Unsupervised machine learning in 5G networks for low latency communications

Date

2018-02-02

Author

Balevi, Eren
Gitlin, Richard D.

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© 2017 IEEE.This paper incorporates fog networking into heterogeneous cellular networks that are composed of a high power node (HPN) and many low power nodes (LPNs). The locations of the fog nodes that are upgraded from LPNs are specified by modifying the unsupervised soft-clustering machine learning algorithm with the ultimate aim of reducing latency. The clusters are constructed accordingly so that the leader of each cluster becomes a fog node. The proposed approach significantly reduces the latency with respect to the simple, but practical, Voronoi tessellation model, however the improvement is bounded and saturates. Hence, closed-loop error control systems will be challenged in meeting the demanding latency requirement of 5G systems, so that open-loop communication may be required to meet the 1ms latency requirement of 5G networks.

Subject Keywords

fog networking, Machine learning, unsupervised clustering

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044045962&origin=inward
https://hdl.handle.net/11511/100947

DOI

https://doi.org/10.1109/pccc.2017.8280492

Conference Name

36th IEEE International Performance Computing and Communications Conference, IPCCC 2017

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Balevi and R. D. Gitlin, “Unsupervised machine learning in 5G networks for low latency communications,” California, Amerika Birleşik Devletleri, 2018, vol. 2018-January, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044045962&origin=inward.