Efficient Training of Volterra Series-Based Pre-distortion Filter Using Neural Networks
Conference Paper
(2022)
Author(s)
V. Bajaj (TU Delft - Team Sander Wahls, Nokia Bell Labs, Stuttgart)
Mathieu Chagnon (Nokia Bell Labs, Stuttgart)
Sander Wahls (TU Delft - Team Sander Wahls)
Vahid Aref (Nokia Bell Labs, Stuttgart)
Research Group
Team Sander Wahls
DOI related publication
https://doi.org/10.1364/OFC.2022.M1H.3
To reference this document use:
https://resolver.tudelft.nl/uuid:4b610b59-15eb-4b84-bbef-fd9c3473b92b
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Publication Year
2022
Language
English
Research Group
Team Sander Wahls
ISBN (electronic)
978-1-55752-466-9
Abstract
We present a simple, efficient “direct learning” approach to train Volterra series-based pre-distortion filters using neural networks. We show its superior performance over conventional training methods using a 64-QAM 64 GBaud simulated transmitter with varying transmitter nonlinearity and noisy conditions.
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