comm.MemorylessNonlinearity

Apply memoryless nonlinearity to complex baseband signal

Description

The comm.MemorylessNonlinearity System object™ applies memoryless nonlinear impairments to a complex baseband signal. Use this System object to model memoryless nonlinear impairments caused by signal amplification in a radio frequency (RF) transmitter or receiver. For more information, see Memoryless Nonlinear Impairments.

Note

All values of power assume a nominal impedance of 1 ohm.

To apply memoryless nonlinear impairments to a complex baseband signal:

Create the comm.MemorylessNonlinearity 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

Syntax

mnl = comm.MemorylessNonlinearity

mnl = comm.MemorylessNonlinearity(Name,Value)

Description

mnl = comm.MemorylessNonlinearity creates a memoryless nonlinearity System object that models RF nonlinear impairments.

example

mnl = comm.MemorylessNonlinearity(Name,Value) specifies properties using one or more name-value pair arguments. Enclose each property name in quotes. For example, 'Method','Saleh model' sets the modeling method to the Saleh method.

Properties

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Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

`Method` — Nonlinearity modeling method
`'Cubic polynomial'` (default) | `'Hyperbolic tangent'` | `'Saleh model'` | `'Ghorbani model'` | `'Rapp model'` | `'Lookup table'`

Nonlinearity modeling method, specified as 'Cubic polynomial', 'Hyperbolic tangent', 'Saleh model', 'Ghorbani model', 'Rapp model', or 'Lookup table'. For more information, see Memoryless Nonlinear Impairments.

Data Types: char | string

`InputScaling` — Input signal scaling factor
`0` (default) | scalar

Input signal scaling factor in decibels, specified as a scalar. This property scales the power gain of the input signal.

Tunable: Yes

Dependencies

To enable this property, set the Method property to 'Saleh model' or 'Ghorbani model'.

Data Types: double

`LinearGain` — Linear gain
`0` (default) | scalar

Linear gain in decibels, specified as a scalar. This property scales the power gain of the output signal.

Tunable: Yes

Dependencies

To enable this property, set the Method property to 'Cubic polynomial', 'Hyperbolic tangent', or 'Rapp model'.

Data Types: double

`IIP3` — Third-order input intercept point
`30` (default) | scalar

Third-order input intercept point in dBm, specified as a scalar.

Tunable: Yes

Dependencies

To enable this property, set the Method property to 'Cubic polynomial' or 'Hyperbolic tangent'.

Data Types: double

`AMPMConversion` — AM/PM conversion factor
`10` (default) | scalar

AM/PM conversion factor in degrees per decibel, specified as a scalar. For more information, see Cubic Polynomial and Hyperbolic Tangent Model Methods.

Tunable: Yes

Dependencies

To enable this property, set the Method property to 'Cubic polynomial' or 'Hyperbolic tangent'.

Data Types: double

`AMAMParameters` — AM/AM parameters
`[2.1587 1.1517]` | `[8.1081 1.5413 6.5202 -0.0718]` | row vector

AM/AM parameters used to compute the amplitude gain for an input signal, specified as a row vector.

When the Method property is set to 'Saleh model', this property must be a two-element vector that specifies alpha and beta values. In this case, the default value is [2.1587 1.1517].
When the Method property is set to 'Ghorbani model', this property must be a four-element vector that specifies x₁, x₂, x₃, and x₄ values. In this case, the default value is [8.1081 1.5413 6.5202 -0.0718].

For more information, see Saleh Model Method and Ghorbani Model Method.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Saleh model' or 'Ghorbani model'.

Data Types: double

`AMPMParameters` — AM/PM parameters
`[4.0033 9.1040]` | `[4.6645 2.0965 10.88 -0.003]` | row vector

AM/PM parameters used to compute the phase change for an input signal, specified as a row vector.

