| Summary: | Different regions are prone severely to various phenomena such as multi-path fading caused by diffractions, refractions, and reflection due to the environmental parameters and water composition. International Telecommunication Union salinity standard value which is 35 ppt is not valid in different seawater regions and the space diversity as an existing solution is quite costly to accommodate the environmental parameters in terms of material manufacturing and maintenance difficulties to compromise strong wind in desert areas and environmental corrosion added to the huge weight in installation. Gaining insight into how rain, fog, water vapor, and salinity affect signal propagation is essential for creating communication systems that can withstand challenging weather conditions. This highlights the importance of exploring strong and efficient models to characterize how these atmospheric factors influence signal transmission. Such understanding will improve the reliability and performance of communication networks, particularly in outdoors and stark environments such as the Arabian Gulf. The thesis objective is to improvise model based on environmental parameters such as rain, fog, vapor, and salinity of the Arabian Gulf in Qatar. Various models including exponential, polynomial, and power models were employed for the environmental parameters modelling in relation to the signal propagation. The study used Matlab to analyze the models and simulate the signal amplitude and phase at 1 and 10MHz in vertical and horizontal polarization under the salinity range from 10 to 90 ppt. The results revealed that the polynomial 3 and 4 models have shown the best fitting for the signal propagation of the environmental parameters of the Arabian Gulf due to their highest R-square values which were all above 96% indicating highly effective in capturing the relationship between the variables. However, the polynomial 4 hit 99% in salinity, rain, fog, and water vapor parameters while the polynomial 3 achieved above 96% in such parameters. Similarly, the polynomial 4 achieved the best fit in sum of the square error (SSE) that hit 0.03 in salinity up to 0.12 in fog, and root mean square error (RMSE) values from 0.05 in water vapor up to 0.08 in salinity, rain and fog demonstrating that the overall error in the model predictions is quite low, whereas the polynomial 3 obtained 0.05 in water vapor up to 0.1 in salinity, rain, and fog in (SSE) and 0.05 in water vapor up to 0.15 and 0.17 in salinity and rain respectively in (RMSE). The model validation demonstrated remarkable accuracy, paving the way for marine wireless design tools to respect the salinity levels.
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