To simulate the Cognitive Radio Networks (CRNs) using MATLAB which requires encompassing to design the dynamic spectrum access in which secondary users (cognitive users) can be used the spectrum once it is not engaged by the primary users (licensed users). Cognitive Radio Networks concentrate on spectrum sensing, decision-making, and spectrum sharing that permits for more effective use of radio frequencies. Below is a simple guide for simulating Cognitive Radio Networks (CRN) projects using MATLAB:
Steps to Simulate Cognitive Radio Networks Projects in MATLAB
- Define System Parameters
Initially, we describe the simple metrics of the Cognitive Radio Network, which containing the amount of primary and secondary users, bandwidth, and the frequency range.
Example: Define basic parameters for a Cognitive Radio Network.
% Cognitive Radio Network Parameters
numPrimaryUsers = 2; % Number of primary users (licensed)
numSecondaryUsers = 3; % Number of secondary users (cognitive)
bandwidth = 10e6; % Bandwidth in Hz (10 MHz)
carrierFrequency = 2.4e9; % Carrier frequency in Hz (2.4 GHz)
snrThreshold = 10; % SNR threshold for spectrum sensing
% Display system parameters
disp(‘Cognitive Radio Network Parameters:’);
disp([‘Primary Users: ‘, num2str(numPrimaryUsers)]);
disp([‘Secondary Users: ‘, num2str(numSecondaryUsers)]);
disp([‘Bandwidth: ‘, num2str(bandwidth / 1e6), ‘ MHz’]);
disp([‘Carrier Frequency: ‘, num2str(carrierFrequency / 1e9), ‘ GHz’]);
disp([‘SNR Threshold for Spectrum Sensing: ‘, num2str(snrThreshold), ‘ dB’]);
- Model Spectrum Sensing
In Cognitive Radio Networks, secondary users require sensing the spectrum to identify whether it is free or engaged by primary users. We need to replicate the spectrum sensing with the support of energy detection in which the secondary users are calculate the received signal strength and relate it to a threshold.
Example: Simulate energy detection for spectrum sensing.
% Generate random signals for primary users
primarySignal = randn(1, bandwidth) + 1i * randn(1, bandwidth); % Primary user signal
% Add noise to simulate the environment
noise = (randn(1, bandwidth) + 1i * randn(1, bandwidth)) * 0.1; % AWGN noise
receivedSignal = primarySignal + noise; % Received signal at the secondary user
% Energy detection for spectrum sensing
energy = sum(abs(receivedSignal).^2); % Calculate the energy of the received signal
if energy > snrThreshold
disp(‘Spectrum occupied by primary user’);
else
disp(‘Spectrum available for secondary user’);
end
- Simulate Dynamic Spectrum Access
When the secondary users are identify that the spectrum is obtainable then they can access it actively. We can replicate the decision-making process in which secondary users are selecting whether to access the spectrum according to the sensing outcomes.
Example: Simulate dynamic spectrum access for secondary users.
% Define the time slots and whether the primary user is active
timeSlots = 100; % Total number of time slots
primaryActive = randi([0 1], 1, timeSlots); % Random primary user activity (1 = active, 0 = inactive)
% Simulate secondary user accessing the spectrum
secondaryAccess = zeros(1, timeSlots);
for t = 1:timeSlots
if primaryActive(t) == 0 % If primary user is inactive
secondaryAccess(t) = 1; % Secondary user can access the spectrum
end
end
% Plot the spectrum access for primary and secondary users
figure;
stairs(1:timeSlots, primaryActive, ‘r’, ‘LineWidth’, 2);
hold on;
stairs(1:timeSlots, secondaryAccess, ‘g’, ‘LineWidth’, 2);
title(‘Spectrum Access by Primary and Secondary Users’);
xlabel(‘Time Slot’);
ylabel(‘Access (1 = Active)’);
legend(‘Primary User’, ‘Secondary User’);
grid on;
- Model Spectrum Sharing
In CRNs, secondary users are distributed the spectrum including other secondary users and the primary users. Spectrum sharing mechanisms need to model to prevent the interference.
