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Path Loss Propagation Prediction and Optimization Using Walfisch-Bertoni Model at 900 and 1800 MHz Over Macro-Cellular Western Regions of Nigeria

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 881))

Abstract

Optimization of macro-cellular network is a key factor to be considered to achieve the required service level agreement. This can be done by synchronizing the link between the base system configuration and the nature of the propagation model to be adopted. In this paper, existing path loss models were tested with the measurement survey carried out on live signals transmission on macro-cellular service provider in an outdoor propagation in western part of Nigeria. Based on the closest agreement with measurement data, the Walfisch-Bertoni (WB) is selected as the best model for signals prediction in Western Nigeria. However, the performance of a model is deemed acceptable if it provides an overall RMSE of about 6 dB. The closest fit propagation model (WB) is then optimized using a least square technique (LST) for urban microcells at 900 and 1800 MHz frequency range network in the region based on the measured data. The fitting of the measured path loss value with normal distribution function was also carried out and the results showed a mean path loss value of 126 dB and standard deviation of 16 dB for operator A at 900 MHz and a mean value of 125 dB and standard deviation of 13 dB for operator B at 1800 MHz. The optimized model and normal distribution model will be useful for the new breed technologies especially the advanced long term evolution in urban environment of Nigeria.

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Correspondence to P. Akinyemi .

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Akinyemi, P., Ojo, J.S., Abiodun, C.I., Ojo, O.L., Abiodun, O.A. (2019). Path Loss Propagation Prediction and Optimization Using Walfisch-Bertoni Model at 900 and 1800 MHz Over Macro-Cellular Western Regions of Nigeria. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-02683-7_44

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