Fog Attenuation Model Validation in the Tropical Climate Using Machine Learning

Abstract

The world is advancing and the communication devices become more powerful and extensively adopted, the need for high data rate is increasing. This has led to spectrum scarcity in Radio Frequency (RF) systems. Consequently, it is necessary to explore other technical means to overcome the raising demand for higher data rates. Free Space Optical Communication (FSOC) offers one of such means. FSOC systems have the potential to offer much higher bandwidth than R F systems. However, a major technical challenge in FSOC system is that, their performance is limited by atmospheric impairments such as: absorption, scattering and turbulence caused by rain, cloud, snow, wind, dust, aerosol and fog. Many researches were carried out with the aim to improving the system through mathematical model and system development. Most of these researches were conducted in Europe, America and Asian countries. This research is aimed at addressing the challenges due to fog in a tropical climate, through mathematical modeling and validation. The new and improved models were developed, optimized and simulated. The results obtained were compared with that of the existing models and observed that, the developed model best fitted the measured result and also achieved lower attenuation.