Skip to main content

MATLAB Code for Pulse Width Modulation (PWM) and Demodulation


 

MATLAB Code for Analog Pulse Width Modulation (PWM)

clc;
clear all;
close all;
fs=30; %frequency of the sawtooth signal
fm=3; %frequency of the message signal
sampling_frequency = 10e3;
a=0.5; % amplitide

t=0:(1/sampling_frequency):1; %sampling rate of 10kHz


sawtooth=2*a.*sawtooth(2*pi*fs*t); %generating a sawtooth wave


subplot(4,1,1);
plot(t,sawtooth); % plotting the sawtooth wave
title('Comparator Wave');

msg=a.*sin(2*pi*fm*t); %generating message wave

subplot(4,1,2);
plot(t,msg); %plotting the sine message wave
title('Message Signal');


for i=1:length(sawtooth)
if (msg(i)>=sawtooth(i))
pwm(i)=1; %is message signal amplitude at i th sample is greater than
%sawtooth wave amplitude at i th sample
else
pwm(i)=0;
end
end

subplot(4,1,3);
plot(t,pwm,'r');
title('PWM');
axis([0 1 0 1.1]); %to keep the pwm visible during plotting.

%% Demodulation
% Demodulation: Measure the pulse width to reconstruct the signal
demodulated_signal = zeros(size(msg));

for i = 1:length(pwm)-1
if pwm(i) == 1
% Measure the time until the next falling edge
j = i + 1;
while pwm(j) == 1 && j < length(pwm)
j = j + 1;
end
% Reconstruct the analog value based on pulse width
demodulated_signal(i) = mean(msg(i:j-1));
end
end

% Low-Pass Filter Design
Fs = 1 / (t(2) - t(1)); % Sampling frequency
Fc = 5; % Cutoff frequency (adjust based on your signal)
[b, a] = butter(4, Fc / (Fs / 2), 'low'); % 4th-order Butterworth filter

% Apply the Low-Pass Filter
filtered_signal = filtfilt(b, a, demodulated_signal);

% Plot the demodulated and filtered signal for comparison
subplot(4,1,4);
plot(t, filtered_signal, 'r', 'LineWidth', 1.5); % Filtered signal in red
title('Demodulated Signal (Filtered)');
xlabel('Time');
ylabel('Amplitude');
grid on;
 

Output 



 MATLAB Code for Digital Pulse Width Modulation (PWM)


% This code is developed by SalimWireless.Com
clc; clear; close all;
% Digital SPWM Generator using Square Wave Carrier in MATLAB

% === PARAMETERS ===
fs_carrier = 20;       % Carrier frequency in Hz
f_signal = 5;           % Message signal frequency in Hz
sampleRate = 50000;      % Samples per second
duration = 1;            % Duration in seconds

% === TIME VECTOR ===
t = linspace(0, duration, sampleRate * duration);

% === MESSAGE SIGNAL (SINE WAVE) ===
signal = sin(2 * pi * f_signal * t);

% === NORMALIZE SIGNAL TO 0–1 FOR DUTY CYCLE ===
normalizedSignal = (signal + 1) / 2;  % Scale from [-1, 1] to [0, 1]

% === PWM GENERATION BASED ON SQUARE CARRIER PERIODS ===
samplesPerCarrierPeriod = floor(sampleRate / fs_carrier);
pwm = zeros(1, length(t));

% Generate PWM: For each carrier cycle, set ON time based on message amplitude at start
for i = 1:samplesPerCarrierPeriod:length(t)
    startIndex = i;
    if startIndex > length(t)
        break;
    end
    
    % Duty cycle at start of period
    duty = normalizedSignal(startIndex);
    onSamples = floor(samplesPerCarrierPeriod * duty);
    
