To improve the Signal-to-Noise Ratio (SNR) vs Bit Error Rate (BER) performance of your data matrix, you can apply various filtering techniques. Here, I'll demonstrate a basic approach using a simple averaging filter, which can help reduce noise and improve the SNR. This example assumes that your data has been corrupted by noise and you want to improve the SNR for each row.
% Sample 4x4 matrix representing data with different SNR levels
data = [1 2 3 4; 5 6 7 8; 9 10 11 12; 13 14 15 16];
% Initialize the filtered data matrix
filteredData = zeros(size(data));
% Define a simple averaging filter
filterSize = 3; % Size of the averaging filter
filterKernel = ones(1, filterSize) / filterSize; % 1D averaging filter
% Apply the filter to each row
for i = 1:size(data, 1)
% Apply the filter to the current row
filteredData(i, :) = conv(data(i, :), filterKernel, 'same');
end
% Display the original and filtered data
disp('Original Data:');
disp(data);
disp('Filtered Data:');
disp(filteredData);
- Data Matrix: The data matrix is assumed to contain samples of signals with different SNR levels on each row. Replace a, b, c, ..., p with your actual data values.
- Averaging Filter: A simple averaging filter is used here. It smooths the data by averaging over a specified number of adjacent samples. This can help reduce high-frequency noise.
- Convolution: The conv function is used to apply the filter to each row of the matrix. The 'same' option ensures that the output size matches the input size.
- Filtered Data: The filtered data is stored in filteredData, which should have improved SNR compared to the original data.
I hope this helps!
