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This paper introduces a GAN-based model to predict real-world RF signal strength by training on a combination of simulated and limited measured data. Accurate RF signal prediction is crucial for optimizing wireless communication and network deployment. The results demonstrate the GAN's ability to accurately predict RF signal strength in urban environments, validated against real-world measurements.
GANs can accurately predict real-world RF signal strength using limited real-world data, enabling better wireless network planning.
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.
