Neural-Network DPD Modeling for PA Gain Oscillation Control

A neural network is introduced as an intermediary component between the input signal and the DPD processing chain. The neural network takes the input signal envelope and PA drain voltage as inputs, processes them through learned parameters, and outputs a correction signal that compensates for drain inductor resonance effects. This intermediary neural network model enables effective mitigation of gain oscillations and out-of-band emissions while maintaining a structured and interpretable system architecture.

The system changes the parameter of sampling rate to match the PA bandwidth rather than using excessively high rates. The neural network processes signals at this optimized sampling rate, and the model parameters (weights and biases) are trained to achieve high DPD accuracy at this reduced rate. This parameter optimization reduces computational complexity and processing requirements while maintaining the necessary DPD accuracy for effective resonance mitigation.