An Intelligent Approach for the Prediction of Load-Pull Contours with Nonlinear Behavior Models

In this paper, we introduce an intelligent method for predicting load-pull contours using an advanced Generative Adversarial Network (GAN) with limited measurement data. The approach incorporates an artificial nonlinear behavior model to generate training data, enabling accurate prediction of load-pull contours. Experimental validation on a GaAs device at 10 GHz demonstrates that this method successfully captures the complex nonlinear behavior of the device while utilizing less than 10% of the original measurement data, proving its efficiency in expanding load-pull measurement capabilities for next-generation wireless communication technologies.