Skip to main content

OFDM in MATLAB


 

MATLAB Script

% The code is written by SalimWireless.Com

1. Initialization

clc;
clear all;
close all;


2. Generate Random Bits

% Generate random bits
numBits = 100;
bits = randi([0, 1], 1, numBits);


3. Define Parameters

% Define parameters
numSubcarriers = 4; % Number of subcarriers
numPilotSymbols = 3; % Number of pilot symbols
cpLength = ceil(numBits / 4); % Length of cyclic prefix (one-fourth of the data length)


4. Add Cyclic Prefix

% Add cyclic prefix
dataWithCP = [bits(end - cpLength + 1:end), bits];


5. Insert Pilot Symbols

% Insert pilot symbols
pilotSymbols = ones(1, numPilotSymbols); % Example pilot symbols (could be any pattern)
dataWithPilots = [pilotSymbols, dataWithCP];

 

6. Perform OFDM Modulation (IFFT)

% Perform OFDM modulation (IFFT)
dataMatrix = reshape(dataWithPilots, numSubcarriers, []);
ofdmSignal = ifft(dataMatrix, numSubcarriers);
ofdmSignal = reshape(ofdmSignal, 1, []);


7. Display the Generated Data

% Display the generated data
disp("Original Bits:");
disp(bits);
disp("Data with Cyclic Prefix and Pilots:");
disp(dataWithPilots);
disp("OFDM Signal:");
disp(ofdmSignal);

%%%%%%%%%%%%%%%%%%%%%%%%%%% Demodulation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


8. Demodulation

% Perform FFT on the received signal
%ofdmSignal = awgn(ofdmSignal, 1000);
ofdmSignal = reshape(ofdmSignal, numSubcarriers, []);
rxSignal = fft(ofdmSignal, numSubcarriers);
%rxSignal = [rxSignal(1,:) rxSignal(2,:) rxSignal(3,:) rxSignal(4,:)];


9. Remove Cyclic Prefix

% Remove cyclic prefix
rxSignalNoCP = rxSignal(cpLength + 1:end);


10. Extract Data Symbols and Discard Pilot Symbols

% Extract data symbols and discard pilot symbols
dataSymbols = rxSignalNoCP(numPilotSymbols + 1:end);


11. Demodulate the Symbols Using Thresholding

% Demodulate the symbols using thresholding
threshold = 0;
demodulatedBits = (real(dataSymbols) > threshold);


12. Plot the Original and Received Bits

figure(1)
stem(bits);
legend("Original Information Bits")

figure(2)
stem(demodulatedBits);
legend("Received Bits")

Output

 

 
Fig 1: Original Information Bits
 
 
 
 
 
Fig 2: OFDM Signal
 
 
 
 
Fig 3: Received Demodulated Bits

 

Copy the MATLAB Code above from here

 

 

MATLAB Code for OFDM using QPSK

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

% Generate random bits (must be even for QPSK)
numBits = 20;
if mod(numBits, 2) ~= 0
numBits = numBits + 1; % Make even
end
bits = randi([0, 1], 1, numBits);

% QPSK Modulation (2 bits per symbol)
bitPairs = reshape(bits, 2, []).';
qpskSymbols = (1/sqrt(2)) * ((2*bitPairs(:,1)-1) + 1j*(2*bitPairs(:,2)-1)); % Gray-coded

% Parameters
numSubcarriers = 4; % Number of OFDM subcarriers
numPilotSymbols = 3; % Number of pilot symbols
cpLength = ceil(length(qpskSymbols) / 4); % Cyclic prefix length

% Insert pilot symbols
pilotSymbols = ones(1, numPilotSymbols); % Example pilot symbols (BPSK pilots)
dataWithPilots = [pilotSymbols, qpskSymbols.'];

% Add cyclic prefix
dataWithCP = [dataWithPilots(end - cpLength + 1:end), dataWithPilots];

% Reshape and perform IFFT (OFDM modulation)
dataMatrix = reshape(dataWithCP, numSubcarriers, []);
ofdmSignal = ifft(dataMatrix, numSubcarriers);
ofdmSignal1 = reshape(ofdmSignal, 1, []);

% Display
disp("Original Bits:");
disp(bits);
disp("QPSK Symbols:");
disp(qpskSymbols.');
disp("Data with CP and Pilots:");
disp(dataWithCP);
disp("OFDM Signal:");
disp(ofdmSignal1);

