The power amplifier (PA) behavior models based on deep neural networks (DNNs) have been widely used. However, it is challenging to balance the network depth, the number of neurons, and the combination of input terms. Accordingly, how to obtain a suitable DNN model becomes a problem. This letter proposes a method to acquire the corresponding optimal DNN structure balancing multiple aspects for a PA by decoupling the relationship of the three factors mentioned above. The algorithm first chooses the optimal input combinations, and then utilizes the inputs to select the DNN structure with excellent capability to fit nonlinear functions and fewer coefficients. Experimental results verify that the DNN model acquired by the algorithm proposed can maintain superior performance with low complexity.

中文翻译：

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功率放大器的最佳深度神经网络模型选择方法

基于深度神经网络（DNN）的功率放大器（PA）行为模型已被广泛使用。但是，要平衡网络深度，神经元数量以及输入项的组合具有挑战性。因此，如何获得合适的DNN模型成为问题。这封信提出了一种方法，该方法通过解耦上述三个因素之间的关系来获取平衡PA多个方面的相应最佳DNN结构。该算法首先选择最佳输入组合，然后利用输入来选择具有出色的非线性函数拟合能力和系数系数的DNN结构。实验结果证明，该算法获得的DNN模型可以保持较高的性能，且复杂度较低。