Behavioral Modeling of Ultra-Broadband mmWave Power Amplifier based on Augmented Long-Short Term Memory Networks

Wang, Shijie; Geng, MingQing; Yu, Chao; Cai, Jialin

doi:10.1109/iwem53379.2021.9790468

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2024

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Cited by 2 publications

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“…In Wang et al, 20 an ALSTM‐based modeling technique is proposed for mmWave PAs, under stimulation of an ultra‐wideband signal. In this article, the work in Wang et al 20 has been extended, a trade‐off between the model complexity and model performance has been analyzed and discussed, and a in‐depth model extraction procedure has been provided.…”

Section: Introductionmentioning

confidence: 99%

Improved behavioral modeling using augmented LSTM networks for ultra‐broadband mmWave PA

Wang

Geng

et al. 2022

Micro & Optical Tech Letters

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In this article, a new behavioral modeling method based on augmented long‐short term memory (ALSTM) networks for ultra‐broadband millimeter‐wave power amplifiers (mmWave PAs), is proposed. The basic theory and modeling procedure of this technique are provided. Different optimization cores of the models are tested, and the optimal algorithm is chosen. Tradeoffs have been made to fix the optimal topology for the proposed modeling technique. To validate the proposed method, a 4‐carrier 320 MHz modulated signal was employed to excite a mmWave PA with the center frequency located at 41 GHz. Experimental results show that the proposed ALSTM model has better predictive capability when compared with the traditional and other existing machine learning modeling techniques.

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Section: Introductionmentioning

confidence: 99%