dpdPreprocessor
Description
The dpdPreprocessor
System object™ configures a DPD input data preprocessor object to generate an augmented input
for AIbased DPDs and power amplifiers (PAs). For more information, see Algorithms.
To configure a DPD input data preprocessor for AI algorithms:
Create the
dpdPreprocessor
object and set its properties.Call the object with arguments, as if it were a function.
To learn more about how System objects work, see What Are System Objects?
Creation
Description
returns a DPD input
data preprocessor object to generate an augmented input for AIbased DPDs and PAs.dpdIn
= dpdPreprocessor
returns a DPD input data preprocessor object with the dpdIn
= dpdPreprocessor(M
,K
)MemoryDepth
property set to M
and the Degree
property set to
K
.
creates a DPD preprocessor object with the specified property dpdIn
= dpdPreprocessor(Name
=Value
)Name
set
to the specified Value
. For example,
dpdPreprocessor(Degree=2)
specifies a DPD preprocessor object with
the DPD nonlinearity degree set to 2. You can specify additional namevalue arguments in
any order as
(Name1
,Value1
,...,NameN
,ValueN
).
Properties
Usage
Syntax
Input Arguments
Output Arguments
Object Functions
To use an object function, specify the
System object as the first input argument. For
example, to release system resources of a System object named obj
, use
this syntax:
release(obj)
Examples
Algorithms
As described in [1], this object implements a data augmenter for use by AIbased DPD and PA models such as the augmented realvalued timedelay neural network (ARVTDNN).
The augmented input consists of Cartesian inphase and quadrature phase
(IQ) components, as well as envelopedependent terms. For
IQ samples (timedelayed samples and current sample
I[n], Q[n]) and the amplitudes of the sample X
[n],
X
[n]^{2}, ...,
X
[n]^{(K – 1)}, the
augmented input is a concatenation of these terms:
I[n – M + 1], I[n – M+2], ..., I[n]
Q[n – M + 1], Q[n – M+3], ..., Q[n]

X
[n – M+1], X
[n – M+2], ..., X
[n]...

X
[n – M+1]^{(K – 1)}, X
[n – M+2]^{(K – 1)}, ..., X
[n]^{(K – 1)}If
X
is a column vector of size Nby1, then the output,Y
, is a matrix with size NbyP, where P is the number of input features.If K and M are scalars, then P = M×(2 + (K  1)).
If K is a vector, then P = M×(2 + length(K)).
Where:
N is the number of rows in input
X
.P is the number of features. The
info
object function returns characteristic information in a structure that indicates the number of features.K is the value of the
Degree
property.M is the value of the
MemoryDepth
property.
References
[1] D. Wang, M. Aziz, M. Helaoui, and F. M. Ghannouchi, "Augmented RealValued TimeDelay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters," IEEE Transactions on Neural Networks and Learning Systems 30, no. 1 (January 2019): pp. 242–54. https://doi.org/10.1109/TNNLS.2018.2838039.
Extended Capabilities
Version History
Introduced in R2024b