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Publication detail
KRÁL, J.
Original Title
Direct Learning Architecture For Digital Predistortion with Real-Valued Feedback
Type
conference paper
Language
English
Original Abstract
The power efficiency is a key parameter of modern comunication systems. Efficient nonlinear power amplifiers are linearised using digital predistorters. Conventional predistorters require two ADCs in the feedback. In this paper we have proposed a modification of the direct learning architecture using solely one ADC in the feedback and an RF mixer instead of a quadrature mixer. This allows us to minimise the system complexity and power consumtion and maximise the efficiency. The proposed architecture has been verified experimentally and compared to the conventional digital predistorters. We have shown that it can achieve same linearisation performance as the conventional architecture with two ADCs. Moreover the proposed method outperformed the conventional DPD with indirect learning architecture.
Keywords
digital predistortion, direct learning architecture, real-valued feedback
Authors
Released
26. 4. 2018
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Communication
Location
Brno, Czech Republic
ISBN
978-80-214-5614-3
Book
Proceedings of the 24th Conference STUDENT EEICT 2018
Pages from
332
Pages to
336
Pages count
5
BibTex
@inproceedings{BUT149715, author="Jan {Král}", title="Direct Learning Architecture For Digital Predistortion with Real-Valued Feedback", booktitle="Proceedings of the 24th Conference STUDENT EEICT 2018", year="2018", pages="332--336", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno, Czech Republic", isbn="978-80-214-5614-3" }