A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning

Beneyam Berehanu Haile*, Edward Mutafungwa, Jyri Hamalainen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
97 Downloads (Pure)

Abstract

The trials and rollout of the fifth generation (5G) network technologies are gradually intensifying as 5G is positioned as a platform that not only accommodates exploding data traffic but also unlocks a multitude use cases, services and deployment scenarios. However, the need for hyperdense 5G deployments is revealing some of the limitations of planning approaches that hitherto proved adequate for pre-5G systems. The hyperdensification envisioned in 5G networks not only adds complexity to network planning and optimization problems, but underlines need for more realistic data-driven approaches that consider cost, varying demands and other contextual attributes to produce feasible topologies. Furthermore, the quest for network programmability and automation including the 5G radio access network (RAN), as manifested by network slicing technologies and more flexible RAN architectures, are also among other factors that influence planning and optimization frameworks. Collectively, these deployment trends, technological developments and evolving (and diverse) service demands point towards the need for more holistic frameworks. This article proposes a data-driven multiobjective optimization framework for hyperdense 5G network planning with practical case studies used to illustrate added value compared to contemporary network planning and optimization approaches. Comparative results from the case study with real network data reveal potential performance and cost improvements of hyperdense optimized networks produced by the proposed framework due to increased use of contextual data of planning area and focus on objectives that target demand satisfaction.

Original languageEnglish
Article number9194766
Pages (from-to)169423-169443
Number of pages21
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Planning
  • 5G mobile communication
  • Optimization
  • Market research
  • Long Term Evolution
  • Radio access networks
  • Macrocell networks
  • 5G
  • hyperdense networks
  • network planning
  • techno-economics
  • network data analytics
  • multiobjective optimization
  • small cells
  • Cloud-RAN
  • RAN slicing
  • BIG DATA
  • WIRELESS
  • DEPLOYMENT
  • RAN

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