Data-Driven Multiobjective Optimization for Massive MIMO and Hyperdensification Empowered 5G Planning under Realistic Network Environment

dc.contributorAalto Universityen
dc.contributor.authorZeleke, Seifu G.
dc.contributor.authorHaile, Beneyam B.
dc.contributor.authorBekele, Ephrem T.
dc.contributor.authorMutafungwa, Edward
dc.contributor.authorHämäläinen, Jyri
dc.contributor.departmentAddis Ababa University
dc.contributor.departmentDepartment of Information and Communications Engineering
dc.contributor.departmentWireless & Mobile Communications
dc.descriptionFunding Information: We would like to thank Aalto University for allowing us to use its high-speed computer cluster (Triton) for our computationally intensive simulation campaign. In addition, we would also like to thank Ethio Telecom for the raw network data that is applied for the formulation of user and demand distribution for the case study. We also thank the Institute of Telecommunication TU Wien, Austria, for the Vienna 5G system-level simulator. The Article Processing Charges (APC) of the study will be covered by my coauthors Dr. Edward Mutafungwa and Prof. Jyri H m l inen (from Aalto University, Finland). Publisher Copyright: © 2023 Seifu G. Zeleke et al.
dc.description.abstractTo accommodate the increasing data rate demand, the fifth-generation (5G) cellular network came up with new technological advancements including massive multiple-input multiple-output (massive MIMO) and hyperdensification which can significantly boost network capacity. On the other hand, the introduction of these technologies along with their heterogeneity brings a challenge in terms of network operators' need to identify a cost-effective optimal deployment approach which is hardly entertained by the legacy planning and optimization method. Hence, to leverage the core benefits of those technologies in a cost-effective manner, we need a holistic planning framework that takes into account their coverage, capacity and cost impact, and realistic spatiotemporal distribution of users. In this work, we present a data-driven multiobjective optimization planning framework that can be used not only for small cells but also for massive MIMO. The planning framework is illustrated using a 5G planning case study for a service area in Addis Ababa, Ethiopia, considering its realistic network data that is collected from the network management system and different potential identified deployment options. Ray tracing is employed to compute propagation, and users and demands are distributed based on the realistic network data. A two-stage optimization and a joint optimization are applied to identify points that provide optimal network performance. Simulation results reveal that the planning method provides Pareto points for different deployment options that can significantly improve the performance of the existing network while reducing the total network cost.en
dc.description.versionPeer revieweden
dc.identifier.citationZeleke , S G , Haile , B B , Bekele , E T , Mutafungwa , E & Hämäläinen , J 2023 , ' Data-Driven Multiobjective Optimization for Massive MIMO and Hyperdensification Empowered 5G Planning under Realistic Network Environment ' , Wireless Communications and Mobile Computing , vol. 2023 , 7146912 .
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dc.publisherJOHN WILEY & SONS
dc.relation.ispartofseriesWireless Communications and Mobile Computingen
dc.relation.ispartofseriesVolume 2023en
dc.titleData-Driven Multiobjective Optimization for Massive MIMO and Hyperdensification Empowered 5G Planning under Realistic Network Environmenten
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi