Answered step by step
Verified Expert Solution
Link Copied!

Question

1 Approved Answer

Could you help to fix error of this MATLAB code. + Error: Undefined function or variable 'music _ spectrum'. Error in Assignment _ 2 (

Could you help to fix error of this MATLAB code.
+ Error: Undefined function or variable 'music_spectrum'.
Error in Assignment_2(line 17)
P_music = music_spectrum(theta_scan, A, noise_eigvecs);
+ Code:
% Part (b): Accuracy for Different Noise Variances
noise_variances = linspace(0.1* sigma_n2,10* sigma_n2,10);
mse_noise = zeros(size(noise_variances));
for var_idx =1:length(noise_variances)
var = noise_variances(var_idx);
mse_values = zeros(1,100); % Preallocate for Monte Carlo simulations
for sim =1:100
noise = sqrt(var/2)*(randn(M, N)+1j * randn(M, N));
X = A * S + noise;
R =(X * X')/ N;
[eigvecs, eigvals]= eig(R);
[~, ind]= sort(diag(eigvals), 'descend');
noise_eigvecs = eigvecs(:, ind(size(S,2)+1:end));
P_music = music_spectrum(theta_scan, A, noise_eigvecs);
[~, max_idx]= max(P_music);
estimated_doa = theta_scan(max_idx);
mse_values(sim)= compute_mse(thetas, estimated_doa);
end
mse_noise(var_idx)= mean(mse_values);
end
% Part (c): Accuracy for Different Number of Sensors
sensor_counts = M:10*M;
mse_sensors = zeros(size(sensor_counts));
for m_idx =1:length(sensor_counts)
m = sensor_counts(m_idx);
A = steering_vector(m, d, thetas);
mse_values = zeros(1,100); % Preallocate for Monte Carlo simulations
for sim =1:100
noise = sqrt(sigma_n2/2)*(randn(m, N)+1j * randn(m, N));
X = A * S(1:m, :) + noise;
R =(X * X')/ N;
[eigvecs, eigvals]= eig(R);
[~, ind]= sort(diag(eigvals), 'descend');
noise_eigvecs = eigvecs(:, ind(size(S,2)+1:end));
P_music = music_spectrum(theta_scan, A, noise_eigvecs);
[~, max_idx]= max(P_music);
estimated_doa = theta_scan(max_idx);
mse_values(sim)= compute_mse(thetas, estimated_doa);
end
mse_sensors(m_idx)= mean(mse_values);
end
% Part (d): Accuracy for Different Number of Snapshots
snapshot_counts = round(linspace(0.1* N,10* N,10));
mse_snapshots = zeros(size(snapshot_counts));
for n_idx =1:length(snapshot_counts)
n = snapshot_counts(n_idx);
mse_values = zeros(1,100); % Preallocate for Monte Carlo simulations
for sim =1:100
noise = sqrt(sigma_n2/2)*(randn(M, n)+1j * randn(M, n));
X = A * S(:,1:n)+ noise;
R =(X * X')/ n;
[eigvecs, eigvals]= eig(R);
[~, ind]= sort(diag(eigvals), 'descend');
noise_eigvecs = eigvecs(:, ind(size(S,2)+1:end));
P_music = music_spectrum(theta_scan, A, noise_eigvecs);
[~, max_idx]= max(P_music);
estimated_doa = theta_scan(max_idx);
mse_values(sim)= compute_mse(thetas, estimated_doa);
end
mse_snapshots(n_idx)= mean(mse_values);
end
% Plotting MSE results
figure('Position',[100,100,1000,800]);
% Noise Variance
subplot(3,1,1);
plot(noise_variances, mse_noise, 'o-');
title('MSE vs. Noise Variance');
xlabel('Noise Variance');
ylabel('MSE');
% Number of Sensors
subplot(3,1,2);
plot(sensor_counts, mse_sensors,'o-');
title('MSE vs. Number of Sensors');
xlabel('Number of Sensors');
ylabel('MSE');
% Number of Snapshots
subplot(3,1,3);
plot(snapshot_counts, mse_snapshots, 'o-');
title('MSE vs. Number of Snapshots');
xlabel('Number of Snapshots');
ylabel('MSE');
sgtitle('MSE Analysis');
% Function to compute MSE of DOA estimates
function mse = compute_mse(true_doa, estimated_doa)
mse = mean((true_doa - estimated_doa).^2);
end

Step by Step Solution

There are 3 Steps involved in it

Step: 1

blur-text-image

Get Instant Access to Expert-Tailored Solutions

See step-by-step solutions with expert insights and AI powered tools for academic success

Step: 2

blur-text-image

Step: 3

blur-text-image

Ace Your Homework with AI

Get the answers you need in no time with our AI-driven, step-by-step assistance

Get Started

Recommended Textbook for

Pro SQL Server Administration

Authors: Peter Carter

1st Edition

1484207106, 9781484207109

More Books

Students also viewed these Databases questions