Path Loss

Path loss, the reduction in signal strength during wireless transmission, is a critical factor in designing and optimizing communication networks. Current research focuses on improving path loss prediction accuracy using machine learning, particularly deep learning models like convolutional neural networks and transformers, often incorporating environmental data (terrain, clutter) from sources such as satellite imagery or 3D maps as input features. These advancements aim to replace computationally expensive simulations and improve the efficiency of network planning, resource allocation, and ultimately, the performance of wireless systems across various applications, including 5G networks and UAV communication. Simplified feature sets and federated learning approaches are also being explored to enhance model efficiency and generalizability.