RF Coverage Map Synthesis Using Semantic GANs

The system performs preliminary actions by training a GAN model offline using historical RF data and semantic maps. Once trained, the model can rapidly generate RF coverage maps for new scenarios without requiring actual field measurements, thus saving time while maintaining accuracy through the model's learned patterns of signal propagation.

The invention creates synthetic copies of RF coverage maps using the trained GAN model. Instead of physically measuring every location, the system generates realistic RF map copies that replicate the characteristics of actual measurements, significantly reducing time consumption while preserving measurement accuracy through the model's training on real data distributions.

The GAN model serves as an intermediary between semantic maps (building layouts, materials) and RF coverage maps. This intermediary learns the complex relationship between environment and signal propagation during training, allowing the system to generate accurate RF maps from simple semantic inputs without requiring complex measurement equipment or procedures.

The invention replaces the mechanical system of physical site surveying (engineers carrying equipment, manually measuring at reference points) with a computational system. The GAN model performs the measurement function through algorithms, substituting physical measurement devices and human operators with automated computational processes that reduce operational complexity.

The system performs preliminary training of the GAN model once using comprehensive RF data, after which it can rapidly generate coverage maps for multiple scenarios. This preliminary action creates a reusable asset that maintains high accuracy while dramatically improving productivity for subsequent map generation tasks across different environments and configurations.

The trained GAN model can rapidly generate multiple copies of RF coverage maps for different scenarios, building configurations, and network setups. This copying capability maintains the accuracy characteristics of the training data while enabling high-volume map generation, thus improving productivity without sacrificing coverage map accuracy.

The GAN model achieves universality by being trained on diverse RF data from multiple scenarios, building types, and environmental conditions. Once trained, it can generalize to new scenarios and configurations that were not explicitly seen during training, providing reliable predictions across varied situations without requiring re-measurement, thus improving both reliability and adaptability.

The system adapts to new scenarios by changing input parameters such as building geometry, material properties, frequency bands, and antenna configurations. The GAN model processes these varying parameters and generates appropriate RF coverage maps, maintaining measurement reliability through its learned understanding of signal propagation physics while achieving versatility across different operational conditions.