Big-data-driven network partitioning for ultra-dense radio access networks

Siqi Huang, Tao Han, Nirwan Ansari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

The increased density of base stations (BSs) may significantly add complexity to network management mechanisms and hamper them from efficiently managing the network. In this paper, we propose a big-data-driven network partitioning and optimization framework to reduce the complexity of the networking mechanisms. The proposed framework divides the entire radio access network (RAN) into multiple sub-RANs and each sub-RAN can be managed independently. Therefore, the complexity of the network management can be reduced. Quantifying the relationships among BSs is challenging in the network partitioning. We propose to extract three networking features from mobile traffic data to discover the relationships. Based on these features, we engineer the network partitioning solution in three steps. First, we design a hierarchical clustering analysis (HCA) algorithm to divide the entire RAN into sub-RANs. Second, we implement a traffic load balancing algorithm to characterize the performance of the network partitioning. Third, we adapt the weights of networking features in the HCA algorithm to optimize the network partitioning. We validate the proposed solution through simulations designed based on real mobile network traffic data. The simulation results reveal the impacts of the RAN partitioning on the networking performance and the computational complexity of the networking mechanism.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
EditorsMerouane Debbah, David Gesbert, Abdelhamid Mellouk
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389990
DOIs
StatePublished - Jul 28 2017
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: May 21 2017May 25 2017

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Other

Other2017 IEEE International Conference on Communications, ICC 2017
CountryFrance
CityParis
Period5/21/175/25/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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