Signal Map

Signal mapping involves creating representations of signal strength and distribution across geographical areas, crucial for applications like cellular network planning and resource allocation, as well as locating signal sources in diverse contexts such as search and rescue operations. Current research focuses on developing robust and efficient methods for signal map creation, employing techniques like Bayesian optimization for autonomous drone navigation, machine learning models (e.g., U-Nets, random forests) trained on synthetic or real-world data, and attention-based multiple-instance learning for integrating diverse data sources. These advancements improve prediction accuracy, reduce computational costs, and address challenges like data scarcity, privacy concerns, and the need for efficient data integration from various sources.