Unsupervised machine learning in 5G networks for low latency communications

Balevi E., Gitlin R. D.

36th IEEE International Performance Computing and Communications Conference, IPCCC 2017, California, United States Of America, 10 - 12 December 2017, vol.2018-January, pp.1-2, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2018-January
  • Doi Number: 10.1109/pccc.2017.8280492
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.1-2
  • Keywords: fog networking, Machine learning, unsupervised clustering
  • Middle East Technical University Affiliated: No

Abstract

© 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.