Prediction of Radio Wave Attenuation due to Clouds by using Support Vector Machine Model

Wider radio spectrum bandwidths are becoming more and more necessary as a result of recent developments in wireless technology across many wireless business sectors. The growing market for faster data speeds and more mobility, which is partly due to the growing popularity of the internet, is driving this need. As a result, there is now increased competition for the radio spectrum. Demand for spectrum has increased even more as a result of regulatory measures put in place by authorities to safeguard the increasing number of spectrum users. To meet the problems posed by this impending spectrum catastrophe, experts are hard at work creating a system. Unlicensed devices may find it easier to get free radio channels, according to certain regulatory agencies. In order to forecast the attenuation brought on by tropospheric phenomena—such as clouds, dust, hail, and gases—especially at frequencies higher than 10 GHz, this article uses a machine learning technique. Accuracy has increased for the Support Vector Machine (SVM) model, which was trained at an Indian facility using real-time data from the AMSER2 satellite. An analysis of the findings via comparison will be included in the results section.