DIGITAL PRE-DISTORTION SYSTEM FOR RADIO FREQUENCY TRANSMITTERS WITH REDUCED SAMPLING RATE IN OBSERVATION LOOP

• US 20140161207A1
• Filed: 07/01/2013
• Published: 06/12/2014
• Est. Priority Date: 12/07/2012
• Status: Active Grant

• Current Assignee: Maxim Integrated Products, Inc. (Analog Devices, Inc.)
• Original Assignee: Maxim Integrated Products, Inc. (Analog Devices, Inc.)
• Inventors: Teterwak, Jerzy Antoni
• Granted Patent: US 9,014,299 B2
• Time in Patent Office: Days
• US Class Current: 375/297
• CPC Class Codes: H04L 1/0033 arrangements specific to the transmitter
• CPC Class Codes: H04L 27/368 adaptive predistortion

Abstract

A system includes a digital to analog converter, a power amplifier, an analog-to-digital converter, a filter, and a pre-distortion module. The digital to analog converter generates analog data based on digital data. The power amplifier generates output data based on the analog data. The analog-to-digital converter generates samples based on the output data at a sampling rate less than a Nyquist sampling rate. The filter filters the digital data and generates filtered data. The pre-distortion module distorts the digital data based on the samples and the filtered data to compensate for distortion generated by the power amplifier.

Claims

1. A system comprising:

a digital to analog converter that generates analog data based on digital data;

a power amplifier that generates output data based on the analog data;

an analog-to-digital converter that generates samples based on the output data at a sampling rate less than a Nyquist sampling rate;

a filter that filters the digital data and that generates filtered data; and

a pre-distortion module that distorts the digital data based on the samples and the filtered data to compensate for distortion generated by the power amplifier.

2. The system of claim 1 further comprising:

an adaptation module that includes a distortion model of the power amplifier that is used to generate weights based on the samples and the filtered data,

wherein the pre-distortion module distorts the digital data based on the weights.

3. The system of claim 1 further comprising an adaptation module that includes:

a distortion model of the power amplifier that is used to distort the filtered data to generate distortion components; and

a correlator that correlates the distortion components with nonlinear components of an error signal generated based on the samples and the filtered data and that generates weights,

wherein the pre-distortion module distorts the digital data based on the weights.

4. The system of claim 1 further comprising:

a downsampling module that downsamples the filtered data and that generates downsampled data that are time-aligned with the samples generated by the analog-to-digital converter;

a subtractor that generates an error signal based on the samples and the downsampled data;

a distortion model of the power amplifier that is used to distort the filtered data to generate distortion components; and

a correlator that correlates the distortion components with nonlinear components of the error signal and that generates weights,

wherein the pre-distortion module distorts the digital data based on the weights.

5. The system of claim 1 further comprising:

a downsampling module that downsamples the filtered data by a factor k and that generates downsampled data that are time-aligned with the samples generated by the analog-to-digital converter; and

a subtractor that generates an error signal based on the samples and the downsampled data,

wherein the filter includes a finite impulse response filter having coefficients that are adjusted based on the error signal at every k-th sample generated by the analog-to-digital converter.

6. A system comprising:

a power amplifier that generates output data based on digital data;

an analog-to-digital converter that generates samples based on the output data at a sampling rate less than a Nyquist sampling rate;

a filter that filters the digital data and that generates filtered data;

a distortion model of the power amplifier that is used to distort the filtered data to generate distortion components;

a correlator that correlates the distortion components with nonlinear components of an error signal generated based on the samples and the filtered data and that generates weights; and

a pre-distortion module that distorts the digital data based on the weights to compensate for distortion generated by the power amplifier.

7. The system of claim 6 further comprising:

a downsampling module that downsamples the filtered data and that generates downsampled data that are time-aligned with the samples generated by the analog-to-digital converter; and

a subtractor that generates the error signal based on the samples and the downsampled data.

8. The system of claim 6 further comprising:

a downsampling module that downsamples the filtered data by a factor k and that generates downsampled data that are time-aligned with the samples generated by the analog-to-digital converter; and

a subtractor that generates the error signal based on the samples and the downsampled data,

wherein the filter includes a finite impulse response filter having coefficients that are adjusted based on the error signal at every k-th sample generated by the analog-to-digital converter.

9. A method comprising:

generating analog data based on digital data;

generating output data based on the analog data using a power amplifier;

generating samples based on the output data at a sampling rate less than a Nyquist sampling rate;

filtering the digital data to generate filtered data; and

distorting the digital data based on the samples and the filtered data to compensate for distortion generated by the power amplifier.

10. The method of claim 9 further comprising:

generating weights based on the samples and the filtered data using a distortion model of the power amplifier; and

distorting the digital data based on the weights.

11. The method of claim 9 further comprising:

distorting the filtered data using a distortion model of the power amplifier to generate distortion components;

correlating the distortion components with nonlinear components of an error signal generated based on the samples and the filtered data to generate weights; and

distorting the digital data based on the weights.

12. The method of claim 9 further comprising:

downsampling the filtered data and generating downsampled data that are time-aligned with the samples;

generating an error signal based on the samples and the downsampled data;

distorting the filtered data using a distortion model of the power amplifier to generate distortion components;

correlating the distortion components with nonlinear components of the error signal to generate weights; and

distorting the digital data based on the weights.

13. The method of claim 9 further comprising:

downsampling the filtered data by a factor k and generating downsampled data that are time-aligned with the samples;

generating an error signal based on the samples and the downsampled data; and

adjusting coefficients for the filtering based on the error signal at every k-th sample.

14. A method comprising:

generating output data based on digital data using a power amplifier;

generating samples based on the output data at a sampling rate less than a Nyquist sampling rate;

filtering the digital data to generate filtered data;

distorting the filtered data using a distortion model of the power amplifier to generate distortion components;

correlating the distortion components with nonlinear components of an error signal generated based on the samples and the filtered data to generate weights; and

distorting the digital data based on the weights to compensate for distortion generated by the power amplifier.

15. The method of claim 14 further comprising:

downsampling the filtered data to generate downsampled data that are time-aligned with the samples; and

generating the error signal based on the samples and the downsampled data.

16. The method of claim 14 further comprising:

downsampling the filtered data by a factor k to generate downsampled data that are time-aligned with the samples; and

generating the error signal based on the samples and the downsampled data; and

adjusting coefficients for the filtering based on the error signal at every k-th sample.