Enhanced digital predistortion

• US 20070296493A1
• Filed: 06/27/2006
• Published: 12/27/2007
• Est. Priority Date: 06/27/2006
• Status: Active Grant

• Current Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Wang, Robert Chuenlin
• Granted Patent: US 7,388,430 B2
• Time in Patent Office: Days
• US Class Current: 330/149
• CPC Class Codes: H03F 1/3247 using feedback acting on predistortion circuits H03F1/3264 takes precedence
• CPC Class Codes: H03F 1/3258 based on polynomial terms
• CPC Class Codes: H03F 2200/228 A measuring circuit being coupled to the input of an amplifier
• CPC Class Codes: H03F 2200/393 A measuring circuit being coupled to the output of an amplifier
• CPC Class Codes: H03F 2200/451 the amplifier being a radio frequency amplifier

Abstract

In one embodiment of the present invention, a weight vector is generated based on a pre-distorted input signal and an output signal of an amplifier using a computation reduction technique. The computation reduction technique decomposes a number of multiplication operations between complex numbers such that a number of multiplications and a number of additions to generate the weight vector is reduced as compared to if the number of multiplication operations between complex numbers was not decomposed. An input signal for input to an amplifier is pre-distorted based on the generated weight vector.

Claims

1. A method of pre-distorting a signal for input to an amplifier, comprising:

generating a weight vector based on a pre-distorted input signal and an output signal of the amplifier using a computation reduction technique; and

pre-distorting an input signal for input to an amplifier based on the generated weight vector; and

wherein the computation reduction technique decomposes a number of multiplication operations between complex numbers such that a total number of multiplications and a total number of additions to generate the weight vector is reduced as compared to if the number of multiplication operations between complex numbers was not decomposed.

2. The method of claim 1, wherein the generating step comprises:

computing a correlation diagonal block derived from one of the output signal and a previous output signal, a first term of the vector being a complex number and having a magnitude, and each subsequent term of the vector being obtained by multiplying the first term by a result of raising the magnitude to a respective exponential order.

3. The method of claim 2, wherein the computing step comprises:

computing a square of the first term to obtain a first scalar value;

computing a square root of the square to obtain a second scalar value;

using scalar multiplication and the first and second scalar values to obtain elements in the diagonal block.

4. The method of claim 1, wherein the generating step comprises:

computing a correlation off-diagonal block derived from a first vector and a second vector,

the first vector derived from one of the output signal and a previous output signal, a first term of the vector being a complex number and having a magnitude, and each subsequent term of the first vector being obtained by multiplying the first term by a result of raising the magnitude to a respective exponential order, and

the second vector derived from a previous output signal, and a first term of the second vector being a previous first term of the first vector.

5. The method of claim 4, wherein the computing step comprises:

computing a first element of a first row of the off-diagonal block as the first term of the first vector times a complex conjugate of the first term of the second vector;

computing remaining elements of the first row of the off-diagonal block using scalar-complex multiplication, the first element and a magnitude of the first term of the second vector;

computing each subsequent row of the off-diagonal block using scalar-complex multiplication, a previous row of the off-diagonal block, and the magnitude of the first term of the first vector.

6. The method of claim 1, wherein the generating step comprises:

computing a cross correlation vector block derived from the pre-distorted input signal and a vector, the vector obtained from one of the output signal and a previous output signal, a first term of the vector being a complex number and having a magnitude, and each subsequent term of the vector being obtained by multiplying the first term by a result of raising the magnitude to a respective exponential order.

7. The method of claim 6, wherein the computing step comprises:

computing a first element of the cross-correlation vector block as the pre-distortion input signal times the first term; and

computing a number of subsequent elements of the cross-correlation block using scalar multiplication a previous term and the magnitude of the first term.

8. The method of claim 1, wherein the generating step comprises:

computing an off-diagonal cross correlation block derived from a first vector and a second vector,

the first vector derived from one of the output signal and a previous output signal as a first term, a first term of the vector being a complex number and having a magnitude, and each subsequent term of the vector being obtained by multiplying the first term by a result of raising the magnitude to a respective exponential order, and

each term of the second vector derived from a different previous output signal and the output signal.

9. The method of claim 8, wherein the computing step comprises:

computing a square of the first term to obtain a first scalar value;

computing a square of each term in the second vector to obtain a set of second scalar values; and

using scalar multiplication, the first scalar value and the set of second scalar values to obtain elements in the off-diagonal cross correlation block.

10. The method of claim 1, wherein the generating step comprises:

computing an off-diagonal cross correlation block derived from a first and a second vector,

each term of the first vector being a different previous output signal squared times the output signal, and

each term of the second vector being a square of a corresponding term in the first vector.

11. The method of claim 10, wherein the computing step comprises:

computing a square of each different previous output signal to obtain a first set of scalar values;

computing a square of each value in the first set of scalar values to obtain a second set of scalar values;

computing a square of the output signal to obtain a third scalar value; and

using scalar multiplication, the first set of scalar value, the second set of scalar value and the third scalar value to obtain the elements of the off-diagonal cross-correlation block.

12. The method of claim 1, wherein the generating step comprises:

computing a diagonal cross-correlation block derived from a first vector, each term of the first vector being a different previous output signal squared times the output signal.

13. The method of claim 12, wherein the computing step comprises:

computing a square of each different previous output signal to obtain a first set of scalar values;

computing a square of the output signal to obtain a second scalar value; and

using scalar multiplication, the first set of scalar values and the second scalar value to obtain the elements of the diagonal cross-correlation block.

14. The method of claim 1, wherein the generating step comprises:

computing a cross correlation vector block derived from the pre-distorted input signal and a first vector, each term of the first vector being a different previous output signal squared times the output signal.

15. The method of claim 14, wherein the computing step comprises:

compute the pre-distortion input signal times the output signal to obtain a complex value;

compute the square of each different previous output signal to obtain a set of scalar values; and

using scalar-complex multiplication, the complex value and the set of scalar values to obtain elements of the cross correlation vector block.

16. The method of claim 1, wherein the generating step comprises:

computing a cross correlation vector block derived from the pre-distorted input signal and a first vector, each term of the first vector being a different previous output signal quadrupled times the output signal.

17. The method of claim 14, wherein the computing step comprises:

compute the pre-distortion input signal times the output signal to obtain a complex value;

compute the square of each different previous output signal to obtain a set of scalar values; and

using scalar-complex multiplication, the complex value and the set of scalar values to obtain elements of the cross correlation vector block.