Performance investigation of Ro-FSO link under clear and fog conditions employing machine learning

This work focuses on evaluating the performance of a radio over free space optical (Ro-FSO) communication system using quadrature amplitude modulation-based orthogonal frequency division multiplexing (QAM-OFDM). Quality of transmission of the Ro-FSO link has been analysed considering January to December months of 8 years of specific region under fog weather conditions. Signal to noise ratio (SNR) values have been investigated as a function of input power for different values of noise figures considering 32-QAM and 64-QAM modulation formats. Performance of the system has also been estimated by employing artificial neural network (ANN), k-nearest neighbour (KNN), and decision tree (DT) algorithms with determination coefficient (R-squared) and root mean square error (RMSE) as evaluation metrics. The ANN exhibits the best model performance when taking RMSE value into account. The model also exhibits higher R² values of training and testing as 0.9854 and 0.9823 respectively.