Dynamic digital pre-distortion system
A Dynamic Digital Pre-Distortion (DDPD) system is disclosed to rapidly correct power amplifier (PA) non-linearity and memory effects. To perform pre-distortion, a DDPD engine predistorts an input signal in order to cancel PA nonlinearities as the signal is amplified by the PA. The DDPD engine is implemented as a composite of one linear filter and N−1 high order term linear filters. The bank of linear filters have programmable complex coefficients. To compute the coefficients, samples from the transmit path and a feedback path are captured, and covariance matrices A and B are computed using optimized hardware. After the covariance matrices are computed, Gaussian elimination processing may be employed to compute the coefficients. Mathematical and hardware optimizations may be employed to simplify and reduce the number of multiplication operands and other operations, which can enable the DDPD system to fit within a single chip.
1. A method for an apparatus for generating covariance matrices A and B for each data capture of a transmit signal, the covariance matrices A and B for use in computing weights W and producing a pre-distorted transmit signal to cancel nonlinearities created by a distorting element or distorting system when transmitting the transmit signal, the method comprising:
providing in the apparatus coefficient estimator circuitry configured for:
establishing power level ranges or bins for the transmit signal as received and at a highest power level for the received transmit signal;
when a predetermined number of data captures of the transmit signal are received that fall within the power level ranges or bins,
averaging covariance matrices A and B computed for the data captures within the power level ranges or bins to generate averaged high end covariance matrices A and B, and
scaling the averaged high end covariance matrix A by a first scaling factor and scaling the averaged high end covariance matrix B by a second scaling factor to compute extrapolated covariance matrices A and B; and
generating covariance matrices A and B for data captures of a transmit signal having a power level below a particular threshold utilizing the extrapolated covariance matrices A and B.
2. The method of claim 1 , further comprising reducing the first and second scaling factors if no data captures of the transmit signal fall within the power level ranges or bins for a predetermined amount of time.
3. The method of claim 1 , further comprising computing the weights W for each data capture of the transmit signal using the generated covariance matrices A and B.
4. The method of claim 3 , further comprising verifying W after computing W.
5. The method of claim 3 , further comprising validating W by:
computing ΔB=B−AW;
computing the normalized error
E
=
Δ
B
B
;
rejecting W and utilizing previous W values if E is larger than a predetermined threshold.
6. An apparatus for generating covariance matrices A and B for each data capture of a transmit signal, the covariance matrices A and B for use in computing weights W and producing a pre-distorted transmit signal to cancel nonlinearities created by a distorting element or distorting system when transmitting the transmit signal, the apparatus comprising coefficient estimator circuitry configured for:
establishing power level ranges or bins for the transmit signal as received and at a highest power level for the received transmit signal;
when a predetermined number of data captures of the transmit signal are received that fall within the power level ranges or bins,
averaging covariance matrices A and B computed for the data captures within the power level ranges or bins to generate averaged high end covariance matrices A and B, and
scaling the averaged high end covariance matrix A by a first scaling factor and scaling the averaged high end covariance matrix B by a second scaling factor to compute extrapolated covariance matrices A and B; and
generating covariance matrices A and B for data captures of a transmit signal having a power level below a particular threshold utilizing the extrapolated covariance matrices A and B.
7. The apparatus of claim 6 , the coefficient estimator circuitry further configured for reducing the first and second scaling factors if no data captures of the transmit signal fall within the power level ranges or bins for a predetermined amount of time.
8. The apparatus of claim 6 , the coefficient estimator circuitry further configured for computing the weights W for each data capture of the transmit signal using the generated covariance matrices A and B.
9. The apparatus of claim 8 , the coefficient estimator circuitry further configured for verifying W after computing W.
10. The apparatus of claim 8 , the coefficient estimator circuitry further configured for validating W by:
computing ΔB=B−AW;
computing the normalized error
E
=
Δ
B
B
;
rejecting W and utilizing previous W values if E is larger than a predetermined threshold.
11. A computer program product for generating covariance matrices A and B for each data capture of a transmit signal, the covariance matrices A and B for use in computing weights W and producing a pre-distorted transmit signal to cancel nonlinearities created by a distorting element or distorting system when transmitting the transmit signal, and including one or more computer readable instructions embedded on a non-transitory computer readable storage medium and configured to cause one or more computer processors to perform the steps of:
establishing power level ranges or bins for the transmit signal at about a highest power transmit signal received;
when a predetermined number of data captures of the transmit signal are received that fall within the power level ranges or bins,
averaging covariance matrices A and B computed for the data captures within the power level ranges or bins to generate averaged high end covariance matrices A and B, and
scaling the averaged high end covariance matrix A by a first scaling factor and scaling the averaged high end covariance matrix B by a second scaling factor to compute extrapolated covariance matrices A and B; and
generating covariance matrices A and B for data captures of a transmit signal having a power level below a particular threshold utilizing the extrapolated covariance matrices A and B.
12. The computer program product of claim 11 , further comprising reducing the first and second scaling factors if no data captures of the transmit signal fall within the power level ranges or bins for a predetermined amount of time.
13. The computer program product of claim 11 , further comprising computing the weights W for each data capture of the transmit signal using the generated covariance matrices A and B.
14. The computer program product of claim 13 , further comprising verifying W after computing W.
15. The computer program product of claim 13 , further comprising validating W by:
computing ΔB=B−AW;
computing the normalized error
E
=
Δ
B
B
;
rejecting W and utilizing previous W values if E is larger than a predetermined threshold.