DPD 和 PA 建模
用于数字预失真和功率放大器建模的 AI
使用神经网络对功率放大器 (PA) 进行建模。为了抵消 PA 中的非线性效应，您可以使用神经网络对输入信号应用数字预失真 (DPD)。
有关工作流的描述，请参阅AI for Digital Predistortion Design (Communications Toolbox)。
主题
- AI for Digital Predistortion Design (Communications Toolbox)
Example workflows for training, compressing, and using a deep learning network for digital predistortion design. (自 R2024a 起)
- 步骤 1: Data Preparation for Neural Network Digital Predistortion Design (Communications Toolbox)
- 步骤 2: Neural Network for Digital Predistortion Design-Offline Training (Communications Toolbox)
- 步骤 3: Neural Network for Digital Predistortion Design - Online Training (Communications Toolbox)
- 步骤 4: Structurally Compress Neural Network DPD Using Projection (Communications Toolbox)
- Complex-Valued Neural Network for Digital Predistortion Design-Offline Training (Communications Toolbox)
Use a complex-valued neural network, that is trained offline, to apply digital predistortion to offset the effects of nonlinearities in a power amplifier. (自 R2026a 起)
- Power Amplifier Modeling Using Neural Networks (Communications Toolbox)
Model a power amplifier (PA) using several different neural network (NN) architectures. (自 R2024a 起)
精选示例
Compare Residual Recurrent Neural Network Structures for Digital Predistortion Design
Compare residual RNN structures for a digital predistortion (DPD) design.