When the Method property is set to 'Saleh model', this property must be a two-element vector that specifies alpha and beta values. In this case, the default value is [4.0033 9.1040].
When the Method property is set to 'Ghorbani model', this property must be a four-element vector that specifies y₁, y₂, y₃, and y₄ values. In this case, the default value is [4.6645 2.0965 10.88 -0.003]

For more information, see Saleh Model Method and Ghorbani Model Method.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Saleh model' or 'Ghorbani model'.

Data Types: double

`PowerLowerLimit` — Input power lower limit
`10` (default) | scalar

Input power lower limit in dBm, specified as a scalar less than the PowerUpperLimit property value. The AM/PM conversion scales linearly for input power values in the range [PowerLowerLimit, PowerUpperLimit]. If the input signal power is below the input power lower limit, the phase shift resulting from AM/PM conversion is zero. For more information, see Cubic Polynomial and Hyperbolic Tangent Model Methods.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Cubic polynomial' or 'Hyperbolic tangent'.

Data Types: double

`PowerUpperLimit` — Input power upper limit
`inf` (default) | scalar

Input power upper limit in dBm, specified as a scalar greater than PowerLowerLimit. The AM/PM conversion scales linearly for input power values in the range [PowerLowerLimit, PowerUpperLimit]. If the input signal power is above the input power upper limit, the phase shift resulting from AM/PM conversion is constant. For more information, see Cubic Polynomial and Hyperbolic Tangent Model Methods.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Cubic polynomial' or 'Hyperbolic tangent'.

Data Types: double

`OutputScaling` — Output signal scaling factor
`0` (default) | scalar

Output signal scaling factor in decibels, specified as a scalar. This property scales the power gain of the output signal.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Saleh model' or 'Ghorbani model'.

Data Types: double

`Smoothness` — Smoothness factor
`0.5` (default) | scalar

Smoothness factor, specified as a scalar. For more information, see Rapp Model Method.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Rapp model'.

Data Types: double

`OutputSaturationLevel` — Output saturation level
`1` (default) | scalar

Output saturation level, specified as a scalar. For more information, see Rapp Model Method.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Rapp model'.

Data Types: double

`Table` — Amplifier characteristics lookup table
N-by-[P_in, P_out, ΔΦ] matrix

Amplifier characteristics lookup table, specified as an N-by-3 matrix of measured power amplifier (PA) characteristics. Each row is of the form [P_in, P_out, ΔΦ]. P_in specifies the PA input signal in dBm, P_out specifies the PA output signal in dBm, and ΔΦ specifies the output phase shift in degrees. The default value is [-25, 5.16, -0.25; -20, 10.11, -0.47; -15, 15.11, -0.68; -10, 20.05, -0.89; -5, 24.79, -1.22; 0, 27.64, 5.59; 5, 28.49, 12.03].

The measured PA characteristics defined by this property are used to compute the AM/AM (in dBm/dBm) and AM/PM (in deg/dBm) nonlinear impairment characteristics. The System object distorts the input signal by the computed AM/AM (in dBm/dBm) and AM/PM (in deg/dBm) values.

Note

To determine appropriate P_out and ΔΦ for P_in values outside the range of values specified in the Table property, the System object applies linear extrapolation from the first two or last two [P_in, P_out, ΔΦ] rows of Table.

Tunable: Yes

Dependencies

To enable this property, set the Method property is set to 'Lookup table'.

Data Types: double

Usage

Syntax

outsig = mnl(insig)

Description

example

outsig = mnl(insig) applies memoryless nonlinear impairments to the input RF baseband signal.

Input Arguments

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`insig` — Input RF baseband signal
scalar | column vector

Input RF baseband signal, specified as a scalar or column vector. Values in this input must be complex.

Data Types: double
Complex Number Support: Yes

Output Arguments

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`outsig` — Output RF baseband signal
scalar | column vector

Output RF baseband signal, returned as a scalar or column vector. The output is of the same data type as the input.

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)

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Common to All System Objects

`step`	Run System object algorithm
`release`	Release resources and allow changes to System object property values and input characteristics
`reset`	Reset internal states of System object

Examples

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Apply Saleh Model of Power Amplifier Nonlinearity to 16-QAM Signal

Open Live Script

Generate 16-QAM data with an average power of 10 mW and pass the data through a nonlinear power amplifier (PA).

