Hi,
Certainly! Below is a basic MATLAB code to simulate cooperative spectrum sensing using an energy detector with a majority fusion rule. The code demonstrates how multiple radios (or sensors) can individually detect the presence of a signal and then send their local decisions to a fusion center, which makes a global decision based on the majority rule.
MATLAB Code
% Parameters
numRadios = 5; % Number of radios
numSamples = 1000; % Number of samples per radio
snr_dB = -5; % Signal-to-noise ratio in dB
threshold = 2; % Energy detection threshold
p_error = 0.1; % Probability of error in the channel
% Generate signals
signalPresent = randn(numSamples, numRadios) + sqrt(10^(snr_dB/10)) * randn(numSamples, numRadios);
signalAbsent = randn(numSamples, numRadios);
% Choose whether the signal is present or absent
signal = signalPresent; % or signalAbsent for absence
% 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
fprintf('Local Decisions: %s\n', mat2str(localDecisions));
fprintf('Received Decisions at Fusion Center: %s\n', mat2str(receivedDecisions));
fprintf('Global Decision: %d\n', globalDecision);
Explanation
1. Parameters:
- `numRadios`: Number of sensors participating in the sensing process.
- `numSamples`: Number of samples each sensor collects.
- `snr_dB`: Signal-to-noise ratio in decibels, which affects the signal strength.
- `threshold`: The energy threshold for local decision-making.
- `p_error`: Probability of error in the communication channel between sensors and the fusion center.
2. Signal Generation:
- `signalPresent` represents the case where the signal is present.
- `signalAbsent` represents the background noise (signal absent).
- You can toggle between these to simulate different scenarios.
3. Energy Detection:
- Each sensor calculates the energy of the received samples and makes a local decision by comparing it to the threshold.
4. Channel Modeling:
- A binary symmetric channel is simulated where each local decision might be flipped with a probability `p_error`.
5. Global Decision:
- The fusion center applies the majority rule to the received decisions to make a global decision.
Considerations
- Threshold Adjustment: The threshold should be chosen based on the desired trade-off between detection probability and false alarm rate.
- Channel Error Probability: Adjust `p_error` to simulate different levels of channel reliability.
- SNR Variation: Change `snr_dB` to see how the system performs under different signal conditions.
This code provides a fundamental framework for your thesis on cooperative spectrum sensing using an energy detector and majority fusion rule. You can expand and modify it based on the specific requirements and scenarios in your research.
Thank you.
