volume | PIER Journals

Abstract

Power amplifiers in wireless communication systems can introduce nonlinear distortion, degrade signal transmission quality, and increase power consumption. The paper presents a BiLSTM-CNN-based model for modelling power amplifier behaviour to address this issue. The model uses BiLSTM layers to capture temporal information from the signal data and incorporates a multi-head attention mechanism to focus on different temporal features. Additionally, convolutional layers process global features and reduce model parameters through weight sharing. Using this model, a digital pre-distortion (DPD) model is proposed to linearise the power amplifier through an indirect learning approach. The results show that the BiLSTM-CNN model achieves a normalised mean square error (NMSE) of -40.3dB, and the DPD model enhances the adjacent channel power ratio (ACPR) of the communication system by 18dB, demonstrating the model's feasibility. Comparative analysis with other network models indicates that BiLSTM-CNN outperforms traditional methods of fitting performance and convergence speed, showcasing its superiority.

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Dr. Jingchang Nan

Dr. Shize Liu

Dr. Jiadong Yu