Review on Artificial Intelligence Application for Enhancing Path Loss for Resource Management in 5G Network

Abstract

This study explores the application of Artificial Intelligence (AI) techniques like machine learning, deep learning and reinforcement learning for enhancing path loss prediction accuracy for network resource management in 5G networks. Through a comprehensive literature review and conceptual analysis, the study highlights the strengths and limitations of various AI models such as Support Vector Regression (SVR), Random Forest (RF), Convolutional Neural Networks (CNN), and hybrid models combining techniques like Principal Component Analysis (PCA) and Gaussian Processes (GP). These models demonstrate improved performance in learning nonlinear propagation patterns, adapting to environmental variability, and optimizing network design parameters such as base station placement and interference mitigation. Ultimately, this research underscores the transformative potential of AI-driven methods in revolutionizing path loss prediction and network optimization in 5G and future wireless communication systems, while also identifying key challenges and directions for future research. Finally, the work recommends that future studies should aim at developing lightweight and interpretable AI models, incorporating transfer learning to overcome data scarcity and expanding the range of training datasets to improve model robustness.