System and method for frequency-domain weighted least squares

1. A method, comprising:

determining a training data set comprising input and output data of a power amplifier;

determining compensation data by regressing the training data set using a frequency-domain weighting function, wherein the frequency-domain weighting function is adjusted according to a target value of at least one of an adjacent channel leakage ratio (ACLR) region and an error vector magnitude (EVM), and wherein the frequency-domain weighting function includes a window that is parameterized by at least one of an ACLR region boundary, a noise region boundary, an ACLR boosting factor, and a noise region boosting factor;

storing the compensation data; and

linearizing an output of the power amplifier using the stored compensation data.

2. The method of claim 1 , wherein the frequency-domain weighting function boosts at least one of the ACLR region, an out of band region, and a noise region.

3. The method of claim 2 , wherein the window is in a shape of at least one of rectangular, Hamming, triangular, bell, sinusoid, and sigmoid.

4. The method of claim 1 , wherein the frequency-domain weighting function is adjusted according to a target value of both the ACLR region and the EVM.

5. The method of claim 1 , wherein power is supplied to the power amplifier by at least one of envelope tracking (ET) or average power tracking (APT).

6. The method of claim 1 , wherein the compensation data is stored in a look up table (LUT).

7. The method of claim 1 , wherein regressing the training data comprises determining coefficients of a memory polynomial using a least squares regression analysis.

8. The method of claim 2 , wherein a power spectral density of the power amplifier output is less than a power spectral density of a non-compensated power amplifier output in the ACLR region.

9. The method of claim 1 , wherein the output of the power amplifier comprises a wireless signal associated with at least one of device to device (D2D), machine type communications (MTC), fifth generation (5G), long term evolution (LTE), long term evolution advanced (LTE-A), code division multiple access (CDMA), wideband code division multiple access (WCDMA), universal mobile telecommunications system (UMTS), wireless broadband (WiBro), global system for mobile communications (GSM), wireless fidelity (Wi-Fi), Bluetooth, and near field communications (NFC).

10. An electronic device, comprising:

a power amplifier;

a storage; and

a processor configured to:

determine a training data set comprising input and output data of the power amplifier,

determine compensation data by regressing the training data set using a frequency-domain weighting function, wherein the frequency-domain weighting function is adjusted according to a target value of at least one of an adjacent channel leakage ratio (ACLR) region and an error vector magnitude (EVM), and wherein the frequency-domain weighting function includes a window that is parameterized by at least one of an ACLR region boundary, a noise region boundary, an ACLR boosting factor, and a noise region boosting factor,

store the compensation data in the storage, and

linearize an output of the power amplifier using the stored compensation data.

11. The device of claim 10 , wherein the frequency-domain weighting function boosts at least one of the ACLR region, an out of band region, and a noise region.

12. The device of claim 11 , wherein the window is in a shape of at least one of rectangular, Hamming, triangular, bell, sinusoid, and sigmoid.

13. The device of claim 10 , wherein the frequency-domain weighting function is adjusted according to a target value of both the ACLR region and the EVM.

14. The device of claim 10 , wherein power is supplied to the power amplifier by at least one of envelope tracking (ET) or average power tracking (APT).

15. The device of claim 10 , wherein the compensation data is stored in a look up table (LUT).

16. The device of claim 10 , wherein regressing the training data comprises determining coefficients of a memory polynomial using a least squares regression analysis.

17. The device of claim 10 , wherein the output of the power amplifier comprises a wireless signal associated with at least one of device to device (D2D), machine type communications (MTC), fifth generation (5G), long term evolution (LTE), long term evolution advanced (LTE-A), code division multiple access (CDMA), wideband code division multiple access (WCDMA), universal mobile telecommunications system (UMTS), wireless broadband (WiBro), global system for mobile communications (GSM), wireless fidelity (Wi-Fi), Bluetooth, and near field communications (NFC).

18. A chipset, the chipset configured to:

determine a training data set comprising input and output data of a power amplifier,

determine compensation data by regressing the training data set using a frequency-domain weighting function, wherein the frequency-domain weighting function is adjusted according to a target value of at least one of an adjacent channel leakage ratio (ACLR) region and an error vector magnitude (EVM), and wherein the frequency-domain weighting function includes a window that is parameterized by at least one of an ACLR region boundary, a noise region boundary, an ACLR boosting factor, and a noise region boosting factor,

store the compensation data in a storage, and

linearize an output of the power amplifier using the stored compensation data.