## 5 Results and Discussion
Figure 11a shows the scatter plot of the SNR range for different geographical regions. It may be observed from the figure that the SNR value of the New Delhi region falls off linearly by \(-18\) dB with an increase in the propagation range from 200 to 2000 m. SNR values above 38 dB have been reported for the other three regions over the observed propagation range. Minimum and maximum SNRs have been obtained in the case of the Delhi and Washington regions, respectively. Figure 11b depicts the received power as a function of the transmission range for the four regions. It may be observed from the figure that received power decreases linearly by 20.79 dBm in the case of the Delhi region with an increase in the propagation range from 200 to 2000 m. A minimum received power of about \(-45\) dB has been obtained for the observed transmission range in the case of the other three regions due to the effect of multi-impairments. Further, different ML techniques (RF, KNN, MLP, GB) have been employed to assess the performance of the DP-QPSK-based FSO link using the Jupiter notebook in Python 3.0. The transmission range, wavelength, average attenuation, LG modes, and SNR are the input features and modeling target of the ML models, respectively. Initially, the entire sets of observations have been split into two subsets. Different models have been trained using the first subset and tested using the second data subset. Training and testing data sets are divided into fractions of 80% and 20%, respectively. The models develop relationships by  
Figure 10: SNR as a function of the transmission range: (**a**) Washington region; (**b**) London region. learning from the features and target values of the training data and predict the SNR value for unseen data. Scaling has been performed to pre-optimize the features of the data set to similar ranges. In RF, multiple decision trees have been developed and further averaged for predictions of the final outputs. It may reduce bias due to ensemble of learning and averaging errors of individual trees. The number of random variables chosen at each stage of splits for a single decision tree is set to 42. A total of 100 trees have been considered as one of the other hyper parameter of the RF algorithm. In the KNN algorithm, an optimal K value of 5 has been chosen to predict the class of new data points by selecting the K closest neighbors based on the distance between points in the training set and the new point. To assign the predicted class based on the majority class, the majority class of K neighbors has been identified. MLP incorporates input, hidden, and output neurons to improve the input-output mapping capabilities. Two hidden layers with 100 and 50 neurons each have been used in our work. The MLP algorithm updates the model's weights using an optimization and back propagation technique. Stochastic gradient descent (SGD) has been used to minimize the loss function in the MLP model. An ensemble of weak learners is trained successively in the GB method, with each weak learner attempting to fix the failings  
Figure 11: Quality of the received signal for different geographic regions: (**a**) SNR; (**b**) Received power. of the preceding learners. Fewer repetitions enable GB to converge rapidly and the model performs well on both training and test data to give low bias. The objective is to estimate the performance of the ML models in terms of SNR for different input feature values. Table 8 depicts the performance metrics employed to analyze the performance of RF, KNN, MLP, and GB ML techniques. The MSE, mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R squared) have been determined. The GB technique has the ability to handle over fitting and noise and works well with the small dataset. The higher R squared (0.99) and lower MSE (0.11), MAE (0.25), and RMSE (0.33) values have been reported in the case of the GB algorithm, depicting a close relationship between training and testing data sets.