Modeling Nonlinear Signal Components Based on Volterra Series in the Frequency Domain during Spectral Reconstruction

Abstract

The article presents a study on the application of Volterra series for modeling nonlinear signal components in the frequency domain. The proposed spectral reconstruction algorithm accounts for the impact of signal nonlinearity on its frequency-time distribution. Volterra series enable the extraction of nonlinear components in the frequency spectrum, improve the accuracy of signal reconstruction, and optimize filtering in complex radio environments. Experimental calculations demonstrated the algorithm's effectiveness in reducing mean-square error (MSE) and mean-square deviation (MSD) by up to 20,55% compared to lower-order models. The algorithm showed the ability to preserve the accuracy of signal amplitude characteristics by 10,3% better than first-order models and to ensure more precise phase reproduction with a 5,2% improvement. In dynamic radio environments, the algorithm significantly reduced the impact of inter-channel and inter-symbol interference, enhancing signal robustness. Specifically, at key time points, the second-order model reduced MSE by an average of 43,6–57,8% compared to the first-order model. The prospects for further research include the development of the algorithm for multichannel communication systems, integration of machine learning methods for dynamic parameter tuning during reconstruction, and the expansion of its application in cognitive radio networks with highly variable environments