Multi-band wideband power amplifier digital predistorition system and method

• US 8,351,877 B2
• Filed: 12/21/2010
• Issued: 01/08/2013
• Est. Priority Date: 12/21/2010
• Status: Active Grant

• Current Assignee: Dali Systems Co., Ltd.
• Original Assignee: Dali Systems Co., Ltd.
• Inventors: Kim, Wan Jong, Cho, Kyoung Joon, Stapleton, Shawn Patrick
• Primary Examiner(s): LE, NHAN T
• Application Number:
  US12/928,934
• Publication Number:
  US 20120154038A1
• Time in Patent Office: 749 Days
• Field of Search: 455/114.2, 455/114.3, 455/127.1, 455/127.2, 455/550.1, 375/296, 375/297, 330/149
• US Class Current: 455/114.3
• CPC Class Codes: H03F 1/3247 using feedback acting on predistortion circuits H03F1/3264 takes precedence
• CPC Class Codes: H03F 2200/111 the amplifier being a dual or triple band amplifier, e.g. 900 and 1800 MHz, e.g. switched or not switched, simultaneously or not
• CPC Class Codes: H03F 2200/451 the amplifier being a radio frequency amplifier
• CPC Class Codes: H04B 1/0475 with means for limiting noise, interference or distortion H04B1/0483 takes precedence
• CPC Class Codes: H04B 1/62 for providing a predistortion of the signal in the transmitter and corresponding correction in the receiver, e.g. for improving the signal/noise ratio
• CPC Class Codes: H04L 27/2626 Arrangements specific to the transmitter only
• CPC Class Codes: H04L 27/368 adaptive predistortion

Abstract

A high performance and cost effective method of RF-digital hybrid mode power amplifier systems with high linearity and high efficiency for multi-frequency band wideband communication system applications is disclosed. The present disclosure enables a power amplifier system to be field reconfigurable and support multiple operating frequency bands on the same PA system over a very wide bandwidth. In addition, the present invention supports multi-modulation schemes (modulation agnostic), multi-carriers and multi-channels.

Claims

1. A digital predistortion system comprising:

an RF input signal;

a power amplifier for providing an amplified output including a distortion characteristic;

a feedback signal derived from the amplified output including a representation of at least a portion of the distortion characteristic; and

predistortion logic responsive to the feedback signal and to the input signal for generating an aliased representation of the input signal and deriving predistortion coefficients based on the aliased signals, wherein a sampling rate of the aliased representation of the input signal is less than twice a maximum bandwidth of the input signal.

2. The digital predistortion system of claim 1 further comprising a digital predistorter that receives the RF input signal, the digital predictor effecting the predistortion coefficients derived by the predistortion logic.

3. The digital predistortion system of claim 2 wherein the digital predistorter utilizes an adaptive polynomial-based digital predistortion system, the polynomial-based digital predistortion system utilizing the predistortion coefficients derived by the predistortion logic.

4. The digital predistortion system of claim 2 wherein the digital predistorter utilizes a look-up-table-based digital predistortion system with finite impulse response filters, wherein coefficients for the finite impulse response filters comprise the predistortion coefficients derived by the predistortion logic.

5. The digital predistortion system of claim 2 further comprising:

a digital to analog convertor coupled to the digital predistorter and the power amplifier; and

an analog to digital convertor that converts the feedback signal from analog to digital format.

6. The digital predistortion system of claim 1 further comprising alignment logic that aligns the feedback signal relative to the RF input signal.

7. The digital predistortion system of claim 1 wherein the aliased representation of the input signal has a frequency in a Nyquist zone.

8. The digital predistortion system of claim 1 wherein the predistortion coefficients are derived using a Direct Learning adaptive algorithm.

9. The digital predistortion system of claim 1 wherein the predistortion coefficients are derived using an Indirect Learning adaptive algorithm.

10. The digital predistortion system of claim 1 wherein the predistortion coefficients are derived to account for memory effects due to one or more of self-heating, bias networks and frequency dependencies of active devices in the digital predistortion system.

11. A method for amplifying signals, the method comprising:

receiving an RF input signal;

detecting an amplified output from a power amplifier, the output being responsive to the RF input signal and including a distortion characteristic;

deriving a feedback signal from the amplified output including a representation of at least a portion of the distortion characteristic; and

generating, via predistortion logic responsive to the feedback signal and to the input signal, an aliased representation of the input signal and deriving predistortion coefficients based on the aliased signals, wherein a sampling rate of the aliased representation of the input signal is less than twice a maximum bandwidth of the input signal.

12. The method of claim 11 further comprising:

receiving a second RF input signal; and

predistorting, using a digital predistortor, the second RF input signal using the predistortion coefficients derived by the predistortion logic.

13. The method of claim 12 wherein predistorting the second RF input signal comprises utilizing an adaptive polynomial-based digital predistortion system, the polynomial-based digital predistortion system utilizing the predistortion coefficients derived by the predistortion logic.

14. The method of claim 12 wherein predistorting the second RF input signal comprises utilizing a look-up-table-based digital predistortion system with finite impulse response filters, wherein coefficients for the finite impulse response filters comprise the predistortion coefficients derived by the predistortion logic.

15. The method of claim 11 further comprising:

converting the feedback signal from analog to digital format; and

converting the predistorted second RF input from digital to analog format.

16. The method of claim 11 further comprising aligning the feedback signal relative to the RF input signal.

17. The method of claim 11 wherein the aliased representation of the input signal has a frequency in a Nyquist zone.

18. The method of claim 11 wherein the predistortion coefficients are derived using a Direct Learning adaptive algorithm.

19. The method of claim 11 wherein the predistortion coefficients are derived using an Indirect Learning adaptive algorithm.

20. The method of claim 11 wherein the predistortion coefficients are derived to account for memory effects due to one or more of self-heating, bias networks and frequency dependencies of active devices in the digital predistortion system.