What is the behavioral model of a power amplifier and how is it used for system simulation?
PA Behavioral Modeling for System Simulation
Behavioral modeling bridges the gap between circuit-level PA design (which uses detailed transistor models and takes hours to simulate) and system-level analysis (which needs to simulate millions of signal samples in seconds). A good behavioral model runs 1000-10000x faster than a circuit simulation while capturing the essential nonlinear behavior.
| Parameter | Class A | Class AB | Class F/Doherty |
|---|---|---|---|
| Max Efficiency | 50% | 50-78% | 70-90% |
| Linearity | Excellent | Good | Moderate (needs DPD) |
| P1dB Backoff | 0-3 dB | 3-6 dB | 6-10 dB |
| Complexity | Low | Low | High |
| Common Use | Test, small signal | General PA | Base station, broadcast |
- Performance verification: confirm specifications against the application requirements before finalizing the design
- Environmental factors: temperature range, humidity, and vibration affect long-term reliability and parameter drift
- Cost vs. performance: evaluate whether the application demands premium components or standard commercial grades
Frequently Asked Questions
How do I extract a behavioral model from measurements?
Capture the PA's input and output I/Q waveforms using a vector signal generator (VSG) and vector signal analyzer (VSA) with a wideband modulated signal (the signal should exercise the PA across its full dynamic range). Align the input and output waveforms in time and amplitude. Fit the model coefficients (polynomial, Volterra, or neural network parameters) using least-squares regression: minimize the error between the model output and the measured output for the captured waveform. Validate the model with a different signal from the one used for extraction.
How accurate are behavioral models?
A well-extracted memory polynomial model achieves: NMSE (normalized mean square error) of -35 to -45 dB between model output and measured output. ACLR prediction: within 1-3 dB of measurement. EVM prediction: within 0.5-1%. The accuracy depends on: using a representative extraction signal (the signal should cover the PA's full dynamic range), including sufficient memory depth, and using appropriate polynomial order. For GaN PAs with strong memory, neural network models can improve accuracy by 5-10 dB in NMSE.
Can I use a behavioral model for DPD design?
Yes, this is the primary use case. The DPD is the mathematical inverse of the PA behavioral model. A memory polynomial PA model yields a memory polynomial DPD with the same structure but inverse coefficients. The DPD coefficients are extracted using the same input/output waveform capture and least-squares fitting, but with the PA output as the 'input' and the desired linear output as the 'desired output' in the fitting.