Browsing by Author "Ometan, O. O."

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Global Transmission Margins Determination and Cloud Attenuation Models at Satellite Bands
(Scientific Research, New Technologies and Applications Vol. 7, 2024) Adewusi, O. M.; Ometan, O. O.; Akinwumi, S. A.; Omotosho, T. V.; Akinyemi, M. I.
Considering the fact that various dimensions of disturbances occur in the atmospheric layers randomly, which often have consequential footprints on the troposphere where mankind naturally lives, there is an obvious need for periodic determination of effective transmission margins consistently for most geographic locations on Earth. The need for the achievement of an effective wireless transmission margin and larger bandwidth at a relatively lower cost precipitates the importance of hydrometeor models’ roles in satellite transmissions. Also, the almost perpetual existence of clouds in tropical climates makes cloud models all the more fundamental. Details of four years of station spectrum analyzer data, five years of climatological data and fifty – eight years of radiosonde data used in this research at tropical test station - Ota, southwest Nigeria - were earlier published. At the station, total cloud attenuation data were measured and logged every minute using the spectrum analyzer. The large data was used to deduce the station cloud attenuation cumulative distribution, which was used to derive the station's new cloud attenuation algorithm. This cumulative distribution was compared with the station cloud attenuation cumulative distribution of each of the other existing cloud models. The radiosonde data was used to derive each existing cloud model’s predicted cloud attenuation cumulative distribution respectively for the tropical station. These sets of distributions were used to deduce the station's new cloud attenuation algorithm’s parameters through a written and published simulation program, which defined the cloud attenuation model for the station. Thus, the generation of any new station cloud attenuation model only fundamentally requires the station’s radiosonde data. The integrity of the radiosonde data renders cloud cover data and all others for a station only for graphical comparisons and corroboration. Thus, the new cloud attenuation algorithm can be used to develop the cloud attenuation model for any geographic location by using the methodology reviewed above and whose details were earlier published. Subsequently, the collected spectrum analyzer data, climatological data and acquired radiosonde data were used to compute projected attenuation values for each cloud attenuation model at propagation signal frequencies between 12 GHz to 50 GHz. The predicted values were extracted and analysed statistically. Spectrally, the station's new cloud attenuation model’s cumulative distribution proportionally averaged the other model’s characteristic cumulative distributions as shown by the graphical figures. The results show that convergence of the range of predicted attenuation values by each of the cloud models increases directly with frequency. Hence, global hydrometeor transmission margins for any set of locations can be determined through the explained method, at an effective frequency.
Machine Learning Projection in Performance Evaluation of Cloud Attenuation Prediction Models for Satellite Transmission Quality Improvement
(2024) Adewusi, M. O.; Ometan, O. O.; Akinwumi, O. A.; Omotosho, V. T.; Akinyemi, M. L.
Artificial satellite applications to information transmission remain of great importance now and in the foreseeable future. While machine learning is breaking research achievement records for good, the increase of political influence on scientific potentials needs to be managed cohesively by all for sustainability. The reliability of social and business interactions on communication infrastructure determines the technological advancement of every nation – developed or still underdeveloped. In the disclaimer notices of most financial institutions' transaction forms and mandatory customer business agreements, they declared that they are not liable for communication channel malfunction that may lead to transaction interruption, transmission blackout, and subsequent delay in their services. These prescribe effective hydrometeors attenuation margins determination periodically, from more accurate models – such as machine trained ones, to guarantee an increase in reliability of signal transmissions for every geographic location globally. Earlier research works established that required increases in transmission frequency for better efficiency are directly proportional to consequent hydrometeor attenuation on the signal, and that satellite communication unavailability in most tropical regions is above the allowed 1% outage percentage, significantly due to cloud attenuation contribution at satellite bands - which have been increasing consistently. The existence of clouds in tropical climates is almost perpetual, making cloud models all the more fundamental in tropical regions – which include Africa and not less than half of the rest of the world. The published new tropical cloud attenuation algorithm and its resulting new tropical cloud attenuation model (NTM) - derived from it, are hereby further analysed with respect to a wider frequency range. In the primary research of this work, data were collected from a spectrum analyzer, weather-link, and radiosonde equipment. The data were used to calculate values of cloud attenuation by each major existing cloud model in the signal propagation range of 12 to 50 GHz. The predicted cloud attenuation values were spectrally processed and analysed, resulting in the observation that the NTM’s predictions generally average the characteristics prediction values of existing models as shown by presented graphical outputs, though its differences in values relative to each of the other models are substantial in most cases, as either an increase or a reduction. Also, the predicted attenuation values by each of the cloud models converge increasingly direction-wise with frequency. The stated periodicity requirement above in these regards needs a machine learning approach to at least increase the periodicity of the result’s integrity and reliability by several tens of years, for every geographic location globally.