Dimensions-Reduced Volterra Digital Pre-Distortion Based On Orthogonal Basis for Band-Limited Nonlinear Opto-Electronic Components

• Published in: IEEE photonics journal 2019-02, Vol.11 (1), p.1-13
• Main Authors: Faig, Hananel, Yoffe, Yaron, Wohlgemuth, Eyal, Sadot, Dan
• Format: Article
• Language: English
• Subjects: Basis functions; Coefficients; Communication systems; Complexity theory; Computational modeling; Computer simulation; Correlation; Correlation analysis; Dependent variables; Distortion; Distribution functions; Eigenvalues; Electronic components; Estimation; Fiber optics communication systems; Mach-Zehnder interferometers; Mathematical models; Modulators; nonlinear digital pre-distortion; Nonlinear distortion; Nonlinear systems; Nonlinearity; Optical communication; Optoelectronics; orthogonal basis; Polynomials; Probability distribution functions; Random variables; Volterra series
• ISSN: 1943-0655; 1943-0647
• DOI: 10.1109/JPHOT.2019.2892400

Optical communication systems often include nonlinear components, such as Mach-Zehnder modulators (MZMs). The components' nonlinearity may have a deleterious effect on the system performance, especially when the memory effect is included. One of the most common methods to mitigate the resultant distortion effects is digital predistortion (DPD) based on the Volterra polynomial model. Typically, the DPD coefficients are extracted using the least squares approach. However, naive implementation of the Volterra polynomial model usually introduces significant complexity due to the large number of model coefficients. Here, we propose the use of orthogonal polynomial basis functions for efficient DPD implementation. The orthogonal basis enables the estimation of each coefficient separately, which provides a significant computational gain. Furthermore, the amount of dominant terms in the orthogonal basis representation is significantly lower, which leads to dramatic relaxation in the DPD implementation. In addition to the analytical modeling approach, a nonparametric method is developed by applying eigenvalue decomposition on the correlation matrix, which is required for the case of dependent random variables, or for the case of unknown probability distribution functions. MZM-based lab experiments and simulations were performed, which indicated a potential saving of 50%-80% in the amount of useful dimensions.