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In recent years, wireless communication systems have exploded in popularity. Optical wireless technology is an excellent alternative to RF wireless, but it has high capacity, data speed, frequency, and license-free spectrum, and it is simple to implement. Optical wireless communication sends data through air using optical beams. Atmospheric turbulence degrades the functioning of free-breathing space photosensitive interaction methods by causing interruptions due to weather conditions like fog, smoke, and different pollutants. In this paper, free-space optical communications using snow and fog attenuations and Numerical Weather Prediction (NWP) method is used to determine the turbulence due to the atmosphere. This method improves the data transmission during unfavorable weather conditions by changing the various parameters like receiver aperture size, the wavelength of the transmitter, etc. Furthermore it shows that decrease in air quality and instability are the double significant causes, that can degrade general usage routine, especially when it’s foggy or raining heavily. The result shows that the aperture size and wavelength should be changed as per the weather condition.

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No abstract

Free-space optical (FSO) communication is an emerging technology that uses light waves to transmit data, providing a faster and more efficient alternative to traditional wired communication. However, FSO communication is susceptible to atmospheric turbulence caused by factors such as rain, snow, and fog. To overcome this challenge, this study employs artificial neural network (ANN) and long short-term memory (LSTM) models to analyze the impact of atmospheric turbulence on FSO communication. The results indicate that higher wavelengths experience less attenuation than lower wavelengths in the presence of fog. The use of ANN and LSTM models to analyze the attenuation of various wavelengths in the presence of fog has shown that higher wavelengths experience less attenuation than lower wavelengths. Additionally, the LSTM model outperforms the ANN model in handling atmospheric turbulence, with an accuracy of 64.68 % compared to 63.98 %. These findings highlight the need for adaptive networks that can quickly adjust to traffic situations while being cost-effective. As the fiber optics industry continues to expand and evolve, there is potential for further developments in optical communications that prioritize speed, efficiency, and flexibility. As technology advances, the pursuit of faster and more reliable communication will continue to drive innovation in this field.