Wireless Circuitry with Narrowband Error Vector Magnitude (EVM) Estimator
US-2024039764-A1 · Feb 1, 2024 · US
US10177719B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10177719-B2 |
| Application number | US-201715584076-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 2, 2017 |
| Priority date | Jun 1, 2016 |
| Publication date | Jan 8, 2019 |
| Grant date | Jan 8, 2019 |
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A method for predistorting an input signal of an amplifier device comprises evaluating a selection criterion for a computational model of the amplifier device. The computational model provides an output signal of the amplifier device for the input signal of the amplifier device. Further, the method comprises selecting between a first computational model of the amplifier device and a second computational model of the amplifier device based on the evaluated selection criterion. Additionally, the method comprises predistorting the input signal of the amplifier device using the selected computational model.
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The invention claimed is: 1. A method for predistorting an input signal of an amplifier device comprising: evaluating a selection criterion for a computational model of the amplifier device, the computational model providing an output signal of the amplifier device for the input signal of the amplifier device; selecting between a first computational model and a second computational model based on the evaluated selection criterion; generating a set of coefficients for the selected computational model, wherein generating a first set of coefficients for the first computational model requires a higher computational effort than generating a second set of coefficients for the second computational model; and predistorting the input signal using the selected computational model. 2. The method of claim 1 , wherein the first computational model has a higher complexity than the second computational model. 3. The method of claim 1 , wherein the first set of coefficients of the first computational model is generated, while predistorting the input signal using the second set of coefficients of the second computational model. 4. The method of claim 1 , wherein at least one of the first computational model and the second computational model provides the output signal depending nonlinearly on the input signal. 5. The method of claim 4 , wherein the first computational model provides the output signal depending on a present portion and depending on a preceding portion of the input signal. 6. The method of claim 1 , wherein the first computational model comprises a first polynomial and wherein the second computational model comprises a second polynomial, the first polynomial comprising more coefficients than the second polynomial. 7. The method of claim 1 , wherein the selection criterion depends on at least one of an available computing power and an available time budget. 8. The method of claim 1 , wherein the selection criterion depends on at least one of a property of the input signal and a property of the amplifier device. 9. The method of claim 8 , wherein the property of the input signal is at least one of a power of the input signal, a power dynamic range of the input signal, a carrier frequency of the input signal, a bandwidth of the input signal, and a modulation of the input signal. 10. The method of claim 8 , wherein the property of the amplifier device is at least one of a supply voltage, a gain, a maximum output power, and an oscillator frequency. 11. The method of claim 1 , further comprising: observing a trigger condition and at least one of reevaluating the selection criterion and generating the set of coefficients for the selected computational model on occurrence of the trigger condition. 12. The method of claim 11 , wherein the trigger condition comprises at least one of a time out, a temperature change, and a change of quality of the output signal. 13. The method of claim 12 , wherein the quality of the output signal is at least one of an error vector magnitude, an adjacent channel power ratio, a symbol error rate, and a bit error rate. 14. The method of claim 1 , further comprising using a Cholesky decomposition for determining a least squares solution of a linear system of equations, wherein the linear system of equations corresponds to the selected computational model. 15. The method of claim 1 , wherein the amplifier device comprises a signal amplifier. 16. The method of claim 15 , wherein the signal amplifier comprises an amplifier circuit. 17. A predistortion circuit configured to predistort an input signal of an amplifier device comprising: a control subcircuit configured to: evaluate a selection criterion for a computational model of the amplifier device, the computational model providing an output signal of the amplifier device for the input signal, and configured to select between a first computational model and a second computational model based on the evaluated selection criterion and generate a set of coefficients for the selected computational model, wherein generating a first set of coefficients for the first computational model requires a higher computational effort than generating a second set of coefficients for the second computational model; and a predistortion subcircuit configured to predistort the input signal using the selected computational model. 18. An amplifier device, comprising: a predistortion circuit configured to predistort an input signal of the amplifier device comprising: a control subcircuit configured to: evaluate a selection criterion for a computational model of the amplifier device, the computational model providing an output signal of the amplifier device for the input signal, and configured to select between a first computational model and a second computational model based on the evaluated selection criterion and to generate a set of coefficients for the selected computational model, wherein generating a first set of coefficients for the first computational model requires a higher computational effort than generating a second set of coefficients for the second computational model; a predistortion subcircuit configured to predistort the input signal using the selected computational model; and a signal amplifier configured to receive the predistorted input signal and to provide a corresponding output signal. 19. The amplifier device of claim 18 , wherein the signal amplifier comprises at least one of an amplifier circuit, a mixer, a baseband filter, a digital-to-analog converter, and an interpolation filter. 20. A non-transitory machine readable storage medium including program code, when executed, to cause a machine to perform a method for predistorting an input signal of an amplifier device comprising: evaluating a selection criterion for a computational model of the amplifier device, the computational model providing an output signal of the amplifier device for the input signal of the amplifier device; selecting between a first computational model and a second computational model based on the evaluated selection criterion; generating a set of coefficients for the selected computational model, wherein generating a first set of coefficients for the first computational model requires a higher computational effort than generating a second set of coefficients for the second computational model; and predistorting the input signal using the selected computational model.
based on polynomial terms · CPC title
using feedback acting on predistortion circuits (H03F1/3264 takes precedence) · CPC title
A non-specified detector of the power of a signal being used in an amplifying circuit · CPC title
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