Digital predistortion is widely used to compensate the nonlinear distortion of power amplifiers (PAs). Among the digital predistortion methods, the polynomial or deep neural networks (DNNs) models are only adopted with one specific state. When the operating conditions of PAs change, it is necessary to retrain and update the coefficients of the PA model. The generalization ability of the DNN models cannot be presented. To address this issue, this letter proposes one new modeling method that can build one generalized PA model with multiple states based on DNN. This method embeds a set of coding vectors representing corresponding states to build the generalized model. Compared with the traditional DNN model, experimental results show that the proposed method can construct the PA model containing multiple states while ensuring good modeling performance.

中文翻译：

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基于深度神经网络的功率放大器多状态行为模型

数字预失真广泛用于补偿功率放大器 (PA) 的非线性失真。在数字预失真方法中，多项式或深度神经网络 (DNN) 模型仅适用于一种特定状态。当 PA 的运行条件发生变化时，需要重新训练和更新 PA 模型的系数。DNN模型的泛化能力无法呈现。为了解决这个问题，这封信提出了一种新的建模方法，可以基于 DNN 构建一个具有多个状态的广义 PA 模型。该方法嵌入一组表示相应状态的编码向量来构建广义模型。与传统的 DNN 模型相比，