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

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


Contact Us

Name

Email *

Message *

Popular Posts

Hybrid Beamforming | Page 1

Beamforming Techniques Hybrid Beamforming... Page 1 | Page 2 | Hybrid Beamforming: Hybrid beam formation was developed to address some of the limitations of digital pre-coding approaches. Every antenna element is connected to an RF chain in digital pre-coding (beam forming) method. We also know that each RF chain is in charge of providing a separate data stream between the transmitter and the receiver. We know that a larger number of independent data streams leads to higher data rates. It has a spatial multiplexing feature for MIMO. As a result, we may assume that switching from MIMO to massive MIMO will benefit us more in terms of spatial multiplexing in massive MIMO, where each antenna is coupled to a single RF chain. We'll proceed with a definition of hybrid beam forming. Overview of hybrid beam forming with example: Unlike digital beam forming, more than one antenna element is connected to a single RF chain in hybr...

MATLAB Code for 8-PSK, 16-PSK, ...

📘 Overview & Theory 🧮 MATLAB Code for BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK 🧮 Simulator for m-ary PSK 📚 Further Reading   MATLAB Code for BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK clc; clear all; close all; rng(10) M = 8; % M = 2, 4, 8, 16, 32, etc. N_Bits = 2520; Phase = 0; data_info_bit = randi([0,1],N_Bits,1); data_temp = bi2de(reshape(data_info_bit,N_Bits/log2(M),log2(M))); modData = pskmod(data_temp,M,Phase); figure(1); scatterplot(modData); channelAWGN = 15; rxData2 = awgn(modData, channelAWGN); figure(2); scatterplot(rxData2); demodData = pskdemod(rxData2,M,Phase);   for BPSK, Constellation Size, M = 2 for QPSK, M = 4 for 8-PSK, M = 8, and so on    Output Figure: 8-PSK Modulation Figure: 8-PSK Demodulation after adding AWGN Noise Using the above MATLAB code you'll able be to modulate and demodulate 2-PSK, 4-PSK, 8-PSK, 16-PSK, 32-PSK and so on.  16-PSK   Fig: 16-PSK In this above code ' M ' is the number of the conste...

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 ...

How Windowing Affects Your Periodogram

The windowed periodogram is a widely used technique for estimating the Power Spectral Density (PSD) of a signal. It enhances the classical periodogram by mitigating spectral leakage through the application of a windowing function. This technique is essential in signal processing for accurate frequency-domain analysis.   Power Spectral Density (PSD) The PSD characterizes how the power of a signal is distributed across different frequency components. For a discrete-time signal, the PSD is defined as the Fourier Transform of the signal’s autocorrelation function: S x (f) = FT{R x (Ï„)} Here, R x (Ï„)}is the autocorrelation function. FT : Fourier Transform   Classical Periodogram The periodogram is a non-parametric PSD estimation method based on the Discrete Fourier Transform (DFT): P x (f) = \(\frac{1}{N}\) X(f) 2 Here: X(f): DFT of the signal x(n) N: Signal length However, the classical periodogram suffers from spectral leakage due to abrupt truncation of the ...

Advanced M-ary Modulation Simulator: Constellation, min dist, Efficiency, SER, EVM (RMS)

Advanced M-ary Communication Lab Analytical & Statistical Performance of Digital Modulation Theoretical Probability of Error (\(P_s\)) \[ P_s = Q\left(\sqrt{\frac{2 E_b}{N_0}}\right) \] Modulation (M-ary) BPSK (M=2) QPSK (M=4) 8-PSK (M=8) 16-QAM (M=16) 64-QAM (M=64) 256-QAM (M=256) SNR (\(E_b/N_0\)): 12 dB Efficiency 2 bps/Hz Min Dist (\(d_{min}\)) 1.41 Symbol Error 1.2e-5 EVM (RMS) 0.0% Constellation Diagram Noise PDF & Decision Tail 1. Geometric Mapping ...

Frequency Shift Keying (FSK) Modulation & Demodulation (with Simulation)

Frequency Shift Keying (FSK) Theoretical Foundations: Frequency Shift Keying (FSK) is a discrete frequency modulation scheme wherein the digital information is encoded via instantaneous shifts in the carrier signal's frequency. The fundamental implementation is Binary FSK (BFSK), which maps binary data onto two distinct, discrete spectral states. A binary '1' (the "mark" state) is represented by a carrier frequency \( f_1 \), while a binary '0' (the "space" state) corresponds to frequency \( f_2 \). Each symbol is sustained for a bit interval denoted by \( T_b \). FSK Transmitter Characterization: The mathematical model for the modulated BFSK output \( s(t) \) is defined as: \[ s(t) = \begin{cases} A_c \cos(2\pi f_1 t), & \text{for } m = 1 \\ A_c \cos(2\pi f_2 t), & \text{for } m = 0 \end{cases} \] ...

Galois Fields: GF(2) and GF(2m) and Primitive Polynomial

Galois Fields: GF(2) and GF(2 m ) 1. What is a Galois Field (GF)? A Galois Field (GF) is a finite set of elements in which the four basic arithmetic operations—addition, subtraction, multiplication, and division (except by zero)— are all well defined and closed. GF(q) ⇒ a field with exactly q elements 2. The Simplest Field: GF(2) GF(2) is the smallest possible finite field and forms the foundation of all digital systems. GF(2) = {0, 1} Addition in GF(2) Addition is performed modulo 2 (XOR operation): + 0 1 0 0 1 1 1 0 Multiplication in GF(2) × 0 1 0 0 0 1 0 1 GF(2) is used in binary logic, XOR operations, and simple error-control codes. 3. Meaning of GF(2 m ) GF(2 m ) is a finite field containing exactly 2 m elements . Each element represents an m-bit symbol . Field Number of Elements GF(2) 2 GF(2²) 4 GF(2³) 8 GF(2⁸) 256 Important: GF(2 m ) is not integer arithmetic modulo 2 m . It is polynomial-based arithmetic. 4....