## Materials and Methods
### Artificial Neural Network Learning Model (ANN-LM)  
The paper employs ANN-LMs to improve modelling outcomes. ANN-LMs are potent AI models capable of learning from data, recognising patterns, and making accurate predictions with minimal human intervention if adapted and described appropriately. The iterative nature of ANN-LMs enables them to adapt to newly introduced data. A robust neural network model with multiple layers that exceed the number of layers in conventional neural networks is utilised in this study. In the context of ANN, use layers to model particular patterns, such as basic edges or object parts, which are then passed to subsequent layers. The configuration of radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models can be provided. To address data issues, preprocessing techniques were employed, and the data were separated into training and testing sets. The choice of ANN-LM structure and network validation training took data classification and measurement into account. The fifth phase involved a method based on selection, which resulted in the effective creation of a network model with high assessment precision. Figure 6 depicts the use of RBFNN and GRNN ANN-LMs, both of which are endowed with pruning capabilities, to predict the output of the system. In addition, a model-based predictive optimisation strategy was used to calculate the protection distance between 5G-BS and FSS-ES techniques. As shown in Figure 6, six input features were used for model predictions in this paper. These inputs include specific data points associated with 30-degree, 20-degree, and 15-degree angles. As a safeguard against interference, the interference-to-noise ratio (INR) was also included. In addition, pertinent 5G and fixed-satellite services (FSS) parameters were evaluated. These parameters collectively serve as the basis for our prediction models, facilitating accurate predictions based on the supplied data and parameters. Initially, we chose the radial basis function neural network (RBFNN) and the generalized regression neural network (GRNN) as our machine learning algorithms for assessing downlink interference in the context of 5G and the FSS Earth station (FSS-ES). These models were chosen due to their demonstrated efficacy in dealing with nonlinear relationships and their successful application in telecommunications and interference analysis. In addition, RBFNN and GRNN provide efficient training and prediction procedures, which are essential to our research. Regarding simulation parameters, we took industry standards and previous research into cautious consideration. In our simulation, a heterogeneous network consisting of multiple 5G base stations and FSS Earth stations was deployed in an urban setting. To assure realistic simulations, the number and placement of these stations were determined based on actual deployment scenarios. In addition, we included various channel characteristics, such as path loss, fading, and shadowing, based on empirical models and measurements specific to the investigated frequency range. Figure 6: ANN-LM schematic. This allowed us to mimic the wireless propagation environment accurately and capture realistic interference scenarios. The significant impact it has on propagation characteristics, interference levels, and the difficulties of coexistence between 5G and FSS-ES systems influenced the choice of the frequency range for our study. The sub-6 GHz frequency band was the primary focus of our research. By examining the frequency range where potential interference issues between 5G and FSS-ES may arise, our research seeks to provide insights and solutions for interference management in these systems. Traditional techniques include frequency planning, power management, and antenna design. Support vector machines (SVM), interference cancellation, accuracy is frequently employed machine learning algorithms in this context. Within the domain of artificial neural network language models (ANN-LMs), RBFNN and GRNN were evaluated.