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

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

Design of CMOS Flip-Flops (SR, D, JK)

Design of CMOS Flip-Flops (SR, D, JK) A flip-flop or latch is a circuit with two stable states, used to store state information. It is the basic storage element in sequential logic and a fundamental building block in digital electronics systems, including computers and communication devices. Flip-flops and latches act as data storage elements for states, pulse counting, and synchronization of variably-timed input signals to a reference clock. Flip-flops can be transparent/opaque (latches) or clocked (synchronous, edge-triggered). Latches are level-sensitive, while flip-flops are edge-sensitive. In sequential logic, the output depends on current inputs and previous states. Fig.1 shows a sequential circuit combining a combinational block and a memory element. ...
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

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

Decision Feedback Equalizer (DFE) in MATLAB

  MATLAB Code M = 4; % QPSK numSymbols = 10000; numTrainingSymbols = 5000; chtaps = [1 0.5*exp(1i*pi/6) 0.1*exp(-1i*pi/8)]; % Generate QPSK symbols data = randi([0 M-1], numSymbols, 1); tx = pskmod(data, M, pi/4); % DFE setup eq = comm.DecisionFeedbackEqualizer; eq.ReferenceTap = 1; eq.NumForwardTaps = 5; eq.NumFeedbackTaps = 3; % SNR sweep snrVec = 0:5:25; % 0,5,10,...25 dB berBefore = zeros(size(snrVec)); berAfter = zeros(size(snrVec)); for k = 1:length(snrVec) % Apply channel and AWGN rx = awgn(filter(chtaps,1,tx), snrVec(k), 'measured' ); % BER before equalization rxDataNoEq = pskdemod(rx, M, pi/4); [~, berBefore(k)] = biterr(data, rxDataNoEq); % Equalize [y,~,~] = eq(rx, tx(1:numTrainingSymbols)); % Phase correction phaseOffset = angle(mean(conj(tx(1:numTrainingSymbols)) .* y(1:numTrainingSymbols))); yCorrected = y * exp(-1i*phaseOffset); % BER after equalization rxDataEq = pskdemod(yCorrected, M, pi/4); [...
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