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



Contact Us

Name

Email *

Message *

Popular Posts

UGC NET Electronic Science Previous Year Question Papers with Solutions

Home / Engineering & Other Exams / UGC NET 2026 PYQ ⬇️ Download Papers and Solutions 📋 Exam Pattern 💡 Preparation Tips ❓ FAQs 📊 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 2025 - Question Paper Download PDF June 2025 - Solved Paper + Explanation ...

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...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

Bit Error Rate (BER) & SNR Guide Analyze communication system performance with our interactive simulators and MATLAB tools. 📘 Theory 🧮 Simulators 💻 MATLAB Code 📚 Resources BER Definition SNR Formula BER Calculator MATLAB Comparison 📂 Explore M-ary QAM, PSK, and QPSK Topics ▼ 🧮 Constellation Simulator: M-ary QAM 🧮 Constellation Simulator: M-ary PSK 🧮 BER calculation for ASK, FSK, and PSK 🧮 Approaches to BER vs SNR Calculation What is Bit Error Rate (BER)? The BER indicates how many corrupted bits are received compared to the total number of bits sent. It is the primary figur...

MATLAB Code for ASK, FSK, and PSK (with Online Simulator)

MATLAB Code for ASK, FSK, and PSK Comprehensive implementation of digital modulation and demodulation techniques with simulation results. 📘 Theory 📡 ASK Code 📶 FSK Code 🎚️ PSK Code 🕹️ Simulator 📚 Further Reading Amplitude Shift Frequency Shift Phase Shift Live Simulator ASK, FSK & PSK HomePage MATLAB Code MATLAB Code for ASK Modulation and Demodulation COPY % The code is written by SalimWireless.Com clc; clear all; close all; % Parameters Tb = 1; fc = 10; N_bits = 10; Fs = 100 * fc; Ts = 1/Fs; samples_per_bit = Fs * Tb; rng(10); binar...

Constellation Diagrams of ASK, PSK, and FSK (with MATLAB Code + Simulator)

Constellation Diagrams: ASK, FSK, and PSK Comprehensive guide to signal space representation, including interactive simulators and MATLAB implementations. 📘 Overview 🧮 Simulator ⚖️ Theory 📈 Q-function 📚 Resources BASK Modulation Transmits one of two signals: 0 or $\sqrt{E_b}$, representing binary 0 and 1. Simple but sensitive to noise. BFSK Modulation Transmits one of two signals: $\sqrt{E_b}$ on the Y-axis or $\sqrt{E_b}$ on the X-axis. These are orthogonal signals. BPSK Modulation Transmits $+\sqrt{E_b}$ or $-\sqrt{E_b}$ (antipodal signaling). Most efficient binary scheme. ...

PSD Calculation with FFT: MATLAB Tutorial for Signal Analysis

  Implementation Steps 1. FFT Computes the Frequency Content of a Signal FFT converts a time-domain signal to the frequency domain. If: The signal is sampled at rate $f_s$ You compute an $N_{\text{FFT}}$-point FFT Then each FFT bin corresponds to a frequency resolution of: $$\Delta f = \frac{f_s}{N_{\text{FFT}}}$$ So the FFT gives you accurate frequency content, assuming the signal is stationary and adequately sampled (Nyquist criterion met).  2. Magnitude Squared Gives Power (Not Amplitude) $$P[k] = |X[k]|^2$$ This gives power at each frequency bin, not just amplitude. It represents how much energy is present at each frequency. It's a key step for PSD.  3. Normalization Makes the PSD Physically Meaningful The equation: $$\text{PSD}[k] = \frac{|X[k]|^2}{N_{\text{FFT}} \cdot f_s \cdot U}$$ is derived from first principles and ensures that the u...

OFDM Symbols and Subcarriers Explained

This article explains how OFDM (Orthogonal Frequency Division Multiplexing) symbols and subcarriers work. It covers modulation, mapping symbols to subcarriers, subcarrier frequency spacing, IFFT synthesis, cyclic prefix, and transmission. Step 1: Modulation First, modulate the input bitstream. For example, with 16-QAM , each group of 4 bits maps to one QAM symbol. Suppose we generate a sequence of QAM symbols: s0, s1, s2, s3, s4, s5, …, s63 Step 2: Mapping Symbols to Subcarriers Assume N sub = 8 subcarriers. Each OFDM symbol in the frequency domain contains 8 QAM symbols (one per subcarrier): Mapping (example) OFDM symbol 1 → s0, s1, s2, s3, s4, s5, s6, s7 OFDM symbol 2 → s8, s9, s10, s11, s12, s13, s14, s15 … OFDM sym...

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...