A comparative study of cellular traffic prediction mechanisms

Author:

Santos Escriche EduardoORCID,Vassaki Stavroula,Peters Gunnar

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Reference38 articles.

1. Huawei Report. Communications networks 2030. https://www-file.huawei.com/-/media/corp2020/pdf/giv/industry-reports/communications_network_2030_en.pdf. Retrieved June 30, 2022.

2. Ericsson Mobility Report. Mobile data traffic outlook. https://www.ericsson.com/en/reports-and-papers/mobility-report/dataforecasts/mobile-traffic-forecast. Retrieved June 30, 2022.

3. Bhushan, N., et al. (2014). Network densification: The dominant theme for wireless evolution into 5G. IEEE Communications Magazine, 52(2), 82–89.

4. Zhu, Y., & Wang, S. (2022). Traffic prediction enabled dynamic access points switching for energy saving in dense networks. Digital Communications and Networks.

5. Azari, A., Salehi, F., Papapetrou, P., & Cavdar, C. (2021). Energy and resource efficiency by user traffic prediction and classification in cellular networks. IEEE Transactions on Green Communications and Networking, 6(2), 1082–1095.

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