On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

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Sulainman Adeniyi Adekola, Professor
Ayotunde Abimbola Ayorinde, Dr.
Hisham Abubakar Muhammed, Engr.
Francis Olutunji Okewole, Engr.
Ike Mowete, Professor


A computationally inexpensive, analytically simple, and remarkably efficient rain attenuation prediction algorithm is presented in this paper. The algorithm, here referred to as the Quasi-Moment-Method (QMM), has only two main requirements for its implementation. First, rain attenuation measurement data (terrestrial or slant path) for the site of interest must be available; and second, a model, known to have predicted attenuation for any site to a reasonable level of accuracy (base model), and whose analytical format can be expressed as a linear combination of its parameters, is also required. An important novelty introduced by the QMM algorithm is a normalization scheme, through which a modelling difficulty concerning exceedance probabilities outside a 0,01 – to -1 range, is eliminated. Model validation and performance evaluation using a comprehensive set of data available from the literature clearly demonstrated that the QMM models consistently improved base model performance by more than 90%; and outperformed all published ‘best fit’ models with which they were compared.

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How to Cite
Adekola, S. A., Ayorinde, A. A., Muhammed, H. A., Okewole, F. O., & Mowete, I. (2023). On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm. Journal of Communication and Information Systems, 38(1). https://doi.org/10.14209/jcis.2023.22
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