Generative Adversarial Network for Predicting Measured RF Signal Strength

Accurate real-time Radio Frequency (RF) signal strength prediction is essential for optimizing wireless communication systems, enabling efficient network deployment, and supporting emerging technologies. In this paper, we propose a Generative Adversarial Network (GAN)-based approach for real-time prediction of real (or measured) RF signal strengths. In particular, we train a GAN model using both simulation and a limited number of real-world measurement data. Then, we use the GAN to predict the measured signal strength anywhere on the provided urban map. The proposed GAN model is applied to real-world urban environments and its performance is evaluated against actual measurement data. The results validate the effectiveness of the GAN-based approach in accurately predicting RF signal strength, demonstrating its potential for improving wireless network planning and optimization.