Power Scalable Neural Network Model for Wideband Digital Predistortion-Reference-Cited by-同舟云学术

Power Scalable Neural Network Model for Wideband Digital Predistortion

Published:2023-12 Issue:12 Volume:33 Page:1658-1661
ISSN:2771-9588
Container-title:IEEE Microwave and Wireless Technology Letters
language:
Short-container-title:IEEE Microw. Wireless Tech. Lett.

Author:

Li Shulan¹^ORCID,Zhao Guobo¹^ORCID,Yu Cuiping¹^ORCID,Li Feifei¹^ORCID,Liu Yuanan¹^ORCID

Affiliation:

1. Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China

Funder

Shenzhen Science and Technology Program

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9944983/10345386/10286858.pdf?arnumber=10286858

Reference11 articles.

1. Power Amplifier and Transmitter Architectures for Software Defined Radio Systems

2. Linearity and Efficiency in 5G Transmitters: New Techniques for Analyzing Efficiency, Linearity, and Linearization in a 5G Active Antenna Transmitter Context

3. Bandwidth and Power Scalable Digital Predistorter for Compensating Dynamic Distortions in RF Power Amplifiers

4. Power Adaptive Digital Predistortion for Wideband RF Power Amplifiers With Dynamic Power Transmission

5. Compensation of Long-Term Memory Effects on GaN HEMT-Based Power Amplifiers

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