S. S. Krishna Chaitanya Bulusu et al., "Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096989
Machine learning-aided piece-wise modeling technique of power amplifier for digital predistortion
|Author:||Bulusu, S. S. Krishna Chaitanya1; Tervo, Nuutti1; Susarla, Praneeth2;|
1Centre for Wireless Communications (CWC), University of Oulu, Oulu, Finland
2Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland
3Department of Mathematical Sciences (DMS), University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023070690326
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-07-06
We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
|Pages:||1 - 5|
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE International Conference on Acoustics, Speech and Signal Processing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This is work was supported in part by the Academy of Finland projects 6Genesis Flagship (grant no 346208) and Profi5 (HiDyn) (grant no 326291).
|Academy of Finland Grant Number:||
346208 (Academy of Finland Funding decision)
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