## 3 Results
### ANN-LM Optimisation Performance Modelling Results  
This section demonstrates the effect of interfering signals on the distance between 5G-BS and FSS-ES in detail. The network algorithm, training function, transfer function, number of hidden layers, number of neurons in the hidden layer and epochs, dataset separation, and performance combinations are examined through ANN-LM optimisation performance modelling. ANN-LM algorithms are tested for overfitting by comparing the performance of the training and testing algorithms. Figure 25a-d illustrate the effectiveness of ANN-LM optimisation performance modelling. As shown in Figure 25a, the performance is similar regardless of the protection distance. This finding is interesting because it makes the performance independent of the protection distance. Figure 25a shows the training process for RBFNN and GRNN algorithms, which mostly reach the design goal of zero error. The significant training performance is approximately 4.21774 \(\times\) 10\({}^{-18}\) for RBFNN and GRNN, with 100 training epochs for a protection distance of 10 km. The models trained using RBF and GRNN algorithms mostly achieve the design goal error of zero with training epochs of 100, and the significant training performance is approximately 2.81559 \(\times\) 10\({}^{-17}\) for 20 km. The models trained using the RBFNN and GRNN algorithms achieve a near-zero design goal error with 100 training epochs, and the significant training performance is approximately 3.61909 \(\times\) 10\({}^{-18}\) for 35 km. The RBFNN and GRNN models are trained symmetrically until reaching 100 epochs of the same data. The training results of ANN-LM optimisation performance at different protection distances are shown in Figure 25b. Training, testing, and validation plotlines were all noticeable. The training, validation, testing, and best performance are represented by a blue curve, a green curve, a red curve, and a dotted line, respectively. The number of training epochs and the mean squared error (MSE) are plotted on the x and y axes, respectively. Epoch 18 appeared when the validation performance dipped below acceptable levels, and the training was stopped. The error histogram plot is shown in Figure 25c. The error histogram plot shows the difference between the critical and predicted values. The regression coefficient R measures the correlation between the outputs and the goals. As shown in Figure 25d, the regression coefficient between the outputs and targets measures how well the targets explain the variation in the outputs. A regression coefficient of R = 1 was found during data training, implying that the training was perfect. A statistically significant difference was observed between the regression coefficients of 0.99997 and 0.99998 for data validation and testing, respectively. Hidden neurons in the network had a correlation coefficient (R) of 38 epochs between the expected output and the target it provides. A correlation coefficient of R = 1 was observed, indicating that the present model perfectly reproduced the expression levels of interference in the protection distance between 5G-BS and FSS-ES. The intention during data training and testing is to maintain the average MSE at as low a level as possible. Epoch neurons range from 0 to 100, and the MSE outputs were calculated. The maximum and average errors are evaluated on the basis of the neuron output values, as shown in Table 11.