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

 

 
 
 
Return to Home →

Further Reading

📊 MATLAB & ONLINE SIMULATIONS (Web-Based)
🎓 GATE & UGC - NET SOLUTIONS
📡 WIRELESS & SIGNAL PROCESSING


Contact Us

Name

Email *

Message *

Popular Posts

CMOS Logic Gates: Inverter, NAND, and NOR

CMOS Logic Gates: Inverter, NAND, and NOR Complementary metal–oxide–semiconductor (CMOS) technology forms the backbone of modern digital IC design. Patented by Frank Wanlass in 1963 ( US patent 3,356,858 ), CMOS enables low-power, high-speed, and noise-immune logic circuits. CMOS gates, including inverters, NAND, and NOR, are widely used in both large-scale and small-scale integrated circuits due to their efficiency and versatility. CMOS Inverter A CMOS inverter consists of a complementary pair of PMOS and NMOS transistors. The PMOS transistor connects to the supply voltage V DD , while the NMOS connects to ground. The input voltage V IN controls both gates, and the output voltage V OUT is taken from the connected drains. CMOS inverters are highly energy-efficient and produce minimal heat due to their comple...
Read more

UGC NET Electronic Science Previous Year Question Papers with Solutions

Download Papers and Solutions Exam Pattern Preparation Tips FAQs More Home / Engineering & Other Exams / UGC NET 2026 PYQ 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) 📥 Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2026 - Question Paper Download PDF June 202...
Read more

Flat vs Frequency Selective Online Simulator

Flat vs Frequency Selective Online Simulator Channel Type Without Fading Flat Fading Multipaths Nakagami m SNR(dB) Run Simulation Input Signal Signal After Fading Constellation Diagram BER vs SNR Explore Advanced Flat vs Frequency-Selective Fading Simulator Want to see these equations in action? Visualize it. Launch Simulator Tool Interactive Rayleigh Fading Simulator Want to see Rayleigh fading in action? Visualize it. Launch Simulator Tool Return to DSP Simulations Main Page →
Read more

Linear vs Circular Convolution

Linear vs Circular Convolution Linear convolution vs circular convolution Linear convolution (what nature does) If: x[n] = transmitted signal h[n] = channel impulse response (multipath) Then the received signal is: y[n] = x[n] * h[n] This means: Past symbols spill into future symbols Causes inter-symbol interference (ISI) DFT of y[n] is not a simple product Circular convolution (what DFT assumes) The DFT assumes periodic signals : y[n] = x[n] ⊛ h[n], where indices wrap around modulo N Only under circular convolution does the DFT satisfy: Y[k] = X[k] · H[k] This is what enables one-tap frequency-domain equalization . What happens in the real channel (linear convolution) The channel performs linear convolution: y[n] = Σ l=0 Lh−1 h[l] x[n-l] But because: CP absorbs the “spillover” Receiver discards CP before DFT The useful part of the received symbol becomes: y[n] = Σ l=0 Lh−1 h[l] x[(n-l) mod N] Modulo inde...
Read more

Online Simulator for ASK, FSK, and PSK Signal Generation

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator Demodulation More Topics 1. ASK (Ampli...
Read more

Design of CMOS XOR/XNOR Gates

Design of CMOS XOR/XNOR Gates The semiconductor industry has experienced rapid integration of multimedia applications into mobile electronics, leading to very high integration density in CMOS VLSI. As operating frequencies increase, power consumption, speed, silicon area, and reliability become critical considerations. The XOR-XNOR circuits are fundamental building blocks in arithmetic circuits (Full Adders, Multipliers), compressors, comparators, parity checkers, code converters, error-detecting/correcting codes, and phase detectors. Their performance directly impacts the complex circuits they are used in. Design goals include full output voltage swing, low power consumption, reduced transistor count, minimal delay, and simultaneous non-skewed outputs. Static Logic (Static CMOS) Stat...
Read more

MUSIC Algorithm Explained (with MATLAB + Simulator)

Practical Implementation of the MUSIC Algorithm The focus is on how the algorithm works computationally , not just theory, and it explains the denominator (a H E n E n H a) mathematically and intuitively. 1. Introduction The MUSIC (Multiple Signal Classification) algorithm is a high-resolution method used in signal processing and array processing to estimate the Direction of Arrival (DOA) of signals received by a sensor array. Unlike classical beamforming methods, MUSIC uses eigenvector decomposition of the covariance matrix to separate the signal subspace and noise subspace , allowing it to achieve much higher angular resolution. In practical implementations, MUSIC works by: Simulating or collecting array signals Computing the covariance matrix Performing eigenvalue decomposition Separating signal and noise subspaces Scanning possible angles using a steering vector Constructing a pseudo-spectrum where peaks indicate signal directions 2. Signal Mo...
Read more

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSK, BPSK (with Simulation)

🧮 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; snr_db = -5:2:25; 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) ber_psk_results(i, :) = berawgn(snr_db, 'psk', psk_orders(i), 'nondiff'); end for i = 1:length(qam_orders) ber_qam_results(i, :) = berawgn(snr_db, 'qam', qam_orders(i)); end figure; semilogy(snr_db, ber_psk_results(1, :), 'o-', 'LineWidth', 1.5, 'DisplayName', 'BPSK'); hold on; for i = 2:length(psk_orders) semilogy(snr_db, ber_psk_results(i, :), 'o-', 'DisplayName', sprintf('%d-PSK', psk_or...
Read more