Transfer Learning with Transformer and LSTM for Digital Pre-distortion of Terahertz/mmWave Transceiver

Gouheng Zhao; Kai Ying; Qingsong Wen; Junwen Zhang; Lin Gui

ICASSP 2025accepted0 citations

Transfer Learning with Transformer and LSTM for Digital Pre-distortion of Terahertz/mmWave Transceiver

Gouheng Zhao, Kai Ying, Qingsong Wen, Junwen Zhang, Lin Gui

Abstract

To ensure high-quality communication, it’s of great value to use digital pre-distortion (DPD) to linearize the core component power amplifier (PA) of terahertz/mmWave transceiver. In this work, we propose transfer learning with Transformer and LSTM for DPD of terahertz/mmWave transceiver, which uses Transformer based PA behavioral model to train effective and lightweight LSTM model for DPD. To collect and analyze the signal data of terahertz/mmWave transceiver, we set up a physical platform for the D-band system. Through experiments, we show that the proposed method is capable of significantly reducing in-band and out-band distortion while avoiding the excessive complexity of DPD models.

BibTeX

@inproceedings{icassp2025_transferlearning,
  title = {Transfer Learning with Transformer and LSTM for Digital Pre-distortion of Terahertz/mmWave Transceiver},
  author = {Gouheng Zhao and Kai Ying and Qingsong Wen and Junwen Zhang and Lin Gui},
  booktitle = {ICASSP 2025},
  year = {2025}
}