Skip to main content

MATLAB Code for Pulse Amplitude Modulation (PAM) and Demodulation


Pulse Amplitude Modulation (PAM) & Demodulation

Pulse Amplitude Modulation (PAM) & Demodulation of an Analog Message Signal

MATLAB Script

clc;
clear all;
close all;
fm = 10; % frequency of the message signal
fc = 100; % frequency of the carrier signal
fs = 1000 * fm; % sampling frequency (100 kHz)
t = 0:1/fs:1;
m = 1 * cos(2 * pi * fm * t);
c = 0.5 * square(2 * pi * fc * t) + 0.5;
s = m .* c;

subplot(4,1,1);
plot(t,m);
title('Message signal');
xlabel('Time');
ylabel('Amplitude');

subplot(4,1,2);
plot(t,c);
title('Carrier signal');
xlabel('Time');
ylabel('Amplitude');

subplot(4,1,3);
plot(t,s);
title('Modulated signal');
xlabel('Time');
ylabel('Amplitude');

% Demodulation
d = s .* c;
filter = fir1(200,fm/fs,'low');
original_t_signal = conv(filter,d);
t1 = 0:1/(length(original_t_signal)-1):1;

subplot(4,1,4);
plot(t1,original_t_signal);
title('Demodulated signal');
xlabel('Time');
ylabel('Amplitude');

web('https://www.salimwireless.com/search?q=pulse%20amplitude%20modulation', '-browser');

Output

PAM analog modulation MATLAB output

Another Code for Pulse Amplitude Modulation and Demodulation of an Analog Message Signal

MATLAB Script

clc;
clear;
close all;

% Parameters
messageFrequency = 2;
carrierFrequency = 20;
samplingFrequency = 1000;
duration = 1;
A = 1;

% Time vector
t = 0:1/samplingFrequency:duration;

% Message signal
messageSignal = A * sin(2 * pi * messageFrequency * t);

% Carrier signal
carrierSignal = A * square(2 * pi * carrierFrequency * t);

% PAM signal
pamSignal = messageSignal .* (carrierSignal > 0);

% Plotting
figure;
subplot(3,1,1); plot(t, messageSignal); title('Message Signal');
subplot(3,1,2); plot(t, carrierSignal); title('Carrier Signal');
subplot(3,1,3); plot(t, pamSignal); title('PAM Signal');

web('https://www.salimwireless.com/search?q=pulse%20amplitude%20modulation', '-browser');

Pulse Amplitude Modulation (PAM) & Demodulation for Digital Data

% The code is written by SalimWireless.Com
clc;
clear;
close all;

% Parameters
M = 8;
numSymbols = 100;
Fs = 1000;
T = 1;

% Generate random data
data = randi([0 M-1], 1, numSymbols);

% PAM Modulation
pamLevels = linspace(-M + 1, M - 1, M);
modulatedSignal = pamLevels(data + 1);

% Create time vector
t = 0:1/Fs:T*numSymbols-1/Fs;

% Upsample and create PAM signal
upsampledSignal = zeros(1, length(t));
for i = 1:numSymbols
    upsampledSignal((i-1)*Fs+1:i*Fs) = modulatedSignal(i);
end

% Add noise
snr = 20;
noisySignal = awgn(upsampledSignal, snr, 'measured');

% PAM Demodulation
receivedSymbols = noisySignal(1:Fs:end);
demodulatedData = zeros(1, numSymbols);
for i = 1:numSymbols
    [~, demodulatedData(i)] = min(abs(receivedSymbols(i) - pamLevels));
end

% Plotting
figure;
subplot(4,1,1); stem(data); title('Original Data');
subplot(4,1,2); plot(t, upsampledSignal); title('Transmitted PAM Signal');
subplot(4,1,3); plot(t, noisySignal); title('Received Noisy PAM Signal');
subplot(4,1,4); stem(demodulatedData); title('Demodulated Data');
grid on;

disp('Original Data:'); disp(data);
disp('Demodulated Data:'); disp(demodulatedData);

web('https://www.salimwireless.com/search?q=pulse%20amplitude%20modulation', '-browser');

