Low power and low complexity adaptive self-linearization
View Patent ↗A method of signal processing comprises receiving an unknown input signal that includes a distorted component and an undistorted component, the unknown input signal having a sampling rate of R; and performing self-linearization based at least in part on the unknown signal to obtain an output signal that is substantially undistorted, including by generating a replica distortion signal that is substantially similar to the distorted component, the generation being based at least in part on a target component having a sampling rate of R/L, L being an integer greater than 1. A digital signal processing system comprises an input terminal configured to receive an unknown input signal that includes a distorted component and an undistorted component, the unknown input signal having a sampling rate of R; and an adaptive self-linearization module coupled to the input terminal, configured to perform self-linearization based at least in part on the unknown input signal to obtain an output signal that is substantially undistorted, wherein the adaptive self-linearization module includes a replica distortion signal generator configured to generate a replica distortion signal that is substantially similar to the distorted component, the generation being based at least in part on a target component having a sampling rate of R/L, L being an integer greater than 1.
1. A method of signal processing, comprising:
receiving an unknown input signal that includes a distorted component and an undistorted component, the unknown input signal having a sampling rate of R; and
performing self-linearization based at least in part on the unknown signal to obtain an output signal that is substantially undistorted, including by generating a replica distortion signal that is substantially similar to the distorted component, the generation being based at least in part on a target component having a sampling rate of R/L, L being an integer greater than 1.
2. The method of claim 1 , wherein performing self-linearization further includes adapting a separation filter to separate from the input signal a reference component and the target component, and wherein the separation filter is adapted to generate the reference component and the target component at a sampling rate of R/L.
3. The method of claim 2 , wherein separating the reference component and the target component includes adapting a persistence filter.
4. The method of claim 2 , wherein separating the reference component and the target component includes:
a) initializing a persistence filter having a plurality of adaptable coefficients, including receiving a plurality of input signal samples;
b) computing a reference component sample using a shared operator, based on the plurality of input signal samples;
c) computing a target component sample based at least in part on the reference component; and
d) updating at least one of the adaptable coefficients based at least in part on the reference component sample.
5. The method of claim 4 , wherein the shared operator includes a shared multiplier.
6. The method of claim 5 , wherein computing the reference component includes:
computing a plurality of input sample-coefficient products using the shared multiplier;
accumulating the plurality of products to generate a sum.
7. The method of claim 6 , wherein computing the target component includes comparing an input sample with the sum.
8. The method of claim 1 , wherein performing self-linearization further includes adapting a digital signal processor (DSP) to obtain a filter transfer function that approximates a system distortion transfer function.
9. A digital signal processing system comprising:
an input terminal configured to receive an unknown input signal that includes a distorted component and an undistorted component, the unknown input signal having a sampling rate of R; and
an adaptive self-linearization module coupled to the input terminal, configured to perform self-linearization based at least in part on the unknown input signal to obtain an output signal that is substantially undistorted, wherein the adaptive self-linearization module includes a replica distortion signal generator configured to generate a replica distortion signal that is substantially similar to the distorted component, the generation being based at least in part on a target component having a sampling rate of R/L, L being an integer greater than 1.
10. The system of claim 9 , wherein the replica distortion signal generator includes a separation filter adapted to separate from the input signal a reference component and the target component, and wherein the separation filter is adapted to generate the reference component and the target component at a sampling rate of R/L.
11. The system of claim 10 , wherein the separation filter includes a persistence filter that generates the reference signal at a down-sampled rate of R/L.
12. The system of claim 11 , wherein the persistence filter includes a shared operator configured to perform repeated operations involving a plurality of input sample-coefficient pairs.
13. The system of claim 11 , wherein the persistence filter includes a plurality of shared operators each configured to perform operations on a corresponding plurality of input samples and a corresponding plurality of adaptable coefficients.
14. The system of claim 10 , wherein the separation filter includes a persistence filter comprising:
a first memory configured to store a plurality of input samples;
a input sample selector configured to select an input sample from the plurality of input samples;
a second memory configured to store a plurality of adaptable filter coefficients;
a coefficient selector configured to select an adaptable filter coefficient from the plurality of adaptable filter coefficients;
a shared operator coupled to the input sample selector and the coefficient selector, configured to perform an operation on the selected input sample and the selected adaptable filter coefficient; and
an update block coupled to the second memory, configured to update the plurality of adaptive filter coefficients.
15. The system of claim 14 , wherein the shared operator includes a shared multiplier configured to compute a product of the selected input sample and the selected adaptable filter coefficient.
16. The system of claim 14 , wherein the shared operator includes a shared multiplier and an accumulator.
17. The system of claim 9 , wherein the replica distortion signal generator includes a DSP adapted to obtain a filter transfer function that approximates a system distortion transfer function.
18. The system of claim 17 , wherein the DSP includes an adaptive nonlinear phase filter having a time-varying phase response, and an adaptive nonlinear amplitude filter having a time-varying amplitude response.
19. The system of claim 18 , wherein the adaptive nonlinear phase filter includes an infinite impulse response (IIR) filter.
20. The system of claim 18 , wherein the adaptive nonlinear phase filter includes an infinite impulse response (IIR) filter whose time domain function is r n =ã 0 (y n −r n−N )+ã 1 (y n−1 −r n−N−1 )+ . . . +y n−N .
21. The system of claim 18 , wherein the adaptive nonlinear amplitude filter includes a finite impulse response (FIR) filter.
22. The system of claim 21 , wherein the FIR filter has a filter coefficient that is a time-varying function of the input to the FIR filter.
23. The system of claim 22 , wherein the time-varying function is a first order function of the input to the FIR filter.
24. The system of claim 9 , wherein the replica distortion signal generator includes a first DSP adapted to obtain a first filter transfer function that approximates a system distortion transfer function, and a duplicate DSP configured to have a duplicate filter transfer function that is substantially similar to the first filter transfer function.