Big data analytics for 5G networks: utilities, frameworks, challenges and opportunities

Taufique, A., Rizwan, A., Imran, A., Arshad, K., Zoha, A. , Abbasi, Q. H. and Imran, M. A. (2021) Big data analytics for 5G networks: utilities, frameworks, challenges and opportunities. In: Tafazolli, R., Wang, C.-L. and Chatzimisios, P. (eds.) Wiley 5G REF: the Essential 5G Reference Online. John Wiley & Sons Ltd. ISBN 9781119471509 (doi: 10.1002/9781119471509.w5GRef230)

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Abstract

In order to meet the challenges of ambitious capacity, user experience, and resource efficiency gains, the next‐generation cellular networks need to leverage end‐to‐end user and network behavior intelligence. This intelligence can be gathered from the mobile network big data which includes the massive telemetric data about network health and status as well as data about user whereabouts, preferences, context, and mobility patterns. As a result, exploitation of big data on wireless cellular network is emerging as an indispensable approach for harnessing intelligence in future wireless communication networks. In this article, we first identify and classify the big data that can be gathered from different layers and ends of a wireless cellular network. We then discuss several new utilities of the big data that can bridge the existing gaps to meet 5G requirements. After that we summarize the existing literature on data analytics for cellular network performance. We present different platforms and two different frameworks to implement big data analytic‐based solutions in 5G and beyond and compare their pros and cons. We then discuss how key performance indicators (KPIs)‐based data collection may not suffice in 5G. Through an exemplary study, we show how to unleash the full potential hidden within the big data, granularity of low‐level performance indicators, and how context is essential. Finally, we highlight the opportunities that can be availed from big data in cellular network and the challenges therein.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Rizwan, Ali and Abbasi, Dr Qammer and Imran, Professor Muhammad
Authors: Taufique, A., Rizwan, A., Imran, A., Arshad, K., Zoha, A., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Publisher:John Wiley & Sons Ltd
ISBN:9781119471509
Published Online:26 January 2021

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