Dimensions-Reduced Volterra-Based Digital Pre-Distortion for Band-Limited Nonlinear Components

Faig, Hananel; Yoffe, Yaron; Sadot, Dan

doi:10.1364/sppcom.2018.spm3g.2

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“…In this work, the use of orthogonal polynomial basis functions for estimating nonlinear components with memory is proposed and extensively analyzed, following the preliminary publication of this method [15]- [16]. Significant advantages are outlined and discussed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%