%%%%%%%%%%%%%%%%%%%%%%%%%%% Demodulation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Reshape back to subcarrier-wise blocks and FFT
ofdmRxMatrix = reshape(ofdmSignal1, numSubcarriers, []);
rxSignal = fft(ofdmRxMatrix, numSubcarriers);

% Remove cyclic prefix
rxSignal1D = reshape(rxSignal, 1, []);
rxSignalNoCP = rxSignal1D(cpLength + 1:end);

% Remove pilots
rxDataSymbols = rxSignalNoCP(numPilotSymbols + 1:end);

% QPSK Demodulation
demodBits = zeros(1, 2*length(rxDataSymbols));
demodBits(1:2:end) = real(rxDataSymbols) > 0;
demodBits(2:2:end) = imag(rxDataSymbols) > 0;

%%%%%%%%%%%%%%%%%%%%%%%%%%% Plotting %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

figure(1)
stem(bits);
title("Original Bits");
xlabel("Bit Index"); ylabel("Bit Value");
legend("Original Bits");

figure(2)
hReal = stem(real(ofdmSignal1), 'r', 'DisplayName', 'Real Part');
hold on;
hImag = stem(imag(ofdmSignal1), 'b', 'DisplayName', 'Imaginary Part');
set(hReal, 'Marker', 'o', 'LineWidth', 1.5);
set(hImag, 'Marker', 'x', 'LineWidth', 1.5);
grid on;
title('OFDM Signal (Time Domain)');
xlabel('Sample Index');
ylabel('Amplitude');
legend;
hold off;

figure(3)
stem(demodBits);
title("Demodulated Bits");
xlabel("Bit Index"); ylabel("Bit Value");
legend("Demodulated Bits");

% Optional: Calculate BER
numErrors = sum(bits ~= demodBits);
ber = numErrors / numBits;
fprintf("Bit Error Rate (BER): %.4f\n", ber);



Output

 
 
 
 
 
  
 
 
 

 

MATLAB Code for OFDM Subcarriers (using 16-QAM)

clc;
clear;
close all;

% OFDM System with 16-QAM and Cooley-Tukey FFT/IFFT

% Parameters
N = 64; % Number of OFDM subcarriers
M = 16; % Modulation order (16-QAM -> M = 16)
nSymbols = 100;% Number of OFDM symbols
Ncp = 16; % Length of cyclic prefix

% Generate random data for transmission (0 to M-1 for 16-QAM)
data = randi([0 M-1], nSymbols, N);

% 16-QAM modulation of the data using custom function
modData = zeros(nSymbols, N);
for i = 1:nSymbols
modData(i, :) = qammod(data(i, :), M);
end

% Perform IFFT using Cooley-Tukey to generate the time domain OFDM signal
ofdmTimeSignal = zeros(size(modData));
for i = 1:nSymbols
ofdmTimeSignal(i, :) = ifft(modData(i, :));
end

% Add cyclic prefix
cyclicPrefix = ofdmTimeSignal(:, end-Ncp+1:end); % Extract cyclic prefix
ofdmWithCP = [cyclicPrefix ofdmTimeSignal]; % Add cyclic prefix to the signal

%% Plot Subcarriers in Frequency Domain (before IFFT)
figure;
stem(0:N-1, abs(modData(100, :))); % Plot absolute value of the subcarriers for the first symbol
title('Subcarriers in Frequency Domain for 1st OFDM Symbol (Before IFFT)');
xlabel('Subcarrier Index');
ylabel('Magnitude');

%% Plot Time Domain OFDM Signal (after IFFT)
figure;
plot(real(ofdmTimeSignal(1, :))); % Plot real part of the OFDM time signal for the first symbol
title('OFDM Signal in Time Domain for 1st OFDM Symbol (Without CP)');
xlabel('Time Sample Index');
ylabel('Amplitude');

%% Plot Time Domain OFDM Signal with Cyclic Prefix
figure;
plot(real(ofdmWithCP(1, :))); % Plot real part of the OFDM time signal with CP for the first symbol
title('OFDM Signal in Time Domain for 1st OFDM Symbol (With Cyclic Prefix)');
xlabel('Time Sample Index');
ylabel('Amplitude');

