I tried implementing a matched filter to improve sensing SNR using the code below, but I did not get good results. If anyone knows a better way, please help.

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LoCi
LoCi 2023-6-23
评论： Yash 2023-6-25
s_t = (EdeltaS*(lamda^2)/(64*(pi^3)*(r_max^2)))...
*(HBR'*(wc*wc'+ws*ws')*(a_RIS_tem*a_RIS_tem')*diag(ome)*diag(ome')*HBR*HBR'*(a_RIS_tem*a_RIS_tem')*diag(ome)*diag(ome')*HBR*(wrx_in*wrx_in')*sin(theta_tem));
s_t_vec = s_t(:); % reshape s_t into a coloumn vector
% Add AWGN to the transmitted signal
r_target = awgn(s_t_vec, 10^(-9));
% Apply the matched filter
template = conj(s_t_vec(end:-1:1));
filtered_signal = conv(r_target, template, 'same');
% Compute SNR of the filitered signal
SNR = abs(filtered_signal).^2 ./ (var(r_target) + var(filtered_signal));

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Yash
Yash 2023-6-23
There's no need of a completely new approach according to me, you can just make some improvements in your code for better functionality.
1) Change the SNR value used in the awgn function. A value of 10^(-9) may result in an extremely high SNR, leading to unrealistic noise levels. Adjust the SNR value based on your signal characteristics and desired noise level.
2) Instead of converting the s_t matrix into a column vector and then applying the matched filter using conv, you can directly perform matrix multiplication to improve memory efficiency. Refer to the following code.
template = conj(flipud(s_t));
filtered_signal = filter2(template, r_target, 'same');
3) Directly use the power expressions in the calculation of the SNR, instead of squared magnitudes expressions. Refer to the following code.
SNR = abs(filtered_signal).^2 / var(filtered_signal);
Hope this helps.
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LoCi
LoCi 2023-6-23
Thank you for clarification, one more question, my maximum transmit power is 30 dB , how to plot the SNR for each transmit power level?
Yash
Yash 2023-6-25
To plot the SNR for each transmit power level, you can modify the code to loop over a range of transmit power levels, apply the matched filter for each power level, and plot the resulting SNR values. Refer to the following code for the same.
% Generate a longer training sequence
train_seq = repmat(s_t_vec, 10, 1);
% Define the range of transmit power levels
p_range = 0:2:30;
% Initialize the SNR vector
SNR = zeros(size(p_range));
% Loop over the transmit power levels
for i = 1:length(p_range)
% Set the transmit power level
p = 10^(0.1*p_range(i));
% Generate the transmitted signal
s_t = ... % code to generate the transmitted signal
% Add noise to the transmitted signal with a higher SNR threshold
r_target = awgn(s_t, 20);
% Apply the matched filter
template = conj(s_t(end:-1:1));
filtered_signal = conv(r_target, template, 'same');
% Compute the SNR of the filtered signal
SNR(i) = abs(filtered_signal).^2 ./ (var(r_target) + var(filtered_signal));
end
% Plot the SNR vs. transmit power
plot(p_range, SNR);
xlabel('Transmit Power (dB)');
ylabel('SNR');
title('SNR vs. Transmit Power');
Please keep in mind to change each parameter value to a value relevant to your model.

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