rf.Amplifier
Model nonlinear amplifiers using cubic polynomial, AM/AM-AM/PM, modified Rapp, or Saleh representations
Since R2024b
Description
Use the rf.Amplifier
System object™ to create an idealized amplifier for command line simulation. The
rf.Amplifier
System object is a complex baseband model of an idealized amplifier with thermal noise. This
System object provides four nonlinearity models and three options to specify noise
representation. The four nonlinearity models are cubic polynomial, AM/AM-AM/PM, modified Rapp,
and Saleh.
To create a complex baseband model of amplifier with noise and nonlinearities:
Create the
rf.Amplifierobject 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
rfamp = rf.Amplifier
rfamp = rf.Amplifier(Name=Value)rf.Amplifier object using one or more name-value
arguments. For example, rfamp = rf.Amplifier(Model='ampm') creates an
idealized amplifier element designed using AM/AM—AM/PM data. Properties you do not specify
retain their default values.
Properties
Usage
Syntax
Description
Input Arguments
Output
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
Create an rf.Amplifier System object.
rfAmp = rf.Amplifier('IPsat',30);Generate modulated symbols.
in = qammod(randi([0 15],1000,1),16,'UnitAveragePower',true);Apply the nonlinearity model to the signal and plot the results.
out = rfAmp(in); scatterplot(out)
Define modulation order, samples per symbol, and input power.
M = 16; sps = 4; pindBm = 0;
Create an rf.Amplifier System object defined using an AM/AM-AM/PM nonlinearity model.
rfamp = rf.Amplifier('Model','ampm');
Apply pulse shaping by interpolating an input signal using a raised cosine finite impulse response (FIR) filter.
txfilter = comm.RaisedCosineTransmitFilter('RolloffFactor',0.3,... 'FilterSpanInSymbols',6,'OutputSamplesPerSymbol',sps,'Gain',sqrt(sps));
Define input signal.
pin = 10.^((pindBm-30)/10); % Unit: W
data = randi([0 M-1],1000,1);Define the modulated, filtered, and amplified signal.
modSig = qammod(data,M,'UnitAveragePower',true)*sqrt(pin);
filtSig = txfilter(modSig);
ampSig = rfamp(filtSig);Calculate the amplified output signal.
poutdBm = (20*log10(abs(ampSig)))+30; simulated_pindBm = (20*log10(abs(filtSig)))+30; phase = angle(ampSig .* conj(filtSig)) * 180/pi;
Plot the AM/AM-AM/PM characteristic of the amplifier by plotting the input vs. output power in a dBm plot.
figure set(gcf,'units','normalized','position',[.25 1/3 .5 1/3]) subplot (1,2,1) pow_idx = simulated_pindBm>-30; plot(simulated_pindBm(pow_idx), poutdBm(pow_idx),'.') hold on plot(rfamp.Table(:,1),rfamp.Table(:,2),'.','Markersize',15) xlabel('Input Power (dBm)') ylabel('Output Power (dBm)') grid on title('AM/AM Characteristic') Legend = {'Simulated Results','Measurement'}; legend (Legend, 'Location' , 'north')
Plot the input power (dBm) vs. output phase shift (deg).
subplot(1,2,2) plot(simulated_pindBm(pow_idx),phase(pow_idx),'.') hold on plot(rfamp.Table(:,1),rfamp.Table(:,3),'.','Markersize',15) legend(Legend,'Location','north') xlabel('Input Power (dBm)') ylabel('Output Phase Shift (deg.)') grid on title('AM/PM Characteristic')
Plot the amplified signal in a constellation plot.
scatterplot(ampSig,sps,0,'.') grid on title('Amplified Signal Constellation')
This example shows how to input a two-tone signal to an idealized system. To design this RF system:
First, generate a two-tone source.
Second, connect this two-tone source to an idealized RF PA.
Finally, observe the output response of this system.
