Nonlinear noise spectrum measurement using a probability-maintained noise power ratio method

Abstract Nonlinear distortion (noise) limits many communication systems, demanding a means of estimating system performance via device nonlinear characteristics. The noise power ratio (NPR) method which was proposed in 1971 solves this problem for systems with Gaussian stimuli or with special nonlinearity, but practical and accurate methods for many communication systems with non-Gaussian stimuli are rare. Here we propose a probability-maintained (PM) NPR method to accurately measure the spectrum of nonlinear noise via a spectrum analyzer in non-specific systems, including systems with non-Gaussian stimuli. Using an equivalent additive noise model in which the spectrum of equivalent nonlinear noise is the measurement result of PM NPRs, nonlinear system performance could be estimated with an error of 0.5 dB. Further, we find that zero-mean Chi-square noise, instead of Gaussian noise, should be selected for large memory and low-order nonlinear systems. Our method is verified in seven different scenarios with various nonlinear mechanisms and communication applications.