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Big Data in 5G

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Encyclopedia of Wireless Networks

Synonyms

5G; Machine learning; Next-generation wireless networks; Sparse signal processing; Wireless Big Data

Definition

The fifth-generation wireless systems, abbreviated as 5G (Andrews et al. 2014), are proposed as the next wireless and mobile communications standards beyond the current 4G standards. 5G networks not only aim at providing higher data rate, lower latency, larger capacity, and better customer experience than 4G but also commit to fulfilling the Internet of things (IoT) with reliable and secure services at low costs (Atzori et al. 2010). To this end, 5G networks call for and rely on seamless operations of distinctive wireless technologies and solutions, including cognitive radio (CR) (Akyildiz et al. 2006), massive multiple-input multiple-output (maMIMO) (Larsson et al. 2014), millimeter wave (mmWave) communications (Rappaport et al. 2013), heterogeneous network (HetNet) architecture, cloud-based radio access, edge computing and caching (Hu et al. 2015), device and...

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Author information

Authors and Affiliations

  1. ECE Department, George Mason University, Fairfax, VA, USA

    Yue Wang & Zhi Tian

Authors
  1. Yue Wang
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  2. Zhi Tian
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Corresponding author

Correspondence to Zhi Tian .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo , Waterloo, Ontario, Canada

    Xuemin (Sherman) Shen

  2. Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Ontario, Canada

    Xiaodong Lin

  3. Dpt.of Electrical & Computer Engineering, University of Nebraska–Lincoln, Omaha, Nebraska, USA

    Kuan Zhang

Section Editor information

  1. No affiliation provided

    Rahim Tafazolli

  2. No affiliation provided

    Rose Hu

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Wang, Y., Tian, Z. (2018). Big Data in 5G. In: Shen, X., Lin, X., Zhang, K. (eds) Encyclopedia of Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-32903-1_58-1

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  • DOI: https://doi.org/10.1007/978-3-319-32903-1_58-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32903-1

  • Online ISBN: 978-3-319-32903-1

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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