# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English version of the page.

# Power Amplifier

Model power amplifier with memory

• Library:
• RF Blockset / Circuit Envelope / Elements

## Description

The Power Amplifier block models two-terminal power amplifiers. A memory polynomial expression derived from the Volterra series models the nonlinear relationship between input and output signals. This power amplifier includes memory effects because the output response depends on the current input signal as well as the input signal at preceding times. These power amplifiers are useful while transmitting wideband or narrowband signals.

## Parameters

expand all

Model type, specified as:

• `Memory polynomial` – This implementation operates on the in-phase (I) and the quadrature (Q) phase components of the envelope of the input signal. The narrowband memory polynomial does not generate new frequency components, but it captures in-band spectral regrowth. You can use this model to create a narrowband amplifier operating at a high frequency. The matrix α represent the memory polynomial series:

• `Generalized Hammerstein` – This implementation operates on the real passband input signal. This wideband model generates harmonics that are integral multiples of carrier frequencies. This formulation is equivalent to a static polynomial nonlinearity followed by a linear filter. You can use this model to create wideband amplifiers operating at low frequency. The matrix α represent the memory polynomial series:

Coefficient matrix, specified as an M-by-N complex coefficient matrix for `Memory polynomial` and M-by-N real matrix for `Generalized Hammerstein` where, M is the Number of delays and N is the highest order of poly voltages in degrees..

For the `Memory polynomial` model, you can identify the complex coefficient matrix based on the measured complex (I,Q) output vs. input amplifier characteristic. Use the `fit_memory_poly_model` function given in Algorithms as an example for identifying the coefficient matrix. When identifying the coefficients, make sure that the input-output data is aligned in time.

For the `Generalized Hammerstein` model, you can identify the real coefficient matrix based on the measured real passband output vs. input amplifier characteristic. Use the `fit_hammerstein_model` function given in Algorithms as an example for identifying the coefficient matrix. When identifying the coefficients, make sure that the input-output data is aligned in time.

Input resistance, specified as a real scalar greater than `0`.

Output resistance, specified as a real scalar greater than `0`.

Ground RF circuit terminals, specified as `on` or `off`. Select this parameter to ground and hide the negative terminals. Clear the parameter to expose the negative terminals. By exposing these terminals, you can connect them to other parts of your model.

By default, this option is selected.

expand all

## References

[1] Morgan,Dennis R., Zhengxiang Ma, Jaehyeong Kim, and Michael G.Zierdt. "A Generalized Memory Polynomial Model for Digital Predistortion of Power Amplifiers". IEEE Transactions on Signal Processing Vol.54, No.10, October 2006.