The behavior models based on artificial neural networks (ANNs) have been widely used in the wideband power amplifier (PA). However, the selected terms of the input signal significantly affect the complexity of the ANNs. In this letter, a method using an attention-based deep neural network (DNN) is proposed to reduce the number of selected input terms for PA modeling. This method first selects the input terms with large contributions to PA modeling offline using the DNN with an attention mechanism. Then, the selected input items are injected into the DNN to build the PA model online. Experimental results show that the proposed method requiring only 1/3 of the input items can achieve good modeling performance with low complexity.

中文翻译：

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基于注意力的宽带无线功率放大器深度神经网络行为模型

基于人工神经网络（ANNs）的行为模型已广泛应用于宽带功率放大器（PA）。然而，输入信号的选定项会显着影响人工神经网络的复杂性。在这封信中，提出了一种使用基于注意力的深度神经网络 (DNN) 的方法来减少用于 PA 建模的所选输入项的数量。该方法首先使用具有注意力机制的 DNN 选择对离线 PA 建模贡献较大的输入项。然后，将选定的输入项注入 DNN 以在线构建 PA 模型。实验结果表明，所提出的方法只需要1/3的输入项就可以在低复杂度的情况下获得良好的建模性能。