To simulate Cognitive Ad-Hoc Networks (CRAHNs) in MATLAB which has step-by-step guidelines that needs to includes designing cognitive radio capabilities within an ad-hoc network environment. CRAHNs permit dynamic spectrum access that allowing unlicensed users (secondary users, SUs) to opportunistically access the spectrum once licensed users (primary users, PUs) are not dynamic. In such networks, cognitive radios can detect the spectrum, allow communication amongst SUs, and prevent the interference with PUs.
Steps to Simulate Cognitive Ad Hoc Network Projects in MATLAB
Step 1: Install Required Toolboxes
Make sure we have installed MATLAB toolboxes on the system:
- Communications Toolbox (for wireless communication and modulation techniques)
- Optimization Toolbox (for resource optimization and spectrum allocation)
- Parallel Computing Toolbox (optional for large-scale network simulations)
- Signal Processing Toolbox (for spectrum sensing and signal analysis)
Step 2: Define System Parameters
Describe the system parameters for the cognitive ad-hoc network:
- Number of Primary Users (PUs): Licensed users along with priority access to the spectrum.
- Number of Secondary Users (SUs): Unlicensed users, which opportunistically access the spectrum.
- Spectrum Bands: The amount of frequency channels is obtainable.
- Transmission Power, Range, and Path Loss Model: To design wireless communication characteristics.
- Spectrum Sensing Accuracy: For spectrum sensing, we can find probability of detection and false alarm
% Network parameters
numPUs = 5; % Number of primary users
numSUs = 10; % Number of secondary users
numChannels = 5; % Number of available spectrum bands
% Define spectrum parameters (for example, using GHz range)
frequencyBands = linspace(2.4e9, 2.5e9, numChannels); % Frequency bands in Hz
% Transmission power and range
transmitPower = 0.1; % Transmit power in watts
sensingThreshold = -100; % Sensing threshold in dBm for spectrum sensing
Step 3: Spectrum Sensing
Cognitive radios require detecting the spectrum to identify whether it is engaged by a PU. We can design the spectrum sensing process utilizing energy detection or more sophisticated detection techniques such as cyclostationary feature detection or matched filtering.
Example: Energy Detection Model for Spectrum Sensing
% Generate a random signal representing PU transmission
SNR = -5; % Signal-to-noise ratio in dB
N = 1000; % Number of samples for sensing
PU_signal = sqrt(10^(SNR/10)) * randn(1, N); % Simulated PU signal with noise
% Energy detection
energy = sum(PU_signal.^2); % Sum of the squared signal (energy detection)
if energy > sensingThreshold
disp(‘Primary User detected’);
else
disp(‘Spectrum is available for Secondary User’);
end
Step 4: Spectrum Access and Dynamic Spectrum Allocation
If the spectrum is identifies as free then the SUs can access the channel. We can execute dynamic spectrum access (DSA) with the help of algorithms such as:
- Opportunistic Spectrum Access: When no PU is identified, the spectrum only accesses by SUs.
- Spectrum Sharing: SUs and PUs distribute the spectrum utilizing the methods such as power control or interference avoidance.
Example: Dynamic Spectrum Access
% Opportunistic Spectrum Access (OSA) for SUs
spectrumStatus = randi([0 1], 1, numChannels); % 1 means channel occupied by PU, 0 means available
% Assign spectrum to SUs based on availability
for su = 1:numSUs
availableChannels = find(spectrumStatus == 0);
if ~isempty(availableChannels)
assignedChannel = availableChannels(randi(length(availableChannels)));
disp([‘Secondary User ‘, num2str(su), ‘ assigned to Channel ‘, num2str(assignedChannel)]);
else
disp([‘No available channels for Secondary User ‘, num2str(su)]);
end
end
Step 5: Routing in Cognitive Ad-Hoc Networks
In ad-hoc networks, routing executes as an important role. Cognitive radios utilize the dynamic routing algorithms to discover the optimal routes rely on spectrum availability and network topology. General algorithms contain:
- Cognitive AODV (Ad-hoc On-demand Distance Vector Routing): Adjusts the AODV protocol for spectrum availability.
- Cognitive DSR (Dynamic Source Routing): Source routing along with spectrum-awareness.
