Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity

Zonglong He, Jinxiang Song, Christian Häger, Kovendhan Vijayan, Peter A. Andrekson, Magnus Karlsson, Alexandre Graell i Amat, Henk Wymeersch, Jochen Schröder

Abstract: We experimentally demonstrate a symbol-based nonlinear digital predistortion (DPD) technique utilizing supervised learning, which is robust against a change of modulation format. Back-to-back transmission of 30 Gbaud 32, 64 and 256QAM confirms that our scheme significantly outperforms the baseline of arcsine-based predistortion.