MATLAB Model for High-Power Amplifier with Nonlinearity Analysis and Compensation

High-Power Amplifiers (HPA) are critical components in wireless communication systems, but their inherent nonlinearities cause signal distortion that degrades system performance. MATLAB provides powerful tools for modeling and simulating HPAs, enabling researchers to analyze nonlinear effects and design compensation algorithms. John Terry's source code offers a practical MATLAB model for high-power amplifiers, emphasizing HPA nonlinear characteristics—specifically Amplitude Modulation to Amplitude Modulation (AM/AM) and Amplitude Modulation to Phase Modulation (AM/PM) effects. This model simulates real-world HPA behaviors under high input power, including gain compression and phase shift phenomena. The core implementation approach includes: Nonlinearity Modeling: Polynomial functions or lookup tables (LUTs) are used to characterize the input-output relationship of HPAs, capturing AM/AM and AM/PM distortions. For example, a MATLAB function like `polyval()` can implement polynomial-based modeling, while `interp1()` handles LUT-based approximations for efficient real-time simulation. Signal Simulation: By feeding modulated signals (e.g., QAM or OFDM waveforms) into the model, users can observe HPA-induced impairments such as spectral regrowth and constellation diagram distortion. The simulation typically involves generating baseband signals using `qammod()` or `ofdmmod()`, passing them through the HPA model, and analyzing outputs with `spectrumAnalyzer` or `scatterplot`. Distortion Analysis and Compensation: Based on simulation results, the impact of HPA nonlinearities on Bit Error Rate (BER) can be evaluated using `berawgn()` or custom BER calculation scripts. The model further supports designing pre-distortion or post-distortion compensation algorithms, such as Digital Pre-Distortion (DPD), using iterative learning control (ILC) or adaptive filters like LMS/RLS implemented via `dsp.LMSFilter`. This MATLAB model is essential for studying power amplifier behaviors in high-frequency systems like 5G and satellite communications, and it facilitates the optimization of linearization techniques like DPD. For advanced exploration, researchers can extend the model to simulate different HPA architectures (e.g., Doherty or GaN-based amplifiers) or integrate machine learning algorithms for adaptive nonlinear compensation using MATLAB’s Regression Learner or Neural Network Toolbox.