Example: Cognitive AODV-based Route Selection
% Define the topology of the ad-hoc network (example with random node placement)
numNodes = numSUs; % Number of nodes in the ad-hoc network
positions = rand(numNodes, 2) * 100; % Random positions of nodes in a 100×100 grid
% Define a simple connectivity based on distance and spectrum availability
range = 30; % Transmission range of the nodes
connectivityMatrix = zeros(numNodes);
for i = 1:numNodes
for j = i+1:numNodes
distance = norm(positions(i,:) – positions(j,:));
if distance < range
% Assume spectrum is available, establish link
connectivityMatrix(i,j) = 1;
connectivityMatrix(j,i) = 1;
end
end
end
% Display connectivity matrix
disp(‘Connectivity Matrix:’);
disp(connectivityMatrix);
Step 6: Interference Management
To prevent the interference along with PUs, SUs require modifying their transmission power and then preventing using occupied channels. It can be attained by power control algorithms or spectrum handoff strategies.
Example: Power Control Algorithm
% Define transmission power control for SUs
maxPower = 0.1; % Maximum transmission power (W)
interferenceThreshold = -80; % Interference threshold in dBm
% Power control based on interference with PU
for su = 1:numSUs
if rand() < 0.5 % Random chance of interference (simplified model)
% Reduce power to avoid interference
transmitPower = maxPower / 2;
disp([‘Secondary User ‘, num2str(su), ‘ reduces power to avoid interference’]);
else
% Use full power
transmitPower = maxPower;
disp([‘Secondary User ‘, num2str(su), ‘ uses full power’]);
end
end
Step 7: Simulation of Network Performance Metrics
After configuring the simple modules of the Cognitive Ad-Hoc Network then we can replicate performance parameters such as:
- Spectrum Utilization: The percentage of obtainable spectrum utilized by SUs.
- Interference Levels: SUs to PUs trigger the level of interference.
- Throughput: The amount of data effectively sent by SUs.
- Delay: The delay experienced by SUs within accessing the routing data and spectrum.
Example: Calculate Spectrum Utilization
% Calculate the percentage of spectrum used by SUs
totalChannels = numChannels;
usedChannels = sum(spectrumStatus == 0); % Number of channels available to SUs
spectrumUtilization = (usedChannels / totalChannels) * 100;
disp([‘Spectrum Utilization: ‘, num2str(spectrumUtilization), ‘%’]);
Step 8: Cognitive Handoff (Spectrum Handoff)
Once a PU turn into dynamic on a channel currently utilized by an SU, the SU need to execute a handoff to alternative available channel. Spectrum handoff approaches make certain seamless communication without interjecting the ongoing connections.
Example: Spectrum Handoff Process
% Check if PU becomes active on a channel used by SU
for su = 1:numSUs
assignedChannel = find(spectrumStatus == 0, 1); % Current channel assigned to SU
if rand() < 0.2 % Random chance of PU becoming active
disp([‘Primary User becomes active on Channel ‘, num2str(assignedChannel)]);
disp([‘Secondary User ‘, num2str(su), ‘ performs handoff to another channel’]);
newChannel = find(spectrumStatus == 0, 1, ‘first’);
if ~isempty(newChannel)
disp([‘Handoff to Channel ‘, num2str(newChannel)]);
else
disp(‘No available channels for handoff’);
end
end
end
Step 9: Advanced Features (Optional)
- Machine Learning for Spectrum Sensing: We can execute the machine learning algorithms like reinforcement learning for more efficient allocation and spectrum sensing.
- Cooperative Sensing: Several SUs can distribute sensing data to enhance the detection accuracy.
- Energy Efficiency: Combine energy-efficient routing and transmission schemes for SUs to maintain battery power.
Step 10: Visualize Results
Using MATLAB’s plotting functions to envision the network performance, spectrum utilization, or interference levels.
Example: Plot Network Topology
figure;
gplot(connectivityMatrix, positions, ‘-o’);
title(‘Cognitive Ad-Hoc Network Topology’);
xlabel(‘X Position’);
ylabel(‘Y Position’);
We had offered complete guide for you to simulate and visualize the Cognitive Adhoc Network projects through the above simulation method with instance coding using MATLAB environment. We are equipped to present further insights on these projects in another manual. If you want to simulate the Cognitive Ad Hoc Network Projects using MATLAB, you can count on us! We’re here to provide you with great guidance, so just send us a message, and we’ll help you out. Our experts are ready to assist you with secondary users (SUs) and primary users (PUs).
