Augmented-LSTM and 1D-CNN-LSTM Models for Linearization of Wideband Power Amplifiers-Reference-Cited by-同舟云学术

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Augmented-LSTM and 1D-CNN-LSTM Models for Linearization of Wideband Power Amplifiers

Published:2023-09-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
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Author:

Rathnayake Ambagahawela¹,Silva Lesthuruge¹,Rezaei Hossein¹,Rajatheva Nandana¹

Affiliation:

1. Univeristy of Oulu,Centre for Wireless Communications,Oulu,Finland

Funder

Academy of Finland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10293227/10293746/10294019.pdf?arnumber=10294019

Reference19 articles.

1. LSTM-CNN for Behavioral Modeling and Predistortion of 5G Power Amplifiers

2. LSTM-Deep Neural Networks based Predistortion Linearizer for High Power Amplifiers

3. A Modular 1D-CNN Architecture for Real-time Digital Pre-distortion

4. A Memorized Recurrent Neural Network Design for Wide Bandwidth PA Linearization

5. Deep Neural Network-Based Digital Predistorter for Doherty Power Amplifiers

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Growing NAS for Complex-Valued CNNs in Digital Predistortion Power Amplifier Applications;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Augmented Envelope Neural Networks on RF-SoC for Digital Self-Interference Cancellation;IEEE Access;2024

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