Hi,
To implement cooperative spectrum sensing using an energy detector in MATLAB, you need to perform several steps: sample collection, local decision making, channel modeling, and global decision making using a majority rule at the fusion center (FC). Below is a guideline and MATLAB code to help you achieve this.
Approach
1. Sample Collection:
- Simulate the received signal at each radio sensor. Assume a simple model where the signal is either present or absent.
2. Energy Detection:
- Calculate the energy of the received signal samples at each sensor.
3. Local Decision Making:
- Compare the calculated energy with a threshold to make a binary decision (0 for absence, 1 for presence).
4. Channel Modeling:
- Model the channel between each sensor and the fusion center, typically using a binary symmetric channel (BSC) to simulate errors in transmission.
5. Global Decision Making:
- Use the majority rule to combine the local decisions at the fusion center.
MATLAB Code
% Parameters
numRadios = 5; % Number of radios
numSamples = 1000; % Number of samples per radio
snr = -5; % Signal-to-noise ratio in dB
threshold = 2; % Energy detection threshold
p_error = 0.1; % Probability of error in channel
% Generate signals
signalPresent = randn(numSamples, numRadios) + sqrt(10^(snr/10)) * randn(numSamples, numRadios);
signalAbsent = randn(numSamples, numRadios);
% Choose whether the signal is present or absent
signal = signalPresent; % or signalAbsent
% Energy detection
energy = sum(signal.^2);
% Local decision making
localDecisions = energy > threshold;
% Simulate channel errors using a binary symmetric channel
receivedDecisions = localDecisions;
for i = 1:numRadios
if rand < p_error
receivedDecisions(i) = ~receivedDecisions(i);
end
end
% Global decision using majority rule
globalDecision = sum(receivedDecisions) > numRadios / 2;
% Display results
disp('Local Decisions:');
disp(localDecisions);
disp('Received Decisions at Fusion Center:');
disp(receivedDecisions);
disp('Global Decision:');
disp(globalDecision);
Explanation
- Sample Generation: The code generates random Gaussian samples for the signal. You can switch between `signalPresent` and `signalAbsent` to simulate different conditions.
- Energy Detection: The energy of the samples is computed and compared against a threshold to make local decisions.
- Channel Modeling: A simple binary symmetric channel is modeled by flipping the decision with a probability `p_error`.
- Majority Rule: The fusion center uses the majority rule to make a global decision. If more than half of the radios detect the signal, the fusion center decides that the signal is present.
Considerations
- Threshold Selection: The threshold for energy detection should be set based on the desired false alarm and detection probabilities.
- Channel Model: The binary symmetric channel is a simple model. Depending on your application, you might need a more complex model that accounts for fading or other channel characteristics.
- SNR and Noise: Adjust the SNR and noise model according to your specific scenario to better simulate real-world conditions.
This code provides a basic framework for cooperative spectrum sensing using an energy detector and demonstrates how to implement local and global decision-making processes in MATLAB.
Thank you.
