Analysis of Land Mobile Spectrum Occupancy using Spatial Interpolation Algorithms: For the Case of Addis Ababa, Ethiopia

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Addis Ababa University


An increase in the development of information industry and wireless communication services shows an increase demand for spectrum resource. Spectrum is a limited resource that needs to be managed properly to enable additional new services, without spectrum, none of todays’ wireless communication will be true. The spectrum occupancy map can be a powerful tool for developing a better knowledge on the occupancy status of this scares resource. Traditionally, spectrum measurement is based on drive test, which conveys geographically measuring different spectrum bands with moveable vehicles equipped with mobile spectrum measurement capability. Drive test cannot be conducted in all regions due to constraints such as, buildings, road conditions, and inefficient measurement tools. Therefore, the drive test is inefficient to get information on spectrum occupancy status and it cannot offer a complete and reliable occupancy information. In this thesis, the performance of three spatial interpolation methods, namely Inverse Distance Weight (IDW), Ordinary Kriging (OK) and Natural Neighbor (NN) are evaluated to select which one is best method to develop spectrum occupancy map. The experimental analysis is performed on sample data, collected from TCI 700-series spectrum measurement unit, in the range of 137MHz-174MHz of VHF bands and 400MHz-470MHz of UHF bands for land mobiles in selected area of Addis Ababa. The collected sample data is given for K-means clustering algorithm to classify the occupancy status at different locations. The three interpolation methods are employed for only UHF bands. The prediction performance of the three algorithms is evaluated through cross validation and Root Mean Square Error (RMSE). The obtained results showed that, OK with Gaussian model of semivariogram estimate with a prediction error of 0.807, while IDW and NN estimate values 2.801 and 2.119 respectively. This show OK is more accurate than IDW and NN.



Spatial Interpolation, Spectrum, Kriging, Inverse Distance Weight, Natural Neighbor, Land Mobile