M = 16;
data = randi([0 (M - 1)]',1000,1);
avgPow = 1e-2;
minD = avgPow2MinD(avgPow,M);

Create a memoryless nonlinearity System object, specifying the Saleh model method.

saleh = comm.MemorylessNonlinearity('Method','Saleh model');

Generate modulated symbols and pass them through the PA nonlinearity model.

modData = (minD/2).*qammod(data,M);
y = saleh(modData);

Generate a scatter plot of the results.

scatterplot(y)

Average power normalization of input signal.

function minD = avgPow2MinD(avgPow,M)
    % Average power to minimum distance    
    nBits = log2(M);
    if (mod(nBits,2)==0)
        % Square QAM
        sf = (M - 1)/6;
    else
        % Cross QAM
        if (nBits>4)
            sf = ((31*M/32) - 1)/6;
        else
            sf = ((5*M/4) - 1)/6;
        end
    end
    minD = sqrt(avgPow/sf);
end

Nonlinear Amplifier Gain Compression

Open Script

Plot the gain compression of a nonlinear amplifier for a 16-QAM signal.

Specify the modulation order and samples per symbol parameters.

M = 16;
sps = 4;

Model a nonlinear amplifier, by creating a memoryless nonlinearity System object with a 30 dB third-order input intercept point. Create a raised cosine transmit filter System object.

amplifier = comm.MemorylessNonlinearity('IIP3',30);

txfilter = comm.RaisedCosineTransmitFilter('RolloffFactor',0.3, ...
    'FilterSpanInSymbols',6, ...
    'OutputSamplesPerSymbol',sps, ...
    'Gain',sqrt(sps));

Specify the input power in dBm. Convert the input power to W and initialize the gain vector.

pindBm = -5:25;
pin = 10.^((pindBm-30)/10);
gain = zeros(length(pindBm),1);

Execute the main processing loop, which includes these steps.

Generate random data symbols.
Modulate the data symbols and adjust the average power of the signal.
Filter the modulated signal.
Amplify the signal.
Measure the gain.

for k = 1:length(pin)
    data = randi([0 (M - 1)],1000,1);
    modSig = qammod(data,M,'UnitAveragePower',true)*sqrt(pin(k));
    filtSig = txfilter(modSig);
    ampSig = amplifier(filtSig);
    gain(k) = 10*log10(var(ampSig)/var(filtSig));
end

Plot the amplifier gain as a function of the input signal power. The 1 dB gain compression point occurs for an input power of 18.5 dBm. To increase the point at which a 1 dB compression is observed, increase the third-order intercept point, amplifier.IIP3.

arrayplot = dsp.ArrayPlot('PlotType','Line','XLabel','Power In (dBm)', ...
    'XOffset',-5,'YLimits',[-5 5]);

arrayplot(gain)

Distort 16-QAM Signal with Custom Power Amplifier Nonlinearities

Open Live Script

Apply nonlinear power amplifier (PA) characteristics to a 16-QAM signal by setting the Method property to 'Lookup table'.

Define parameters for the modulation order, samples per symbol, and input power. Create random data.

M = 16; % Modulation order
sps = 4; % Samples per symbol
pindBm = -2; % Input power
pin = 10.^((pindBm-30)/10); % power in Watts
data = randi([0 (M - 1)],1000,1);
refdata = 0:M-1;
refconst = qammod(refdata,M,'UnitAveragePower',true);

Create a memoryless nonlinearity System object, a transmit filter System object, and a constellation diagram System object. The default lookup table values are used for the memoryless nonlinearity System object.

amplifier = comm.MemorylessNonlinearity('Method','Lookup table');
txfilter = comm.RaisedCosineTransmitFilter('RolloffFactor',0.3, ...
    'FilterSpanInSymbols',6,'OutputSamplesPerSymbol',sps,'Gain',sqrt(sps));
constellation = comm.ConstellationDiagram('SamplesPerSymbol',4,'ReferenceConstellation',refconst, ...
    'Title','Amplified/Distorted Signal');

