Behavioral Modeling of Power Amplifiers Leveraging Multi-Channel Convolutional Long Short-Term Deep Neural Network

Abstract

To improve the performance metrics of power amplifier (PA) nonlinear modeling while reducing the complexity of neural networks (NN), this paper proposes a multi-channel convolutional long short-term deep neural network (MCLDNN) approach. This method is designed to efficiently extract information from both temporal and spatial dimensions. The input to the model is a two-dimensional (2D) array, which includes envelope-dependent terms and the in-phase and quadrature (I/Q) components of both current and past signals. Additionally, this model splits the 2D array into multiple one-dimensional (1D) array. We utilize 1D and 2D convolution processes on these arrays to capture local and global patterns. Long short-term memory (LSTM) is then applied to process sequential data, enabling accurate and effective extraction of temporal correlations. The experimental evaluation of PA nonlinear behavioral modeling demonstrates that the proposed method outperforms other comparative methods in normalized mean square error (NMSE) and adjacent channel error power ratio (ACEPR).