Enhancing Receiver Linearity Through Behavioral Modeling of Measured Data and Digital Postcompensation Techniques

The challenge of receiver nonlinearity in radio observation systems results in diminished system linearity, dynamic range, and sensitivity, particularly in low-amplitude and linear regions. To address these issues, this article proposes a digital postcompensation calibration method based on a nonlinear model of radio receivers. By using nonlinear digital postcompensation technology, we analyze the nonlinear effects exhibited by radio receivers. A nonlinear model of radio receivers is constructed using empirical data. The compensation-linearized output is generated by compensating for the observed input of the radio observation system’s output response through the inverse model of the nonlinear model. Subsequently, the receiver response compensates for the difference between the actual radio receiver response and the response corresponding to linear gain, thereby eliminating nonlinear distortion and achieving real-time digital postcompensation linearization of these effects. We introduce a method for modeling the nonlinear behavior of radio receivers based on empirical data, along with a nonlinear postcompensation linearization approach rooted in this model. Experimental validation via radio observation systems reveals a mean square error of approximately 0.8226% between the model output and the system response. Furthermore, the nonlinear postcompensation linearization method, which is based on the nonlinear model of radio receivers, significantly enhances system linearity, particularly in the low-amplitude region and nonlinear region. The amplitude response difference of the system, concerning the quiet sun and cold space, shifts from approximately 5 dB to approximately 11 dB, thereby amplifying the sensitivity of the solar radio observation system.