Modulate the random data. Filter and apply the nonlinear amplifier characteristics to the modulation symbols.

modSig = qammod(data,M,'UnitAveragePower',true)*sqrt(pin);
filtSig = txfilter(modSig);
ampSig = amplifier(filtSig);

Compute input and output signal levels and the phase shift.

poutdBm = (20*log10(abs(ampSig))) + 30;
simulated_pindBm = (20*log10(abs(filtSig))) + 30;
phase = angle(ampSig.*conj(filtSig))*180/pi;

Plot AM/AM characteristics, AM/PM characteristics, and the constellation results.

figure
set(gcf,'units','normalized','position',[.25 1/3 .5 1/3])
subplot(1,2,1)
plot(simulated_pindBm,poutdBm,'.'); 
hold on
plot(amplifier.Table(:,1),amplifier.Table(:,2),'.','Markersize',15);
xlabel('Input Power (dBm)')
ylabel('Output Power (dBm)');
grid on; 
title('AM/AM Characteristics');
leglabels = {'Simulated results','Measurement'};
legend (leglabels,'Location','north');

subplot(1,2,2)
plot(simulated_pindBm,phase,'.');
hold on
plot(amplifier.Table(:,1),amplifier.Table(:,3),'.','Markersize',15);
legend (leglabels,'Location','north');
xlabel('Input Power (dBm)'); 
ylabel('Output Phase Shift (degrees)');
grid on; title('AM/PM Characteristics');

Generate a constellation diagram of the amplified signal and reference constellation. The nonlinear amplifier characteristics cause compression of the amplified signal constellation compared to the reference constellation.

constellation(ampSig)

Distort 16-QAM Signal with Measured Power Amplifier Nonlinearities

Open Live Script

Apply nonlinear power amplifier (PA) characteristics to a 16-QAM signal by setting the Method property to 'Lookup table'.

Define parameters for the modulation order, samples per symbol, and input power. Create random data.

M = 16; % Modulation order
sps = 4; % Samples per symbol
pindBm = -8; % Input power
pin = 10.^((pindBm-30)/10); % power in Watts
data = randi([0 (M - 1)],1000,1);
refdata = 0:M-1;
refconst = qammod(refdata,M,'UnitAveragePower',true);
paChar = pa_performance_characteristics();

amplifier = comm.MemorylessNonlinearity('Method','Lookup table','Table',paChar);
txfilter = comm.RaisedCosineTransmitFilter('RolloffFactor',0.3, ...
    'FilterSpanInSymbols',6,'OutputSamplesPerSymbol',sps,'Gain',sqrt(sps));
constellation = comm.ConstellationDiagram('SamplesPerSymbol',4, ...
    'Title','Amplified/Distorted Signal','NumInputPorts',2, ...
    'ReferenceConstellation', refconst,'ShowLegend',true, ...
    'ChannelNames',{'Filtered signal','Amplified signal'});

Modulate the random data. Filter and apply the nonlinear amplifier characteristics to the modulation symbols.

modSig = qammod(data,M,'UnitAveragePower',true)*sqrt(pin);
filtSig = txfilter(modSig);
ampSig = amplifier(filtSig);

Compute input and output signal levels and the phase shift.

poutdBm = (20*log10(abs(ampSig))) + 30;
simulated_pindBm = (20*log10(abs(filtSig))) + 30;
phase = angle(ampSig.*conj(filtSig))*180/pi;

Plot AM/AM characteristics, AM/PM characteristics, and the constellation results.

figure
set(gcf,'units','normalized','position',[.25 1/3 .5 1/3])
subplot(1,2,1)
plot(simulated_pindBm,poutdBm,'.');
hold on
plot(amplifier.Table(:,1),amplifier.Table(:,2),'.','Markersize',15);
xlabel('Input Power (dBm)')
ylabel('Output Power (dBm)');
grid on;
title('AM/AM Characteristics');
leglabel = {'Simulated results','Measurement'};
legend (leglabel,'Location','south');

subplot(1,2,2)
plot(simulated_pindBm,phase,'.');
hold on
plot(amplifier.Table(:,1),amplifier.Table(:,3),'.','Markersize',15);
legend (leglabel,'Location','south');
xlabel('Input Power (dBm)');
ylabel('Output Phase Shift (degrees)');
grid on;
title('AM/PM Characteristics');