Example: Simulate spectrum sharing among multiple secondary users.
% Simulate secondary users accessing different parts of the spectrum
secondaryUser1 = zeros(1, timeSlots);
secondaryUser2 = zeros(1, timeSlots);
% Check for primary user activity and share the spectrum
for t = 1:timeSlots
if primaryActive(t) == 0 % If primary user is inactive
secondaryUser1(t) = 1; % First secondary user accesses the spectrum
if rand > 0.5
secondaryUser2(t) = 1; % Second secondary user may access as well
end
end
end
% Plot the spectrum sharing for secondary users
figure;
stairs(1:timeSlots, secondaryUser1, ‘b’, ‘LineWidth’, 2);
hold on;
stairs(1:timeSlots, secondaryUser2, ‘m’, ‘LineWidth’, 2);
title(‘Spectrum Sharing Among Secondary Users’);
xlabel(‘Time Slot’);
ylabel(‘Access (1 = Active)’);
legend(‘Secondary User 1’, ‘Secondary User 2’);
grid on;
- Simulate Power Control and Interference
Secondary users need to handle their transmit power to prevent the interfering with the primary users. We can replicate the power control mechanisms, which modify the secondary user’s transmit power rely on the channel conditions and interference constraints.
Example: Replicate power control for secondary users.
% Power control parameters
maxPower = 10; % Maximum transmit power in dBm
interferenceThreshold = -80; % Interference threshold in dBm
% Simulate power control for secondary user
distanceToPrimary = 100; % Distance between secondary and primary user in meters
pathLossExponent = 3; % Path loss exponent for wireless channel
interferencePower = -10 * pathLossExponent * log10(distanceToPrimary); % Path loss calculation
% Adjust secondary user power to avoid interference
if interferencePower > interferenceThreshold
secondaryPower = maxPower – (interferencePower – interferenceThreshold); % Reduce power
else
secondaryPower = maxPower; % Use maximum power
end
disp([‘Secondary user transmit power: ‘, num2str(secondaryPower), ‘ dBm’]);
- Measure Network Performance Metrics
In Cognitive Radio Networks, crucial performance parameters contain spectrum efficiency, throughput, and packet delivery ratio (PDR). We can compute these parameters to compute the CRN’s performance.
Example: Calculate spectrum efficiency and throughput.
% Parameters for performance metrics
bandwidthAvailable = sum(secondaryAccess) * bandwidth / timeSlots; % Available bandwidth for secondary users
dataRate = 1e6; % Data rate in bits per second (1 Mbps)
% Calculate spectrum efficiency (in bits/s/Hz)
spectrumEfficiency = dataRate / bandwidthAvailable;
disp([‘Spectrum Efficiency: ‘, num2str(spectrumEfficiency), ‘ bits/s/Hz’]);
% Calculate throughput (in bits per second)
throughput = dataRate * sum(secondaryAccess) / timeSlots;
disp([‘Throughput: ‘, num2str(throughput), ‘ bits per second’]);
- Advanced Cognitive Radio Network Simulations
For more innovative CRN projects, we can discover to these topics likes:
- Cooperative Spectrum Sensing: Several secondary users are work together to sense the spectrum more exactly.
- Game-Theoretic Spectrum Sharing: Execute the game theory for fair spectrum allocation between several secondary users.
- Cognitive Radio with Machine Learning: Utilize the machine learning algorithms to enhance the spectrum sensing and decision-making.
- Security in Cognitive Radio Networks: Replicate the secure spectrum access and protection versus attacks such as jamming or spectrum hijacking.
In this manual, we concentrate on how to design the dynamic spectrum access and how to simulate and measure the Cognitive Radio Networks projects utilizing given instructions in MATLAB. Also, we provided more innovative CRN projects ideas that are very useful to you. You can receive expert guidance on spectrum sensing, decision-making, and spectrum sharing for your projects from our team. At phdprime.com, we specialize in simulating Cognitive Radio Networks projects using MATLAB, ensuring timely completion and providing comprehensive explanations for your work.
