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Mobile Information Systems
Volume 2017 (2017), Article ID 9242058, 17 pages
Research Article

A Planning and Optimization Framework for Ultra Dense Cellular Deployments

1Department of Communications and Networks, Aalto University, Espoo, Finland
2Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia
3College of Electrical Engineering, Universidad Tecnológica de Panamá, Panamá, Panama

Correspondence should be addressed to Edward Mutafungwa

Received 2 November 2016; Revised 19 January 2017; Accepted 12 February 2017; Published 8 March 2017

Academic Editor: Massimo Condoluci

Copyright © 2017 David González González et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To accommodate the ever-expanding wireless data traffic volumes, mobile network operators are complementing their macrocellular networks by deploying low-power base stations (or small cells) to offload traffic from congested macrocells and to reuse spectrum. To that end, Ultra Dense Network (UDN) deployments provide means to aggressively reuse spectrum, thus providing significant enhancements in terms of system capacity. However, these deployments entail several challenges, including the increased complexity in network planning and optimization. In this paper, we propose a versatile optimization framework for planning UDN deployments. The planning and optimization framework is underpinned by metrics that consider scalability in terms of number of users, cost of densification, and fairness. The proposed methodology is evaluated using a real-world UDN planning case. The numerical results expose a number of interesting insights, including the impact of different bandwidth allocation strategies and spatial service demand distribution on the performance of various network topologies. Specifically, we provide a performance comparison of the optimized UDN topologies versus random (unplanned), regular grid, and heuristically derived UDN topologies. This comparison further underlines the need for flexible network planning and optimization frameworks as different operator performance metrics of interest may require different radio access networks configurations.