Transfer Learning

A neural network predistorter is first trained extensively on a source PA using abundant simulated or measured data. This phase captures universal nonlinear behaviors—such as AM/AM compression and AM/PM conversion—that are common across amplifier classes. The resulting model serves as a feature-rich initialization point rather than starting from random weights.

The pretrained model is adapted to the target PA by continuing training on a limited set of measurements. Only the final layers may be updated (partial fine-tuning), or the entire network can be adjusted with a very low learning rate. This process captures device-specific memory effects and manufacturing variances without requiring the exhaustive characterization a full training cycle demands.

Early layers of the neural network learn generalizable signal transformations—such as envelope detection and memory tap weighting—that apply across different PAs. Transfer learning exploits this hierarchical feature reuse:

• Layer 1-2: Universal I/Q preprocessing and envelope extraction
• Layer 3-4: PA-class-specific nonlinear basis functions
• Layer 5+: Device-specific fine distortion shaping

When the source and target PA characteristics diverge significantly, advanced domain adaptation methods align the feature distributions:

• Maximum Mean Discrepancy (MMD) loss minimizes the statistical distance between source and target hidden representations
• Adversarial domain confusion trains the network to produce features indistinguishable between PAs
• Correlation alignment matches second-order statistics of layer activations

Transfer learning enables practical deployment scenarios where a model trained at one carrier frequency or on one device specimen generalizes to others:

• Cross-frequency: A 2.6 GHz model adapts to 3.5 GHz operation with minimal retraining
• Cross-device: A model from PA unit #1 transfers to unit #2, compensating for fabrication tolerances
• Cross-signal: Adaptation from LTE to 5G NR waveforms preserves linearization efficacy

During fine-tuning, the network risks overwriting useful pretrained knowledge. Mitigation strategies include:

• Elastic Weight Consolidation (EWC) penalizes changes to parameters important for the source task
• Learning without Forgetting (LwF) uses the source model's outputs as soft targets during target training
• Progressive neural networks freeze pretrained columns and add lateral connections to new adapter layers

A modeling failure where the neural network predistorter memorizes the training data's noise and specific signal characteristics rather than learning the true underlying PA nonlinearity, degrading performance on new signals. Overfitting is the antithesis of transfer learning—a source model that overfits to one amplifier's idiosyncrasies will catastrophically fail when fine-tuned on a target PA. Indicators include:

• Low training error but high validation error
• Sensitivity to slight changes in signal statistics
• Poor Adjacent Channel Leakage Ratio (ACLR) on unseen waveforms Mitigation strategies include early stopping, L2 weight regularization, and batch normalization.

The process of adapting a pre-trained source model to a target power amplifier by continuing training on a small dataset from the new device. Unlike training from scratch, fine-tuning initializes the network with weights that already encode general PA physics, dramatically reducing the required measurement time and computational cost. Common strategies include:

• Layer freezing: keeping early layers fixed while updating only later layers
• Differential learning rates: using lower rates for transferred layers, higher for new layers
• Progressive unfreezing: gradually unfreezing layers to avoid catastrophic forgetting Typical fine-tuning achieves equivalent linearization with 10-20% of the data required for full retraining.

A subfield of transfer learning that addresses the distribution shift between the source PA's behavioral characteristics and the target PA's. Even when both amplifiers are GaN Doherty designs, variations in semiconductor process, bias conditions, and thermal profiles create a domain gap. Domain adaptation techniques align the feature representations:

• Maximum Mean Discrepancy (MMD) minimization in hidden layers
• Adversarial domain confusion where a discriminator tries to identify which PA generated a feature vector
• Correlation alignment matching second-order statistics between source and target activations This ensures the transferred features are invariant to device-specific variations.

The process by which early layers of a neural network learn to transform raw I/Q samples into a representation that captures the universal structure of nonlinear distortion. In transfer learning for DPD, the source model's early layers act as a fixed feature extractor that encodes:

• AM/AM and AM/PM distortion curves
• Memory effect time constants
• Envelope frequency dependencies These extracted features form a device-agnostic basis. The later layers—which map features to predistortion coefficients—are then fine-tuned for the specific target PA. This separation of representation learning from task-specific mapping is the core mechanism enabling transfer.

An adaptive training paradigm where the neural network predistorter coefficients are continuously updated during live signal transmission to track time-varying PA characteristics due to temperature and aging. When combined with transfer learning, online adaptation starts from a robust pre-trained initialization rather than random weights, enabling:

• Faster convergence to optimal linearization after a PA change
• Stability during the initial adaptation phase
• Reduced risk of divergence in closed-loop Direct Learning Architectures (DLA) This hybrid approach—offline transfer followed by online refinement—is the production deployment model for adaptive DPD systems in 5G base stations.