.envres.2024.119524
Couto RCruz PPacheco RSouza VCampista MCosta L(2024)A survey of public datasets for O-RAN: fostering the development of machine learning modelsAnnals of Telecommunications10.1007/s12243-024-01029-179:9-10(649-662)Online publication date: 5-Apr-2024
https://doi.org/10.1007/s12243-024-01029-1
Couto RCruz PCampista MCosta L(2023)Using Public Datasets to Train O-RAN Deep Learning Models2023 2nd International Conference on 6G Networking (6GNet)10.1109/6GNet58894.2023.10317705(1-8)Online publication date: 18-Oct-2023
https://doi.org/10.1109/6GNet58894.2023.10317705

Index Terms

Where Are the (Cellular) Data?
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Networks
  1. Network performance evaluation
    1. Network measurement
  2. Network types
    1. Mobile networks

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 2

February 2024

974 pages

EISSN:1557-7341

DOI:10.1145/3613559

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 15 September 2023

Online AM: 20 July 2023

Accepted: 11 July 2023

Revised: 13 January 2023

Received: 05 August 2022

Published in CSUR Volume 56, Issue 2

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Cited By

Korkmaz EAerts SCoesoij RBhatt CVelghe MColussi LLand DPetroulakis NSpirito MBolte J(2024)A comprehensive review of 5G NR RF-EMF exposure assessment technologies: fundamentals, advancements, challenges, niches, and implicationsEnvironmental Research10.1016/j.envres.2024.119524260(119524)Online publication date: Nov-2024
https://doi.org/10.1016/j.envres.2024.119524
Couto RCruz PPacheco RSouza VCampista MCosta L(2024)A survey of public datasets for O-RAN: fostering the development of machine learning modelsAnnals of Telecommunications10.1007/s12243-024-01029-179:9-10(649-662)Online publication date: 5-Apr-2024
https://doi.org/10.1007/s12243-024-01029-1
Couto RCruz PCampista MCosta L(2023)Using Public Datasets to Train O-RAN Deep Learning Models2023 2nd International Conference on 6G Networking (6GNet)10.1109/6GNet58894.2023.10317705(1-8)Online publication date: 18-Oct-2023
https://doi.org/10.1109/6GNet58894.2023.10317705

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