For the purpose of constellation comparison, normalize the amplified signal and the filtered signal. Generate a constellation diagram of the filtered signal and amplified signal. The nonlinear amplifier characteristics cause compression of the amplified signal constellation compared to the filtered constellation.

filtSig = filtSig/mean(abs(filtSig)); % Normalized filtered signal
ampSig = ampSig/mean(abs(ampSig)); % Normalized amplified signal
constellation(filtSig,ampSig)

The pa_performance_characteristics helper function calculates the amplifier performance characteristics. The data is extracted from figure 4 of Hammi, Oualid, et al. "Power amplifiers' model assessment and memory effects intensity quantification using memoryless post-compensation technique." IEEE Transactions on Microwave Theory and Techniques 56.12 (2008): 3170-3179.

function paChar = pa_performance_characteristics()

The operating specification for the LDMOS-based Doherty amplifier are:

A frequency of 2110 MHz
A peak power of 300 W
A small signal gain of 61 dB

Each row in HAV08_Table specifies Pin (dBm), gain (dB), phase shift (degrees).

HAV08_Table =...
    [-35,60.53,0.01;
    -34,60.53,0.01;
    -33,60.53,0.08;
    -32,60.54,0.08;
    -31,60.55,0.1;
    -30,60.56,0.08;
    -29,60.57,0.14;
    -28,60.59,0.19;
    -27,60.6,0.23;
    -26,60.64,0.21;
    -25,60.69,0.28;
    -24,60.76,0.21;
    -23,60.85,0.12;
    -22,60.97,0.08;
    -21,61.12,-0.13;
    -20,61.31,-0.44;
    -19,61.52,-0.94;
    -18,61.76,-1.59;
    -17,62.01,-2.73;
    -16,62.25,-4.31;
    -15,62.47,-6.85;
    -14,62.56,-9.82;
    -13,62.47,-12.29;
    -12,62.31,-13.82;
    -11,62.2,-15.03;
    -10,62.15,-16.27;
    -9,62,-18.05;
    -8,61.53,-20.21;
    -7,60.93,-23.38;
    -6,60.2,-26.64;
    -5,59.38,-28.75];

Convert the second column of the HAV08_Table from gain to Pout for use by the memoryless nonlinearity System object.

paChar = HAV08_Table;
paChar(:,2) = paChar(:,1) + paChar(:,2);
end

More About

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Memoryless Nonlinear Impairments

Memoryless nonlinear impairments distort the input signal amplitude and phase. The amplitude distortion is amplitude-to-amplitude modulation (AM/AM) and the phase distortion is amplitude-to-phase modulation (AM/PM).

Model Method	Memoryless Nonlinear Impairment
Cubic polynomial	AM/AM and AM/PM
Hyperbolic tangent
Saleh model
Ghorbani model
Rapp model	AM/AM only
Lookup table	Applies impairment according to [P_in, P_out, ΔΦ] amplifier characteristics specified by the `Table` property

The modeled impairments apply the AM/AM and AM/PM distortions differently, according to the model method you specify. The models apply the memoryless nonlinear impairment to the input signal by following these steps.

Multiply the signal by an input gain factor.

Note
You can normalize the signal to 1 by setting the input scaling gain to the inverse of the input signal amplitude.
Split the complex signal into its magnitude and angle components.
Apply an AM/AM distortion to the magnitude of the signal, according to the selected model method, to produce the magnitude of the output signal.
Apply an AM/PM distortion to the phase of the signal, according to the selected model method, to produce the angle of the output signal.

Note
This step does not apply for the Rapp model.
Combine the new magnitude and angle components into a complex signal. Then, multiply the result by an output gain factor.

