Data-driven network optimization in ultra-dense radio access networks

Siqi Huang, Qiang Liu, Tao Han, Nirwan Ansari

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

2 Scopus citations

Abstract

The complexity of networking mechanisms will increase significantly because of the dense deployment of radio base stations in ultra-dense mobile networks. As a result, the existing networking mechanisms may be unable to efficiently manage ultra-dense mobile networks. To solve this problem, we propose a datadriven network optimization framework which integrates the big data analysis methods with networking mechanisms. In the proposed framework, we adopt big data analysis methods to divide densely deployed base stations into groups. Then, each group of base stations are managed with networking mechanisms independently. In this way, the complexity of the networking mechanisms is reduced. The key challenge in designing the framework is to optimally group base stations into clusters in realtime. Addressing this challenge, the proposed framework consists of an offline machine learning module and an online base station clustering and network optimization module. The offline machine learning module predicts the optimal number of base station groups in the next time interval based on the historical data. The online base station clustering and network optimization module clusters base stations and optimize the network in realtime. The performance of the proposed data-driven network management framework is validated through network simulations with real network data traces.

Original languageEnglish (US)
Title of host publication2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781509050192
DOIs
StatePublished - Jul 1 2017
Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
Duration: Dec 4 2017Dec 8 2017

Publication series

Name2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE Global Communications Conference, GLOBECOM 2017
CountrySingapore
CitySingapore
Period12/4/1712/8/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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