Nonlinear behavior models and methods for use thereof in wireless radio systems

• US 8,078,561 B2
• Filed: 12/03/2007
• Issued: 12/13/2011
• Est. Priority Date: 12/01/2006
• Status: Active Grant

• Current Assignee: UTI Limited Partnership
• Original Assignee: UTI Limited Partnership
• Inventors: Liu, Taijun, Boumaiza, Slim, Ghannouchi, Fadhel
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US11/999,264
• Publication Number:
  US 20080133204A1
• Time in Patent Office: 1,471 Days
• Field of Search: 706/46
• US Class Current: 706/46
• CPC Class Codes: H03F 1/3241 using predistortion circuits H03F1/3211, H03F1/3217 take precedence
• CPC Class Codes: H03F 1/3258 based on polynomial terms
• CPC Class Codes: H04B 17/391 Modelling the propagation channel
• CPC Class Codes: H04B 17/3912 Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
• CPC Class Codes: H04B 2001/0425 with linearisation using predistortion

Abstract

Disclosed is a behavioral model for wide-band radio frequency transmitters. Also disclosed are various implementation of the behavioral model for purpose of baseband predistortion of dynamic nonlinear systems, such as wideband wireless transmitters and power amplifiers. In one example embodiment, a LBG behavioral model comprises two non-linear cascading modules: a dynamic weak nonlinear (DWNL) module, which models dynamic week nonlinearities of the system, and a static strong nonlinear (SSNL) module, which models static strong nonlinearities of the system. In one example embodiment, a forward LBG model includes DWNL module followed by the SSNL module. In another example embodiment, a reverse LBG model includes SSNL module followed by DWNL module.

Claims

1. A behavioral model of a nonlinear dynamic system, the model comprising:

a first module characterizing dynamic nonlinear characteristics of the system, wherein the first module includes an input and an output; and

a second module characterizing static nonlinear characteristics of the nonlinear system, wherein the second module includes an input and an output;

wherein the first module is coupled to the second module.

2. The model of claim 1, wherein the first module is coupled to the second module such that an output of the first module is input to the second module.

3. The method of claim 1, wherein the second module is coupled to the first module such that an output of the second module is input to the first module.

4. The model of claim 1 wherein the first module comprises a nonlinear finite impulse response-based memory effect system and the second module comprises a look-up table-based nonlinear memoryless system.

5. The model of claim 1 wherein the first module is a dynamic weak nonlinear model and the second module is a static strong nonlinear model.

6. The model of claim 1 wherein the first module comprises a finite impulse response filter-based multi-branch nonlinear system and the second module comprises two look-up tables, each look-up table dependent on the magnitude of the input signal.

7. A predistorter for nonlinear wireless system, the predistorter comprising:

a dynamic nonlinear predistorter module; and

a static nonlinear predistorter module coupled to the dynamic nonlinear predistorter module such that an output of the dynamic nonlinear predistorter module is input to the static nonlinear predistorter module.

8. The predistorter of claim 7 wherein the dynamic nonlinear predistorter is a finite impulse response-based memory effect predistorter and the static nonlinear predistorter is a look-up table-based memoryless predistorter.

9. The predistorter of claim 7 wherein the dynamic nonlinear predistorter is a dynamic weak nonlinear predistorter and the static nonlinear predistorter is a static strong nonlinear predistorter.

10. The predistorter of claim 9 wherein the dynamic weak nonlinear predistorter is implemented as a finite impulse response filter-based multi-branch nonlinear structure embedded within a polynomial like series and the static strong nonlinear predistorter includes two look-up tables, each look-up table depends on the magnitude of a current input signal.

11. A predistorter for nonlinear wireless system, the predistorter comprising:

a static nonlinear predistorter module; and

dynamic nonlinear predistorter module coupled to the static nonlinear predistorter module such that an output of the static nonlinear predistorter module is input to the dynamic nonlinear predistorter module.

12. The predistorter of claim 11 wherein the dynamic nonlinear predistorter module is a finite impulse response-based memory effect predistorter module and the static nonlinear predistorter is a look-up table-based memoryless predistorter module.

13. The predistorter of claim 11 wherein the dynamic nonlinear predistorter module is a dynamic weak nonlinear predistorter module and the static nonlinear predistorter is a static strong nonlinear predistorter module.

14. The predistorter of claim 12 wherein the dynamic weak nonlinear predistorter is implemented as a finite impulse response filter-based multi-branch nonlinear structure and the static strong nonlinear predistorter includes two look-up tables, each look-up table depends on the magnitude of a current input signal.

15. An augmented Wiener predistorter, the predistorter comprising:

a dynamic nonlinear first order predistorter module; and

a static nonlinear predistorter module coupled to the dynamic nonlinear predistorter module such that an output of the dynamic nonlinear predistorter module is input to the static nonlinear predistorter module.

16. The predistorter of claim 15 wherein the dynamic nonlinear predistorter is a finite impulse response-based memory effect predistorter and the static nonlinear predistorter is a look-up table-based memoryless predistorter.

17. The predistorter of claim 15 wherein the dynamic nonlinear predistorter is a dynamic weak nonlinear predistorter and the static nonlinear predistorter is a static strong nonlinear predistorter.

18. The predistorter of claim 17 wherein the dynamic weak nonlinear predistorter is implemented as a finite impulse response filter-based multi-branch nonlinear structure and the static strong nonlinear predistorter includes two look-up tables, each look-up table depends on the magnitude of a current input signal.

19. An augmented Hammerstein predistorter, the predistorter comprising:

a static nonlinear predistorter module; and

a dynamic nonlinear first order predistorter module coupled to the static nonlinear predistorter module such that an output of the static nonlinear predistorter module is input to the dynamic nonlinear predistorter module.

20. The predistorter of claim 19 wherein the dynamic nonlinear predistorter module is a finite impulse response-based memory effect predistorter module and the static nonlinear predistorter is a look-up table-based memoryless predistorter module.

21. The predistorter of claim 19 wherein the dynamic nonlinear predistorter module is a dynamic weak nonlinear predistorter module and the static nonlinear predistorter is a static strong nonlinear predistorter module.

22. The predistorter of claim 21 wherein the dynamic weak nonlinear predistorter is implemented as a finite impulse response filter-based multi-branch nonlinear structure and the static strong nonlinear predistorter includes two look-up tables, each look-up table depends on the magnitude of a current input signal.