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A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning
DOI:10.1109/ACCESS.2020.3023452.png)
Abstract
En 中文
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.
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
Journal
IF:
3.6
Papers:
9.8W
Citations:
33.3W
Organization
Cited Papers
Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks
IEEE ACCESS
IF3.6

