Jueschke P, Fischer G (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: IEEE Computer Society
Pages Range: 58-60
Conference Proceedings Title: IEEE Radio and Wireless Symposium, RWS
Event location: San Antonio, TX, USA
ISBN: 9798350340457
DOI: 10.1109/RWS56914.2024.10438563
Energy efficiency and bandwidth of RF power amplifiers (PAs) is always a challenge for future radios. Static and dynamic nonlinear effects are responsible for degrading before mentioned parameters. Static effects can be characterized by measuring the PA output with a defined input signal. Fast changing dynamic nonlinearities like thermal memory and charge trapping effects with different time constants are more difficult to determine but have a strong influence on the transfer characteristic of the PA and require a dedicated and costly feedback path for fast adaption of the digital predistortion (DPD). This work shows a method to measure the trapping condition in the transistor to enhance the predistortion performance with artificial neural networks (ANN) and reduce complexity of the feedback path.
APA:
Jueschke, P., & Fischer, G. (2024). Predistortion of Charge Trapping Memory Effects in GaN based RF Power Amplifiers with Artificial Neural Networks. In IEEE Radio and Wireless Symposium, RWS (pp. 58-60). San Antonio, TX, USA: IEEE Computer Society.
MLA:
Jueschke, Patrick, and Georg Fischer. "Predistortion of Charge Trapping Memory Effects in GaN based RF Power Amplifiers with Artificial Neural Networks." Proceedings of the 2024 IEEE Radio and Wireless Symposium, RWS 2024, San Antonio, TX, USA IEEE Computer Society, 2024. 58-60.
BibTeX: Download