Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM
Journal article, 2022

We experimentally demonstrate a novel size reduction approach for symbol-based look-up table (LUT) digital predistortion (DPD) of the transmitter impairments taking advantage of the periodicity in the pattern-dependent distortions. Compared to other reduced-size LUT schemes, the proposed method can significantly lessen the storage memory requirements with negligible performance penalty for high-order modulation formats. To further alleviate the storage memory restriction, a twice reduced-size LUT scheme is proposed to provide further size reduction. Importantly, given a targeted memory length, we verify the importance of averaging over sufficient occurrences of the patterns to obtain a well-performing LUT. Moreover, it is necessary to evaluate the performance of LUT-based DPD using random data. Finally, we demonstrate a neural network (NN) based nonlinear predistortion technique, which achieves nearly identical performance to the full-size LUT for all employed constellations and is robust against a change of modulation format. The proposed techniques are verified in a back-to-back transmission experiment of 20 Gbaud 64-QAM, 256-QAM, and 1024-QAM signals considering 3 and 5 symbol memory. The performance of the LUT-based DPD is further validated in a noise loading experiment.

signal processing

Look-up table

transmitter nonlinearity compensation

neural network

predistortion

Author

Zonglong He

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Kovendhan Vijayan

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Alexandre Graell I Amat

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Peter Andrekson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Magnus Karlsson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Jochen Schröder

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Journal of Lightwave Technology

0733-8724 (ISSN) 1558-2213 (eISSN)

Vol. 40 18 6168-6178

Unlocking the Full-dimensional Fiber Capacity

Knut and Alice Wallenberg Foundation (KAW 2018.0090), 2019-07-01 -- 2024-06-30.

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/JLT.2022.3191464

More information

Latest update

7/27/2024