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, Amerika Birleşik Devletleri, 10 - 12 Aralık 2017, cilt.2018-January, ss.1-2 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2018-January
  • Doi Numarası: 10.1109/pccc.2017.8280492
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.1-2
  • Anahtar Kelimeler: fog networking, Machine learning, unsupervised clustering
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

© 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.