Comparison of Cellular Network Coverage Prediction Using Machine Learning Algorithms and Conventional Models

Machine learning has emerged as a powerful tool for predicting signal range in wireless communication networks, offering significant improvements over conventional models. Traditional methods, such as empirical path loss models, rely on predetermined formulas and parameters that may not fully capture the complex variability of real-world environments. In contrast, machine learning algorithms leverage large datasets to learn intricate patterns and dependencies within the data, resulting in more accurate and robust predictions. Moreover, machine learning demonstrates better adaptability compared to traditional methods because it does not depend on fixed formulas, allowing it to adjust more effectively to diverse and changing conditions, especially in complex urban environments where signal propagation is affected by numerous variables. This advancement not only improves network planning and optimization but also ensures higher service quality for end-users. Therefore, incorporating machine learning into signal coverage prediction marks substantial progress in creating more efficient and dependable wireless communication networks.