Output

PAM digital modulation MATLAB output
Parameter PAM PWM PPM DM PCM
What is varied? Amplitude Width Position Delta (difference) Binary code
Pulse Width Constant Variable Constant Constant Constant
Noise Immunity Low Moderate High Moderate High
Bandwidth Low Medium High Low High
Complexity Simple Moderate Complex Simple Complex
MATLAB Code PAM Script PWM Script PPM Script DM Script PCM Script
Detailed Study PAM PWM PPM DM PCM

Simulation Results for Comparison of PAM, PWM, PPM, DM, and PCM

Message Signal Simulation
PWM Signal Simulation
PPM Signal Simulation
PCM Signal Simulation
PWM | PPM | DM | Quantization | PCM
Explore Signal Processing Simulations

Simulator Setup PAM Modulation Demodulation

Instructions for Pulse Amplitude Modulation (PAM)

  • Note: Use the input fields to enter the message frequency and the square pulse carrier frequency.
  • Step 1: Click the "Generate Message" button to generate the input message signal.
  • Step 2: Click the "Generate Carrier" button to generate the carrier signal. Carrier must be > Message.
  • Step 3: Click the "Generate PAM Signal" button to generate the Modulated signal.

PAM Modulation Control Center

Perform Pulse Amplitude Modulation by interacting with the signal generators below.

Ready for Signal Recovery?

After generating your PAM signal, proceed to the Demodulation section to recover the original message using a reconstruction filter.

Go to Demodulation

Instructions for Pulse Amplitude Demodulation

  • The reconstruction filter recovers the original message from the sampled PAM signal.
  • A low-pass filter (LPF) is used with a cutoff frequency at or above the message frequency.
  • Click 'Demodulate' to view the recovered baseband signal.

Technical Definition: PAM Signal

MATLAB Mathematical Representation Copy Snippet
% Ideal Sampling with Pulse Train t = 0:0.001:1; fm = 2; fc = 10; m_t = cos(2*pi*fm*t); c_t = square(2*pi*fc*t); s_pam = m_t .* (c_t > 0); % Low Pass Reconstruction [b,a] = butter(4, fm/(fs/2)); demod = filter(b,a, s_pam);

Advanced PAM Simulator (Try real signal)

Upload CSV, .wav, or .mp4


Generate CSV

Parameters

Actual Sample Rate (fs): -- Hz

By default, the test signal is 5 Hz, and the pusle carrier signal is 50 Hz.


On this page, you can test real signals just as you would in MATLAB. If you want to access basic signal processing simulations, you can visit the page below. In that page, you can generate CSV files for signals such as the message signal, modulated signal, and others. After generating the files, you can return to this page to analyze the signals.



Further Reading

  1. Pulse Amplitude Modulation and Demodulation theory
  2. Is PAM a Digital Modulation Technique ?
  3. Pulse Width Modulation (PWM) and Demodulation
  4. Pulse Position Modulation (PPM) and Demodulation
  5. Delta Modulation and demodulation
  6. Pulse Code Modulation (PCM)
  7. Quantization Signal to Noise Ration (Q-SNR)
  8. MATLAB Code for Pulse Width Modulation and Demodulation
  9. MATLAB Code for Pulse Position Modulation (PPM) and Demodulation
  10. MATLAB Code for Pulse Code Modulation (PCM) and demodulation
📊 MATLAB & ONLINE SIMULATIONS (Web-Based)
🎓 GATE & UGC - NET SOLUTIONS
📡 WIRELESS & SIGNAL PROCESSING


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 ...
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

