US 11,451,419 B2Granted

Integrating volterra series model and deep neural networks to equalize nonlinear power amplifiers

AssigneeThe Research Foundation for The State University

InventorsXiaohua Li, Robert Thompson

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Abstract

The nonlinearity of power amplifiers (PAs) has been a severe constraint in performance of modern wireless transceivers. This problem is even more challenging for the fifth generation (5G) cellular system since 5G signals have extremely high peak to average power ratio. Non-linear equalizers that exploit both deep neural networks (DNNs) and Volterra series models are provided to mitigate PA nonlinear distortions. The DNN equalizer architecture consists of multiple convolutional layers. The input features are designed according to the Volterra series model of nonlinear PAs. This enables the DNN equalizer to effectively mitigate nonlinear PA distortions while avoiding over-fitting under limited training data. The non-linear equalizers demonstrate superior performance over conventional nonlinear equalization approaches.

Claims20

Dependent claims

A distortion-compensating processor, comprising:

an input port configured to receive a distorted signal y(n) representing information x(n) distorted by a channel h;
at least one automated processor, configured to:
decompose the distorted signal y(n) as a Volterra series expansion
y
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(
n
)
=
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d
=
0
D
∑
k
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P
b
k
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d
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x
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❘
"\[LeftBracketingBar]"
x
⁡
(
n
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d
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❘
"\[RightBracketingBar]"
k
-
1
;
and
implement an equalizer operating on separate terms of the Volterra series expansion to produce an output z(n) having reduced distortion with respect to the distorted signal y(n),
the equalizer comprising a deep neural network comprising a plurality of neural network hidden layers trained with respect to distortion of the channel h to provide an input-output response and
z
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1
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0
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…
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=
0
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f
d
1
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…
,
d
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∏
i
=
1
k
r
⁡
(
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-
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)
;
and
an output port configured to present the output,
wherein:
r(n) is a response of the channel h to the nonlinearly distorted radio frequency signal y(n);
p is a respective nonlinearity order;
d is a memory depth parameter;
D is a total memory length;
P is a total nonlinearity order;
k is a nonlinear order;
and
bkd are nonlinear response parameters.

106 more elements

A method of compensating for a distortion, comprising:

receiving a distorted signal y(n) representing information x(n) distorted and communicated through a channel h;
decomposing the distorted signal y(n) with a Volterra processor to produce a Volterra series expansion of form
y
⁡
(
n
)
=
∑
d
=
0
D
∑
k
=
0
P
b
k
⁢
d
⁢
x
⁡
(
n
-
d
)
⁢
❘
"\[LeftBracketingBar]"
x
⁡
(
n
-
d
)
❘
"\[RightBracketingBar]"
k
-
1
;
and
equalizing the distorted signal y(n) with an automated nonlinear equalizer, comprising a deep neural network having a plurality of neural network hidden layers trained with respect to distortion of the channel h to have an input-output response
z
⁡
(
n
)
=
∑
k
=
1
P
∑
d
1
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0
D
…
⁢
∑
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k
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0
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f
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…
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∏
i
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1
k
r
⁡
(
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)
,
which receives the Volterra series expansion, and produces an output z(n) having reduced distortion with respect to the distorted signal y(n),
wherein:
r(n) is a response of channel h to the nonlinearly distorted radio frequency signal y(n);
p is a respective nonlinearity order;
d is a memory depth parameter;
D is a total memory length;
P is a total nonlinearity order;
k is a nonlinear order;
and
bkd are nonlinear response parameters.

104 more elements

A non-linear distortion-compensating processor,

comprising:
an input configured to receive a non-linearly distorted radio frequency signal r(n) representing information x(n) distorted by a radio frequency communication system to produce a distorted signal y(n) communicated through a channel h;
at least one automated processor, configured to:
decompose the non-linearly distorted radio frequency signal r(n) received from the channel h as a Volterra series expansion
y
⁡
(
n
)
=
∑
d
=
0
D
∑
k
=
0
P
b
k
⁢
d
⁢
x
⁡
(
n
-
d
)
⁢
❘
"\[LeftBracketingBar]"
x
⁡
(
n
-
d
)
❘
"\[RightBracketingBar]"
k
-
1
;
process the Volterra series expansion with a deep neural network comprising a plurality of neural network hidden layers trained with respect to the distortion of the radio frequency communication system comprising the non-linear distortion and channel distortion, to equalize the distortion, the process having an input-output response
z
⁡
(
n
)
=
∑
k
=
1
P
∑
d
1
=
0
D
…
⁢
∑
d
k
=
0
D
⁢
f
d
1
,
⁢
…
,
d
k
⁢
∏
i
=
1
k
r
⁡
(
n
-
d
i
)
;
and
demodulate z(n) to extract the information x(n) modulated in the non-linearly distorted radio frequency signal r(n),
wherein:
p is a respective nonlinearity order;
d is a memory depth parameter;
D is a total memory length;
P is a total nonlinearity order;
k is a nonlinear order;
and
bkd are nonlinear response parameters.

106 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/234,102Filed 19 Apr 2021 · continuation
Priority: 15 Mar 2019
Published: US 2021/0243056 A15 Aug 2021
Granted: 20 Sep 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification17 codes

H04L 25/03165
Arrangements for removing intersymbol interference: using neural networks
G06N 3/04
Neural networks: architecture, e.g. interconnection topology
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]

Family

Application62/819,054Us provisional application · filed 15 Mar 2019
Application16/812,229Parent of this continuation · filed 6 Mar 2020
US 10,985,951 B2Parent of this continuation · filed 20 Apr 2021
US 2021/0243056 A1Pre-grant publication · published 5 Aug 2021
US 2020/0295975 A1Published application
US 11,451,419 B2This patent

From the US filing history. Foreign family members aren’t listed.

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