The first four model methods (cubic polynomial, hyperbolic tangent, Saleh model, and Ghorbani model) apply AM/AM and AM/PM impairments as shown in this figure.

The Rapp model method applies AM/AM distortion as shown in this figure.

The lookup table method uses the power amplifier (PA) characteristics lookup table, specified as an N-by-3 matrix of measured power amplifier (PA) characteristics. Each row is of the form [P_in, P_out, ΔΦ]. P_in specifies the PA input signal in dBm, P_out specifies the PA output signal in dBm, and ΔΦ specifies the output phase shift in degrees. The measured PA characteristics defined by the Table property are used to compute the AM/AM (in dBm/dBm) and AM/PM (in deg/dBm) nonlinear impairment characteristics. The System object distorts the input signal by the computed AM/AM (in dBm/dBm) and AM/PM (in deg/dBm) values.

Note

Cubic Polynomial and Hyperbolic Tangent Model Methods

This figure shows the AM/PM conversion behavior for the cubic polynomial and hyperbolic tangent model methods.

The AM/PM conversion scales linearly with an input power value between the lower and upper limits of the input power level. Outside this range, the AM/PM conversion is constant at the values corresponding to the lower and upper input power limits, which are zero and (AM/PM conversion) × (upper input power limit – lower input power limit), respectively.

Saleh Model Method

This figure shows the AM/AM behavior (output voltage versus input voltage for the AM/AM distortion) and the AM/PM behavior (output phase versus input voltage for the AM/PM distortion) for the Saleh model method.

The AM/AM parameters, α_AMAM and β_AMAM, are used to compute the amplitude distortion of the input signal by using

$F_{AMAM} (u) = \frac{α_{AMAM} \times u}{1 + β_{AMAM} \times u^{2}},$

where u is the magnitude of the scaled signal.

The AM/PM parameters, α_AMPM and β_AMPM, are used to compute the phase distortion of the input signal by using

$F_{AMPM} (u) = \frac{α_{AMPM} \times u^{2}}{1 + β_{AMPM} \times u^{2}},$

where u is the magnitude of the scaled signal. The α and β parameters for AM/AM and AM/PM are similarly named but distinct.

Ghorbani Model Method

The Ghorbani model method applies AM/AM and AM/PM distortion as described in this section.

The AM/AM parameters (x₁, x₂, x₃, and x₄) are used to compute the amplitude distortion of the input signal by using

$F_{AMAM} (u) = \frac{x_{1} u^{x_{2}}}{1 + x_{3} u^{x_{2}}} + x_{4} u,$

where u is the magnitude of the scaled signal.

The AM/PM parameters (y₁, y₂, y₃, and y₄) are used to compute the phase distortion of the input signal by using

$F_{AMPM} (u) = \frac{y_{1} u^{y_{2}}}{1 + y_{3} u^{y_{2}}} + y_{4} u,$

where u is the magnitude of the scaled signal.

Rapp Model Method

The Rapp model method applies AM/AM distortion as described in this section. The Rapp model does not apply AM/PM distortion to the input signal.

The smoothness factor and output saturation level are used to compute the amplitude distortion of the input signal given by

$F_{AMAM} (u) = \frac{u}{{(1 + {(\frac{u}{O_{sat}})}^{2S})}^{1/2S}},$

where

u is the magnitude of the scaled signal.
S is the smoothness factor.
O_sat is the output saturation level.

References

[1] Saleh, A.A.M. “Frequency-Independent and Frequency-Dependent Nonlinear Models of TWT Amplifiers.” IEEE Transactions on Communications 29, no. 11 (November 1981): 1715–20. https://doi.org/10.1109/TCOM.1981.1094911.

[2] Ghorbani, A., and M. Sheikhan. "The Effect of Solid State Power Amplifiers (SSPAs) Nonlinearities on MPSK and M-QAM Signal Transmission." In 1991 Sixth International Conference on Digital Processing of Signals in Communications, 193–97, 1991.