    % Set PWM high for onSamples
    endIndex = min(startIndex + samplesPerCarrierPeriod - 1, length(t));
    onEndIndex = min(startIndex + onSamples - 1, endIndex);
    
    pwm(startIndex:onEndIndex) = 1;
end

% === GENERATE SQUARE CARRIER FOR REFERENCE PLOTTING ===
carrierSquare = double(mod(t * fs_carrier, 1) < 0.5);

% === TRIM TO FIRST 3 CYCLES OF MESSAGE SIGNAL FOR VISUALIZATION ===
samplesToPlot = floor(3 * (sampleRate / f_signal));
t_plot = t(1:samplesToPlot);
signal_plot = signal(1:samplesToPlot);
carrier_plot = carrierSquare(1:samplesToPlot);
pwm_plot = pwm(1:samplesToPlot);

% === PLOTTING ===
figure('Name', 'PWM with Square Wave Carrier', 'Color', 'w');
hold on;
plot(t_plot, signal_plot, 'b', 'LineWidth', 1.2);
plot(t_plot, carrier_plot, 'g--', 'LineWidth', 1);
stairs(t_plot, pwm_plot, 'r', 'LineWidth', 1.2);
hold off;

xlabel('Time (s)');
ylabel('Amplitude');
title('PWM Output with Square Wave Carrier');
legend('Message Signal (Sine)', 'Square Carrier', 'PWM Output', 'Location', 'southoutside', 'Orientation', 'horizontal');
grid on;
web('https://www.salimwireless.com/search?q=pwm%20pulse%20modulation', '-browser');

Output

 
Parameter PAM PWM PPM DM PCM
Parameter varied Signal amplitude Pulse duration Pulse timing Sample difference (delta) Digital code
Pulse duration Fixed Adjustable Fixed Fixed Fixed
Resistance to noise Poor Average Good Average Good
Bandwidth requirement Low Moderate High Low High
Implementation complexity Low Medium High Low High
MATLAB implementation PAM Script PWM Script PPM Script DM Script PCM Script
Further reading PAM PWM PPM DM PCM

PWM Signal Generation

 

 
 
 

Further Reading

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   Start Simulator for binary ASK Modulation Message Bits (e.g. 1,0,1,0) Carrier Frequency (Hz) Sampling Frequency (Hz) Run Simulation Simulator for binary FSK Modulation Input Bits (e.g. 1,0,1,0) Freq for '1' (Hz) Freq for '0' (Hz) Sampling Rate (Hz) Visualize FSK Signal Simulator for BPSK Modulation ...

Constellation Diagrams of ASK, PSK, and FSK

📘 Overview of Energy per Bit (Eb / N0) 🧮 Online Simulator for constellation diagrams of ASK, FSK, and PSK 🧮 Theory behind Constellation Diagrams of ASK, FSK, and PSK 🧮 MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK 📚 Further Reading 📂 Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... 🧮 Simulator for constellation diagrams of m-ary PSK 🧮 Simulator for constellation diagrams of m-ary QAM BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

📘 Overview of BER and SNR 🧮 Online Simulator for BER calculation of m-ary QAM and m-ary PSK 🧮 MATLAB Code for BER calculation of M-ary QAM, M-ary PSK, QPSK, BPSK, ... 📚 Further Reading 📂 View Other Topics on M-ary QAM, M-ary PSK, QPSK ... 🧮 Online Simulator for Constellation Diagram of m-ary QAM 🧮 Online Simulator for Constellation Diagram of m-ary PSK 🧮 MATLAB Code for BER calculation of ASK, FSK, and PSK 🧮 MATLAB Code for BER calculation of Alamouti Scheme 🧮 Different approaches to calculate BER vs SNR What is Bit Error Rate (BER)? The abbreviation BER stands for Bit Error Rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. BER = (number of bits received in error) / (total number of tran...

