Techno-economic Investigation Of Street Furniture Usage For Outdoor Small Cell Planning The Case Of Addis Ababa Ethiopia

For more than a decade, mobile network data traffic and users’ demand are considerablyrnincreased due to the advent of evolving mobile technologies, capable end-user devicesrnand data-intensive content. Different forecasts show that the growth will continue forrnsimilar reasons. To accommodate the increasing network traffic and users’ demand,rnoperators should continuously expand the capacity of their mobile networks usingrndifferent capacity enhancing technologies. Network densification using small cells is onernof the important capacity enhancement methods that are being implemented. rnTo successfully exploit capacity benefits of network densification, efficient networkrnplanning is needed to address its deployment challenges including availability and costrnof site, energy source and backhaul while minimizing interference and maximizingrnnetwork capacity and users’ throughputs. Using street furniture including lamp postsrnand utility poles during the planning of dense/ultra-dense networks is being consideredrnas one method to address the availability of sites and cost challenges. But technoeconomicrnbenefitsrnofrnusingrnthernfurniturernandrnusagernmethodsrnarernnotrnstudiedrninrntherncontextrnrnofrnAddisrnAbaba.rnrn In this thesis work, the techno-economic benefits of using the furniture from an outdoorrnnetwork planning perspective for a selected area of Addis Ababa will be investigated.rnThe outdoor planning optimization is performed using technical and economic objectivesrnwhile considering lamp posts and utility poles of the selected areas as part of candidaternlocations for small cells. The multiobjective optimization is solved using Geneticrnalgorithm and its implementation and result analysis are performed using MATLAB. rnPropagation computation for network simulation is undertaken using a deterministicrndominant path model that is implemented within WinProp. Empirical economic analysis results show that using streetlamp posts and utility polesrndecreases deployment cost by 17% compared to using a new standalone pole (dedicatedrnpole used for only small cell deployment). Furthermore, obtained Pareto optimalrnnetworks from outdoor planning simulation that considers 143 lamp posts and 81 utilityrnpoles as candidate cells provide 53 lamp posts and 43 utility poles. Thus, only 24% isrnconsumed by street posts for 96 sites while for 13 sites using new standalone pole 57% ofrnthe total deployment cost is consumed.

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