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Li, R., Yao, Z., Wang, Y., Lin, Y., Ohtsuki, T., Gui, G., et al. (2025) Behavioral Modeling of Power Amplifiers Leveraging Multi-Channel Convolutional Long Short-Term Deep Neural Network. IEEE Transactions on Vehicular Technology, 74, 11456-11460.
https://doi.org/10.1109/tvt.2025.3543885

has been cited by the following article:

• TITLE: Saleh Assisted Deep Neural Network Behavioral Model of Radio Frequency Power Amplifiers

  AUTHORS: Zhe Jin

  KEYWORDS: Behavioral Model, Radio Frequency Power Amplifier, Deep Neural Network, Saleh Model

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.13 No.10, October 15, 2025

  ABSTRACT: In modern broadband communication systems, radio frequency power amplifiers suffer from severe nonlinear distortion and memory effects. To accurately analyze the impact of their characteristics on communication systems, it is necessary to establish precise amplifier models. In recent years, deep neural networks have been widely applied to power amplifier modeling. In order to describe the complex dynamic characteristics of power amplifiers, the deep neural network often requires a large number of hidden layers, which leads to large model scale and difficulties in training. To address this issue, a novel behavioral model is proposed in this paper. It combines the classical Saleh model with a deep feedforward neural network. Since the Saleh model can be identified using the least squares method without requiring iterative gradient algorithms, the computational load of model training is significantly reduced. Simulation results demonstrate that, compared to deep neural network models with equivalent parameter quantities, the proposed model exhibits higher accuracy and faster convergence.