Introduction
The RF system designed in this example uses the Idealized Baseband System object™ to analyze a cascade of mathematical models of RF components within the MATLAB® environment. During analysis, these System objects uses complex-baseband representation of the RF elements to compute time-domain waveforms. These complex modulated signals represent the transmitted information, assumed to be centered around an implicit carrier frequency. The models from the Idealized Baseband library do not model out-of-band behavior or spurious harmonics generated by nonlinear effects or interfering signals. Additionally, models from the Idealized Baseband library do not model impedance mismatches and assume that all blocks are perfectly matched. As a result, RF models built with the Idealized Baseband library simulate rapidly.
Create Two-Tone Source Generator
This figure shows the architecture of a two-tone source generator. This generator consists of a sine wave generator that produces two sinusoidal signals. These signals are multiplied by a -40 dB gain and then processed by a summer.
Define sample rate and samples per frame of the sine wave signals.
sampleRate = 1/2e-8; samplesPerFrame = 512;
Define a gain of - 40 dB.
m40dB = 10^(-40/20);
Create two sinusoid signals at 1.8 GHz and 2.6 GHz.
SineObj1 = dsp.SineWave('Frequency',1.8e6,'SampleRate',sampleRate, ... SamplesPerFrame=samplesPerFrame,ComplexOutput=true); SineObj2 = dsp.SineWave('Frequency',2.6e6,'SampleRate',sampleRate, ... SamplesPerFrame=samplesPerFrame,ComplexOutput=true);
Display the spectral analysis of a two tone signal source, where each tone is -10 dBm.
SpectAnal = spectrumAnalyzer('SampleRate',sampleRate,'ShowLegend',true); for Iter = 1:10 sineWave1 = SineObj1(); sineWave2 = SineObj2(); sigIn = m40dB*(sineWave1 + sineWave2); SpectAnal(sigIn); end
Simulate Idealized System
This figure shows how to create a system to IQ modulate the two-tone signal and amplify using an RF PA. The system architecture involves connecting a cubic polynomial amplifier to an IQ modulator. The output from the IQ modulator is then connected to a cross-term (CT) memory power amplifier. A two-tone signal is supplied as input to the system, and the output is connected to a spectrum analyzer for visualizing the power amplifier characteristics.
Load the pre-computed PA coefficient matrix.
load('PAcoefficients_rf.mat','fitCoefMat')
Create an idealized baseband cubic polynomial amplifier and IQ modulator with nonlinearity and noise.
preAmpIQ = rf.Amplifier('Model','cubic','Gain',8,'Nonlinearity','IIP3','IIP3',45, ... 'IncludeNoise',true,NF=1.5); mixerModIQ = rf.Mixer('Model','iqmod','Gain',0,'LO',1e8, ... 'PhaseImbalance',.5,'Nonlinearity','IIP3',IIP3=50, ... IncludeNoise=true,NoiseType='NF',NF=6);
Create an idealized baseband cross-term memory PA.
powerAmp = rf.PAmemory('Model','Cross-term memory', ... 'CoefficientMatrix','fitCoefMat');
Connect the RF chain and provide the two-tone signal as input.
SpectAnal = spectrumAnalyzer('SampleRate',sampleRate,'ShowLegend',true); for Iter = 1:10 sineWave1 = SineObj1(); sineWave2 = SineObj2(); sineIn = m40dB*(sineWave1 + sineWave2); ampIQ = preAmpIQ(sineIn); modIQ = mixerModIQ(ampIQ); paOut = powerAmp(modIQ*sqrt(50))/sqrt(50); SpectAnal(paOut); end
References
[1] Razavi, Behzad. “Basic Concepts “ in RF Microelectronics, 2nd edition, Prentice Hall, 2012.
[2] Rapp, C., “Effects of HPA-Nonlinearity on a 4-DPSK/OFDM-Signal for a Digital Sound Broadcasting System.” Proceedings of the Second European Conference on Satellite Communications, Liege, Belgium, Oct. 22-24, 1991, pp. 179-184.
[3] Saleh, A.A.M., “Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers.” IEEE Trans. Communications, vol. COM-29, pp.1715-1720, November 1981.
[4] IEEE 802.11-09/0296r16. “TGad Evaluation Methodology.“ Institute of Electrical and Electronics Engineers.https://www.ieee.org/
[5] Kundert, Ken.“ Accurate and Rapid Measurement of IP2 and IP3,“ The Designer Guide Community, May 22, 2002.