%% Receiver Side - Remove Cyclic Prefix and Demodulate
% Remove cyclic prefix
receivedSignal = ofdmWithCP(:, Ncp+1:end); % Remove cyclic prefix

% Apply FFT using Cooley-Tukey to recover the received subcarriers (back to frequency domain)
receivedSubcarriers = zeros(size(receivedSignal));
for i = 1:nSymbols
receivedSubcarriers(i, :) = fft(receivedSignal(i, :));
end

% 16-QAM Demodulation of the received subcarriers using custom function
receivedData = zeros(nSymbols, N);
for i = 1:nSymbols
receivedData(i, :) = qamdemod(receivedSubcarriers(i, :), M);
end

% Calculate symbol errors
numErrors = sum(data(:) ~= receivedData(:));
fprintf('Number of symbol errors: %d\n', numErrors);

%% Plot Received Subcarriers in Frequency Domain (after FFT at the receiver)
figure;
stem(0:N-1, abs(receivedSubcarriers(100, :))); % Plot absolute value of received subcarriers for the first symbol
title('Received Subcarriers in Frequency Domain for 1st OFDM Symbol (After FFT)');
xlabel('Subcarrier Index');
ylabel('Magnitude');

%% Plot Transmitted Data Constellation (Before IFFT)
figure;
scatterplot(modData(1, :)); % Plot for the first OFDM symbol
title('Transmitted 16-QAM Symbols for 1st OFDM Symbol');
xlabel('In-phase');
ylabel('Quadrature');

%% Plot Received Data Constellation (After Demodulation)
receivedModData = qammod(receivedData(1, :), M); % Map back for plotting
figure;
scatterplot(receivedModData);
title('Received 16-QAM Symbols for 1st OFDM Symbol');
xlabel('In-phase');
ylabel('Quadrature');

 Output

 
















Copy the MATLAB code above from here

 

Read more about

[1] OFDM in details

[2] Structure of an OFDM packet

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   Start Simulator for binary ASK Modulation Message Bits (e.g. 1,0,1,0) Carrier Frequency (Hz) Sampling Frequency (Hz) Run Simulation Simulator for binary FSK Modulation Input Bits (e.g. 1,0,1,0) Freq for '1' (Hz) Freq for '0' (Hz) Sampling Rate (Hz) Visualize FSK Signal Simulator for BPSK Modulation ...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

📘 Overview of BER and SNR 🧮 Online Simulator for BER calculation of m-ary QAM and m-ary PSK 🧮 MATLAB Code for BER calculation of M-ary QAM, M-ary PSK, QPSK, BPSK, ... 📚 Further Reading 📂 View Other Topics on M-ary QAM, M-ary PSK, QPSK ... 🧮 Online Simulator for Constellation Diagram of m-ary QAM 🧮 Online Simulator for Constellation Diagram of m-ary PSK 🧮 MATLAB Code for BER calculation of ASK, FSK, and PSK 🧮 MATLAB Code for BER calculation of Alamouti Scheme 🧮 Different approaches to calculate BER vs SNR What is Bit Error Rate (BER)? The abbreviation BER stands for Bit Error Rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. BER = (number of bits received in error) / (total number of tran...

Channel Impulse Response (CIR)

📘 Overview & Theory 📘 How CIR Affects the Signal 🧮 Online Channel Impulse Response Simulator 🧮 MATLAB Codes 📚 Further Reading What is the Channel Impulse Response (CIR)? The Channel Impulse Response (CIR) is a concept primarily used in the field of telecommunications and signal processing. It provides information about how a communication channel responds to an impulse signal. It describes the behavior of a communication channel in response to an impulse signal. In signal processing, an impulse signal has zero amplitude at all other times and amplitude ∞ at time 0 for the signal. Using a Dirac Delta function, we can approximate this. Fig: Dirac Delta Function The result of this calculation is that all frequencies are responded to equally by δ(t) . This is crucial since we never know which frequenci...

