IMPLEMENTATION OF MACHINE LEARNING TECHNIQUES TO PREDICT THE CHANNEL CAPACITY

Abstract: The ability to predict channel capacity in wireless communication systems is critical for optimizing network performance and ensuring efficient data transmission. This work utilizes machine learning techniques to predict channel capacity based on key environmental and network parameters, including Signal-to-Noise Ratio (SNR), bandwidth, fading coefficients, and interference. Simulated data is generated to model the relationship between these factors and channel capacity using Shannon’s theorem. A Random Forest Regressor is employed to develop a predictive model, with hyper parameter tuning carried out using Grid Search CV for optimal performance. The model's performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². Visualizations are provided to illustrate the relationship between actual and predicted values of channel capacity, as well as the feature importance ranking. The work concludes with the saving of the trained model and scaler for future use. This predictive model serves as a step toward more intelligent and adaptive wireless network management, providing insights into optimizing communication systems under varying conditions.

Keywords: ML, MAE, MSE, RMSE, EDA.