RF-power amplifier modelling using inverse system identification

System identification, the process of deriving mathematical models from empirical data, plays a crucial role in control systems with significant applications across electrical, electronics, and telecommunications domains. While linear system identification is relatively straightforward, nonlinear systems present substantial challenges. This complexity is particularly evident in inverse modeling scenarios, where research remains nascent despite its growing importance. Our study focuses on developing a system identification method for Radio Frequency (RF) power amplifiers-critical components in communication systems that exhibit nonlinear behavior and generate adjacent channel interference. Recent advancements in inverse modeling encompass various approaches, including machine learning-based pre-inverse models, specialized neural network architectures, and optimization techniques. Each methodology presents distinct trade-offs in terms of model accuracy, robustness, and computational efficiency. The widespread occurrence of inverse systems across various applications underscores the significance of this research direction and its potential impact on improving communication system performance.