Q-function in BER vs SNR Calculation

Q-function in BER vs. SNR Calculation In the context of Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) calculations, the Q-function plays a significant role, especially in digital communications and signal processing . What is the Q-function? The Q-function is a mathematical function that represents the tail probability of the standard normal distribution. Specifically, it is defined as: Q(x) = (1 / sqrt(2Ï€)) ∫â‚“∞ e^(-t² / 2) dt In simpler terms, the Q-function gives the probability that a standard normal random variable exceeds a value x . This is closely related to the complementary cumulative distribution function of the normal distribution. The Role of the Q-function in BER vs. SNR The Q-function is widely used in the calculation of the Bit Error Rate (BER) in communication systems, particularly in systems like Binary Phase Shift Ke...

Wireless Communication Interview Questions | Page 2

Wireless Communication Interview Questions Page 1 | Page 2| Page 3| Page 4| Page 5   Digital Communication (Modulation Techniques, etc.) Importance of digital communication in competitive exams and core industries Q. What is coherence bandwidth? A. See the answer Q. What is flat fading and slow fading? A. See the answer . Q. What is a constellation diagram? Q. One application of QAM A. 802.11 (Wi-Fi) Q. Can you draw a constellation diagram of 4QPSK, BPSK, 16 QAM, etc. A.  Click here Q. Which modulation technique will you choose when the channel is extremely noisy, BPSK or 16 QAM? A. BPSK. PSK is less sensitive to noise as compared to Amplitude Modulation. We know QAM is a combination of Amplitude Modulation and PSK. Go through the chapter on  "Modulation Techniques" . Q.  Real-life application of QPSK modulation and demodulation Q. What is  OFDM?  Why do we use it? Q. What is the Cyclic prefix in OFDM?   Q. In a c...

BER performance of QPSK with BPSK, 4-QAM, 16-QAM, 64-QAM, 256-QAM, etc

📘 Overview 📚 QPSK vs BPSK and QAM: A Comparison of Modulation Schemes in Wireless Communication 📚 Real-World Example 🧮 MATLAB Code 📚 Further Reading   QPSK provides twice the data rate compared to BPSK. However, the bit error rate (BER) is approximately the same as BPSK at low SNR values when gray coding is used. On the other hand, QPSK exhibits similar spectral efficiency to 4-QAM and 16-QAM under low SNR conditions. In very noisy channels, QPSK can sometimes achieve better spectral efficiency than 4-QAM or 16-QAM. In practical wireless communication scenarios, QPSK is commonly used along with QAM techniques, especially where adaptive modulation is applied. Modulation Bits/Symbol Points in Constellation Usage Notes BPSK 1 2 Very robust, used in weak signals QPSK 2 4 Balanced speed & reliability 4-QAM ...

Difference between AWGN and Rayleigh Fading

📘 Introduction, AWGN, and Rayleigh Fading 🧮 Simulator for the effect of AWGN and Rayleigh Fading on a BPSK Signal 🧮 MATLAB Codes 📚 Further Reading Wireless Signal Processing Gaussian and Rayleigh Distribution Difference between AWGN and Rayleigh Fading 1. Introduction Rayleigh fading coefficients and AWGN, or Additive White Gaussian Noise (AWGN) in Wireless Channels , are two distinct factors that affect a wireless communication channel. In mathematics, we can express it in that way. Fig: Rayleigh Fading due to multi-paths Let's explore wireless communication under two common noise scenarios: AWGN (Additive White Gaussian Noise) and Rayleigh fading. y = h*x + n ... (i) Symbol '*' represents convolution. The transmitted signal x is multiplied by the channel coeffic...

Antenna Gain-Combining Methods - EGC, MRC, SC, and RMSGC

📘 Overview 🧮 Equal gain combining (EGC) 🧮 Maximum ratio combining (MRC) 🧮 Selective combining (SC) 🧮 Root mean square gain combining (RMSGC) 🧮 Zero-Forcing (ZF) Combining 🧮 MATLAB Code 📚 Further Reading  There are different antenna gain-combining methods. They are as follows. 1. Equal gain combining (EGC) 2. Maximum ratio combining (MRC) 3. Selective combining (SC) 4. Root mean square gain combining (RMSGC) 5. Zero-Forcing (ZF) Combining  1. Equal gain combining method Equal Gain Combining (EGC) is a diversity combining technique in which the receiver aligns the phase of the received signals from multiple antennas (or channels) but gives them equal amplitude weight before summing. This means each received signal is phase-corrected to be coherent with others, but no scaling is applied based on signal strength or channel quality (unlike MRC). Mathematically, for received signa...