DPD and PA Modeling
Model a power amplifier (PA) using a neural network. To offset the effects of nonlinearities in a PA, you can use a neural network to apply digital predistortion (DPD) to the input signal.
For a description of the workflow, see AI for Digital Predistortion Design (Communications Toolbox).
Topics
- AI for Digital Predistortion Design (Communications Toolbox)
Example workflows for training, compressing, and using a deep learning network for digital predistortion design. (Since R2024a)
- STEP 1: Data Preparation for Neural Network Digital Predistortion Design (Communications Toolbox)
- STEP 2: Neural Network for Digital Predistortion Design-Offline Training (Communications Toolbox)
- STEP 3: Neural Network for Digital Predistortion Design - Online Training (Communications Toolbox)
- STEP 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. (Since R2026a)
- Power Amplifier Modeling Using Neural Networks (Communications Toolbox)
Model a power amplifier (PA) using several different neural network (NN) architectures. (Since R2024a)
Featured Examples
Compare Residual Recurrent Neural Network Structures for Digital Predistortion Design
Compare residual RNN structures for a digital predistortion (DPD) design.