[3] Rapp, Ch. "Effects of HPA-Nonlinearity on a 4-DPSK/OFDM-Signal for a Digital Sound Broadcasting System." In Proceedings Second European Conf. on Sat. Comm. (ESA SP-332), 179–84. Liege, Belgium, 1991. https://elib.dlr.de/33776/.

Documentation

comm.MemorylessNonlinearity

Description

Creation

Syntax

Description

Properties

Method — Nonlinearity modeling method 'Cubic polynomial' (default) | 'Hyperbolic tangent' | 'Saleh model' | 'Ghorbani model' | 'Rapp model' | 'Lookup table'

InputScaling — Input signal scaling factor 0 (default) | scalar

Dependencies

LinearGain — Linear gain 0 (default) | scalar

Dependencies

IIP3 — Third-order input intercept point 30 (default) | scalar

Dependencies

AMPMConversion — AM/PM conversion factor 10 (default) | scalar

Dependencies

AMAMParameters — AM/AM parameters [2.1587 1.1517] | [8.1081 1.5413 6.5202 -0.0718] | row vector

Dependencies

AMPMParameters — AM/PM parameters [4.0033 9.1040] | [4.6645 2.0965 10.88 -0.003] | row vector

Dependencies

PowerLowerLimit — Input power lower limit 10 (default) | scalar

Dependencies

PowerUpperLimit — Input power upper limit inf (default) | scalar

Dependencies

OutputScaling — Output signal scaling factor 0 (default) | scalar

Dependencies

Smoothness — Smoothness factor 0.5 (default) | scalar

Dependencies

OutputSaturationLevel — Output saturation level 1 (default) | scalar

Dependencies

Table — Amplifier characteristics lookup table N-by-[Pin, Pout, ΔΦ] matrix

Dependencies

Usage

Syntax

Description

Input Arguments

insig — Input RF baseband signal scalar | column vector

Output Arguments

outsig — Output RF baseband signal scalar | column vector

Object Functions

Common to All System Objects

Examples

Apply Saleh Model of Power Amplifier Nonlinearity to 16-QAM Signal

Nonlinear Amplifier Gain Compression

Distort 16-QAM Signal with Custom Power Amplifier Nonlinearities

Distort 16-QAM Signal with Measured Power Amplifier Nonlinearities

More About

Memoryless Nonlinear Impairments

Cubic Polynomial and Hyperbolic Tangent Model Methods

Saleh Model Method

Ghorbani Model Method

Rapp Model Method

References

Extended Capabilities

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

See Also

Objects

Blocks

Communications Toolbox Documentation

Support

`Method` — Nonlinearity modeling method
`'Cubic polynomial'` (default) | `'Hyperbolic tangent'` | `'Saleh model'` | `'Ghorbani model'` | `'Rapp model'` | `'Lookup table'`

`InputScaling` — Input signal scaling factor
`0` (default) | scalar

`LinearGain` — Linear gain
`0` (default) | scalar

`IIP3` — Third-order input intercept point
`30` (default) | scalar

`AMPMConversion` — AM/PM conversion factor
`10` (default) | scalar

`AMAMParameters` — AM/AM parameters
`[2.1587 1.1517]` | `[8.1081 1.5413 6.5202 -0.0718]` | row vector

`AMPMParameters` — AM/PM parameters
`[4.0033 9.1040]` | `[4.6645 2.0965 10.88 -0.003]` | row vector

`PowerLowerLimit` — Input power lower limit
`10` (default) | scalar

`PowerUpperLimit` — Input power upper limit
`inf` (default) | scalar

`OutputScaling` — Output signal scaling factor
`0` (default) | scalar

`Smoothness` — Smoothness factor
`0.5` (default) | scalar

`OutputSaturationLevel` — Output saturation level
`1` (default) | scalar

`Table` — Amplifier characteristics lookup table
N-by-[P_in, P_out, ΔΦ] matrix

`insig` — Input RF baseband signal
scalar | column vector

`outsig` — Output RF baseband signal
scalar | column vector

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.