US 8,804,872 B1Granted

Dynamic determination of volterra kernels for digital pre-distortion

AssigneeTexas Instruments Incorporated

InventorsHongzhi Zhao, Xiaohan Chen, Xing Zhao, David L. Brubaker

Life of this patent

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Abstract

A method of dynamically calculating and updating the Volterra kernels used by the Digital Pre Distortion engine based on output power, input signal bandwidth, multicarrier configuration, frequency response and power amplifier temperature. A dominant Volterra kernels searching DSP engine based on innovation bases with minimum RMS error selection is implemented to continuously update the Volterra kernels set used in DPD to model the power amplifier non linear behaviors.

Claims4

Dependent claims

A method of Digital Pre Distortion (DPD) comprising the steps of:

modeling the non linear behavior of a power amplifier by using Volterra Kernel series with memory effects:
y
⁡
[
n
]
=
∑
l
⁢
⁢
w
l
⁢
U
l
⁡
(
x
⁡
[
n
]
)
where:
y[n] is the power amplifier output at sampling time n, x[n] is PA input at sampling time n;
Ul(x[n]) is general Volterra kernel with memory effects expressed in apolynomial form;
and wl is a corresponding coefficient;
searching for the dominant Volterra Kernel with the minimum residue error by letting x[n] be the first new build orthogonal base e1[n]=x[n]
Step 1, calculating a residual error of using e1[n] to model y[n] as follows:
y_error
⁡
[
n
]
1
=
y
⁡
[
n
]
-
〈
y
⁡
[
n
]
,
e
1
⁡
[
n
]
〉
〈
e
1
⁡
[
n
]
,
e
1
⁡
[
n
]
〉
⁢
e
1
⁡
[
n
]
where:
<x,y> denotes x and y cross-correlation,
Step 2, building an innovation base for each of the Volterra Kernels versus e1(n) as follows:
{
U
l
⁡
(
x
⁡
[
n
]
)
}
innovation
=
U
l
⁡
(
x
⁡
[
n
]
)
-
〈
U
l
⁡
(
x
⁡
[
n
]
)
,
e
1
⁡
[
N
]
〉
〈
e
1
⁡
[
N
]
,
e
1
⁡
[
N
]
〉
⁢
e
1
⁡
[
N
]
,
l
=
2
,
3
,
4
⁢
⁢
…
Step 3, calculating the residual error of using each innovation base and selecting an innovative base with the minimum residual error as the new orthogonal base e2[n], putting the corresponding Volterra kernel into a candidate list for the next update of the Volterra kernels set for the DPD engine as follows:
y_error
⁡
[
n
]
1
l
=
y_error
⁡
[
n
]
1
-
〈
y_error
⁡
[
n
]
1
,
{
U
l
(
x
⁡
[
n
]
}
innovation
〉
〈
{
U
l
(
x
⁡
[
n
]
}
innovation
,
{
U
l
(
x
⁡
[
n
]
}
innovation
〉
⁢
⁢
{
U
l
(
x
⁡
[
n
]
}
innovation
,
⁢
l
-
2
,
3
,
4
,
5
⁢
⁢
…
⁢
⁢
⁢
e
2
⁡
[
n
]
=
{
U
k
(
x
⁡
[
n
]
}
innovation
,
k
=
min
⁢
{
〈
y_error
⁡
[
n
]
1
l
,
y_error
⁡
[
n
]
1
l
〉
}
⁢
⁢
⁢
y_error
⁡
[
n
]
2
=
y_error
⁡
[
n
]
1
k
where:
Volterra Kernels Candidate is {U1, Uk} and Uk is removed from search candidate list, and
repeating step 1, step 2 and step 3 to generate a new orthogonal base list [e1, e2, e3 . . . ] and a dominant Volterra Kernel candidates list [U1, Uk . . . ];
and
continuously updating the digital pre distortion process with the optimal Volterra Kernel set.

311 more elements

Dependencies are inferred from the claim text.

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Abstract

A method of Digital Pre Distortion (DPD) comprising the steps of:

modeling the non linear behavior of a power amplifier by using Volterra Kernel series with memory effects:
y
⁡
[
n
]
=
∑
l
⁢
⁢
w
l
⁢
U
l
⁡
(
x
⁡
[
n
]
)
where:
y[n] is the power amplifier output at sampling time n, x[n] is PA input at sampling time n;
Ul(x[n]) is general Volterra kernel with memory effects expressed in apolynomial form;
and wl is a corresponding coefficient;
searching for the dominant Volterra Kernel with the minimum residue error by letting x[n] be the first new build orthogonal base e1[n]=x[n]
Step 1, calculating a residual error of using e1[n] to model y[n] as follows:
y_error
⁡
[
n
]
1
=
y
⁡
[
n
]
-
〈
y
⁡
[
n
]
,
e
1
⁡
[
n
]
〉
〈
e
1
⁡
[
n
]
,
e
1
⁡
[
n
]
〉
⁢
e
1
⁡
[
n
]
where:
<x,y> denotes x and y cross-correlation,
Step 2, building an innovation base for each of the Volterra Kernels versus e1(n) as follows:
{
U
l
⁡
(
x
⁡
[
n
]
)
}
innovation
=
U
l
⁡
(
x
⁡
[
n
]
)
-
〈
U
l
⁡
(
x
⁡
[
n
]
)
,
e
1
⁡
[
N
]
〉
〈
e
1
⁡
[
N
]
,
e
1
⁡
[
N
]
〉
⁢
e
1
⁡
[
N
]
,
l
=
2
,
3
,
4
⁢
⁢
…
Step 3, calculating the residual error of using each innovation base and selecting an innovative base with the minimum residual error as the new orthogonal base e2[n], putting the corresponding Volterra kernel into a candidate list for the next update of the Volterra kernels set for the DPD engine as follows:
y_error
⁡
[
n
]
1
l
=
y_error
⁡
[
n
]
1
-
〈
y_error
⁡
[
n
]
1
,
{
U
l
(
x
⁡
[
n
]
}
innovation
〉
〈
{
U
l
(
x
⁡
[
n
]
}
innovation
,
{
U
l
(
x
⁡
[
n
]
}
innovation
〉
⁢
⁢
{
U
l
(
x
⁡
[
n
]
}
innovation
,
⁢
l
-
2
,
3
,
4
,
5
⁢
⁢
…
⁢
⁢
⁢
e
2
⁡
[
n
]
=
{
U
k
(
x
⁡
[
n
]
}
innovation
,
k
=
min
⁢
{
〈
y_error
⁡
[
n
]
1
l
,
y_error
⁡
[
n
]
1
l
〉
}
⁢
⁢
⁢
y_error
⁡
[
n
]
2
=
y_error
⁡
[
n
]
1
k
where:
Volterra Kernels Candidate is {U1, Uk} and Uk is removed from search candidate list, and
repeating step 1, step 2 and step 3 to generate a new orthogonal base list [e1, e2, e3 . . . ] and a dominant Volterra Kernel candidates list [U1, Uk . . . ];
and
continuously updating the digital pre distortion process with the optimal Volterra Kernel set.

311 more elements