MATLAB Code

clc;

clear all; close all;

Data_sym = [0 1 1 0 1 0 0 1];

M = 4;

Phase = 0;

Sampling_rate = 48e3;

Data_Rate = 100;

Bandwidth = 400;

Upsampling_factor = Sampling_rate/Data_Rate;

Rolloff = 0.4;

Upsampled_Data = upsample(pskmod(Data_sym,M,Phase),Upsampling_factor);

Pulse_shape = firrcos(2*Upsampling_factor,Bandwidth/2,Rolloff,Sampling_rate,'rolloff','sqrt');

Output

What if we change the roll-off

roll-off = 0.01

roll-off = 0.99

What if we change the bandwidth

Bandwidth = 100 Hz

Bandwidth = 1000 Hz

What if we change the sampling rate

Sampling rate = 10 KHz

Sampling rate = 100 KHz

Another MATLAB Code

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

% Parameters
fs = 1000; % Sampling frequency in Hz
symbolRate = 100; % Symbol rate (baud)
span = 6; % Filter span in symbols
alpha = 0.25; % Roll-off factor for raised cosine filter

% Generate random data symbols
numSymbols = 100; % Number of symbols
data = randi([0 1], numSymbols, 1) * 2 - 1; % Generate random binary data (BPSK symbols: -1, 1)

% Upsample the data to match sampling rate
samplesPerSymbol = fs / symbolRate; % Samples per symbol based on fs and symbol rate
dataUpsampled = upsample(data, samplesPerSymbol);

% Create a raised cosine filter
rcFilter = rcosdesign(alpha, span, samplesPerSymbol, 'sqrt'); % Square root raised cosine filter

% Apply the filter to the upsampled data
txSignal = conv(dataUpsampled, rcFilter, 'same');

figure;
subplot(4,1,1)
stem(data);
title('Original Message signal');
grid on;

subplot(4,1,2)
plot(dataUpsampled);
title('Upsampled Message signal');
grid on;

subplot(4,1,3)
plot(rcFilter);
title('Raise Cosine Filter Coefficient');
grid on;

subplot(4,1,4)
plot(txSignal);
title('Transmitted Signal after Raised Cosine Filtering');
grid on;

Output

Copy the MATLAB Code above from here

% The code is developed by SalimWireless.Com clc; clear; close all; % Parameters fs = 1000; % Sampling frequency in Hz symbolRate = 100; % Symbol rate (baud) span = 6; % Filter span in symbols alpha = 0.25; % Roll-off factor for raised cosine filter % Generate random data symbols numSymbols = 100; % Number of symbols data = randi([0 1], numSymbols, 1) * 2 - 1; % Generate random binary data (BPSK symbols: -1, 1) % Upsample the data to match sampling rate samplesPerSymbol = fs / symbolRate; % Samples per symbol based on fs and symbol rate dataUpsampled = upsample(data, samplesPerSymbol); % Create a raised cosine filter rcFilter = rcosdesign(alpha, span, samplesPerSymbol, 'sqrt'); % Square root raised cosine filter % Apply the filter to the upsampled data txSignal = conv(dataUpsampled, rcFilter, 'same'); figure; subplot(4,1,1) stem(data); title('Original Message signal'); grid on; subplot(4,1,2) plot(dataUpsampled); title('Upsampled Message signal'); grid on; subplot(4,1,3) plot(rcFilter); title('Raise Cosine Filter Coefficient'); grid on; subplot(4,1,4) plot(txSignal); title('Transmitted Signal after Raised Cosine Filtering'); grid on; web('https://www.google.com/search?q=salimwireless.com+raised%20cosine%20filter', '-browser');

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Raised Cosine Filter Online Simulator

Random Bits

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BPSK Mapping (±1)

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Upsampling

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RRC (Root Raised Cosine) Filter Pulse Shaping

Number of Symbols: Samples per Symbol (Oversampling): Noise Std Dev: RRC Roll-off Factor (β):

Simulation Note: The simulator generates random BPSK symbols, applies pulse shaping using a Root Raised Cosine (RRC) filter with roll-off factor β , adds AWGN noise, and detects symbols using a matched filter.
Lower β values use less bandwidth but pulses are narrower in time and more susceptible to inter-symbol interference (ISI). Higher β values spread the pulse in time but reduce ISI and require more bandwidth.

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Raised Cosine Filter in MATLAB (with Simulator)

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