Browsing by Author "Habtamu, Abayneh"

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    Optimal Multi-Objective Capacity Enhancement and Energy Efficient HetNet Planning and Deployment Approach: The Case of Addis Ababa, Ethiopia
    (Addis Ababa University, 2019-12) Habtamu, Abayneh; Dereje, Hailemariam (PhD)
    Following growth in infrastructure, number of subscribers and availability of smart devices and applications, the aggregate cellular data traffic in Addis Ababa city’s cellular network is increasing exponentially. Moreover, traffic growth follows non-uniform distribution both in space and time. To accommodate this non-uniformly growing data traffic, ethio telecom, for now, the sole telecom service provider in the city, deploy single-layer homogeneous macro base stations (MBSs). Macro-cell densification has been used to increase capacity of the radio access network (RAN). However, excess densification increases the RAN energy consumption, which is becoming a concern for cellular network operators like ethio telecom. Deploying small cells overlaid with macro BSs, named as the heterogeneous network (HetNet), is an energy-efficient (EE) approach capable of meeting the high capacity demand and also keeps network deployment costs low. Many studies have analyzed the HetNet planning and deployment scenario. However, user usage scenarios and their mobility pattern based on realistic data are not considered for the selection of initial small cell candidate locations. Their results differ from one another depending on the environment, cell size, data set, and technology, or the methodology on which the research is made. This research investigates a genetic algorithm (GA) based multi-objective optimization based on system capacity and EE maximization to provide a set of optimal solutions for HetNet selection. In doing so, based on a dataset collected from Addis Ababa cellular network, existing macro BSs data traffic, user usage scenarios, and spatial data traffic demand distribution are generated to identify hotspot areas, and are given as input parameters for the GA for optimal small cell selection. Then, layered planning and deployment is carried out in an interference-limited LongTerm Evolution (LTE) network based on the target requirement. Finally, performance gain of the optimized layered approach is evaluated with system capacity and EE as performance metrics and compared with a uniform topology which is resulted from unplanned small cell deployment through network simulation tool. The simulation results mainly show that both EE (up to 22%) and up to 16% capacity gain with cell edge performance gain (52%) of the target area are, improved by the deployment of optimized small cells, over the uniform (unplanned) deployment.