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...
Read more

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. ...
Read more

Wiener Filter in MATLAB

  MATLAB Code  % Wiener Filter Based on Wiener-Hopf Equation % This script demonstrates how to apply the Wiener filter to recover % a reference signal from a noisy signal using the Wiener-Hopf equation. % The filter minimizes the mean squared error between the noisy signal and the reference signal. clear; close all; clc; % Signal Parameters fs = 4000; % Sampling frequency (Hz) T = 1; % Total recording time (seconds) L = T * fs; % Signal length (samples) tt = (0:L-1) / fs; % Time vector ff = (0:L-1) * fs / L; % Frequency vector % Generate Reference Signal (a sinusoid) y = sin(2 * pi * 120 * tt); % Reference sinusoidal signal y = y(:); % Ensure column vector % Create Noisy Signal by Adding Gaussian Noise x = 0.50 * randn(L, 1) + y; % Noisy signal x = x(:); % Ensure column vector % Define Filter Order (Number of Coefficients) N = 200; % Apply Wiener Filter using custom function [xest, b, MSE] = wienerFilt(x, y, N); % Plot Results figure; subplot(411); plot(tt, x, 'k'), hold on, p...
Read more

MIMO Channel Matrix | Rank and Condition Number

MIMO / Massive MIMO MIMO Channel Matrix | Rank and Condition...   The channel matrix in wireless communication is a matrix that describes the impact of the channel on the transmitted signal. The channel matrix can be used to model the effects of the atmospheric or underwater environment on the signal, such as the absorption, reflection or scattering of the signal by surrounding objects. When addressing multi-antenna communication, the term "channel matrix" is used. Let's assume that only one TX and one RX are in communication and there's no surrounding object. Here, in our case, we can apply the proper threshold condition to a received signal and get the original transmitted signal at the RX side. However, in real-world situations, we see signal path blockage, reflections, etc.,  (NLOS paths [↗]) more frequently. The obstruction is typically caused by building walls, etc. Multi-antenna communication was introduced to address this issue. It makes diversity app...
Read more

Overmodulation & Distortion in AM

Overmodulation in AM and How It Causes Distortion 1. AM Signal Equation s(t) = A c [1 + μ m(t)] cos(2Ï€ f c t) A c = carrier amplitude m(t) = normalized modulating signal (|m(t)| ≤ 1) μ = modulation index 2. Modulation Index μ = A m / A c - Normal AM: 0 < μ ≤ 1 → no distortion - Overmodulation: μ > 1 → distortion occurs 3. Envelope and Overmodulation A(t) = A c [1 + μ m(t)] - For undistorted AM: 1 + μ m(t) ≥ 0 at all times - If μ > 1: 1 + μ m(t) < 0 at negative peaks → carrier flips Example: Let m(t) = cos(2Ï€ f m t), A c = 1 V, μ = 1.2 Minimum envelope: A min = A c [1 - 1.2] = -0.2 V Negative amplitude → envelope crosses zero → 180° phase flip 4. Mathematical Consequence -A c cos(θ) = A c cos(θ + Ï€) This phase reversal is what causes distortion in the demodulated signal. 5. Instantaneous AM Signal s...
Read more

Frequency Selective Fading vs Flat Fading in MATLAB

In the MATLAB code below, a comparison between  frequency-selective fading  and  flat fading  is shown. In frequency-selective fading, multipath propagation causes multiple delayed copies of the signal to arrive at the receiver. When the channel delay spread exceeds the symbol duration, these delayed components overlap, resulting in inter-symbol interference (ISI). In flat fading, ISI does not occur because the signal bandwidth is much smaller than the channel’s coherence bandwidth . Therefore, the channel response remains approximately constant across the signal bandwidth, and all symbols experience the same fading. MATLAB Code for frequency selective fading channel % OFDM over frequency selective Rayleigh fading channel clc; clearvars; close all ; % Simulation parameters nSym = 10^4; % Number of OFDM symbols EbN0dB = 0:2:20; % Eb/N0 range MOD_TYPE = 'MPSK' ; % 'MPSK' or 'MQAM' M = 4; % QPSK N = 64; % Total number ...
Read more