Theoretical BER vs SNR for m-ary PSK and QAM

Relationship Between Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) The relationship between Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) is a fundamental concept in digital communication systems. Here’s a detailed explanation: BER (Bit Error Rate): The ratio of the number of bits incorrectly received to the total number of bits transmitted. It measures the quality of the communication link. SNR (Signal-to-Noise Ratio): The ratio of the signal power to the noise power, indicating how much the signal is corrupted by noise. Relationship The BER typically decreases as the SNR increases. This relationship helps evaluate the performance of various modulation schemes. BPSK (Binary Phase Shift Keying) Simple and robust. BER in AWGN channel: BER = 0.5 × erfc(√SNR) Performs well at low SNR. QPSK (Quadrature...

Q-function in BER vs SNR Calculation

Q-function in BER vs. SNR Calculation In the context of Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) calculations, the Q-function plays a significant role, especially in digital communications and signal processing . What is the Q-function? The Q-function is a mathematical function that represents the tail probability of the standard normal distribution. Specifically, it is defined as: Q(x) = (1 / sqrt(2Ï€)) ∫â‚“∞ e^(-t² / 2) dt In simpler terms, the Q-function gives the probability that a standard normal random variable exceeds a value x . This is closely related to the complementary cumulative distribution function of the normal distribution. The Role of the Q-function in BER vs. SNR The Q-function is widely used in the calculation of the Bit Error Rate (BER) in communication systems, particularly in systems like Binary Phase Shift Ke...

Fading : Slow & Fast and Large & Small Scale Fading

📘 Overview 📘 LARGE SCALE FADING 📘 SMALL SCALE FADING 📘 SLOW FADING 📘 FAST FADING 🧮 MATLAB Codes 📚 Further Reading LARGE SCALE FADING The term 'Large scale fading' is used to describe variations in received signal power over a long distance, usually just considering shadowing.  Assume that a transmitter (say, a cell tower) and a receiver  (say, your smartphone) are in constant communication. Take into account the fact that you are in a moving vehicle. An obstacle, such as a tall building, comes between your cell tower and your vehicle's line of sight (LOS) path. Then you'll notice a decline in the power of your received signal on the spectrogram. Large-scale fading is the term for this type of phenomenon. SMALL SCALE FADING  Small scale fading is a term that describes rapid fluctuations in the received signal power on a small time scale. This includes multipath propagation effects as well as movement-induced Doppler fr...

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...

🧮 MATLAB Code for BPSK, M-ary PSK, and M-ary QAM Together 🧮 MATLAB Code for M-ary QAM 🧮 MATLAB Code for M-ary PSK 📚 Further Reading MATLAB Script for BER vs. SNR for M-QAM, M-PSK, QPSK, BPSK % Written by Salim Wireless clc; clear; close all; num_symbols = 1e5; snr_db = -20:2:20; psk_orders = [2, 4, 8, 16, 32]; qam_orders = [4, 16, 64, 256]; ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); for i = 1:length(psk_orders) psk_order = psk_orders(i); for j = 1:length(snr_db) data_symbols = randi([0, psk_order-1], 1, num_symbols); modulated_signal = pskmod(data_symbols, psk_order, pi/psk_order); received_signal = awgn(modulated_signal, snr_db(j), 'measured'); demodulated_symbols = pskdemod(received_signal, psk_order, pi/psk_order); ber_psk_results(i, j) = sum(data_symbols ~= demodulated_symbols) / num_symbols; end end for i...

Wiener Filter Theory: Equations, Error Signal, and MSE

  Assuming known stationary signal and noise spectra and additive noise, the Wiener filter is a filter used in signal processing to provide an estimate of a desired or target random process through linear time-invariant (LTI) filtering of an observed noisy process. The mean square error between the intended process and the estimated random process is reduced by the Wiener filter. Fig: Block diagram view of the FIR Wiener filter for discrete series. An input signal x[n] is convolved with the Wiener filter g[n] and the result is compared to a reference signal s[n] to obtain the filtering error e[n]. In the big picture, the signal is attenuated and added with noise, then the signal is passed through a Wiener filter. And the function of the Wiener filter is to minimize the mean square error between the filter output of the received signal and the reference signal by adjusting the Wiener filter tap coefficient.   Description...