Hi Kasun,
You can generate a BPSK sample data set in MATLAB using the randi function. Here's an example code snippet to generate a BPSK signal with 1000 bits:
bits = randi([0 1], 1000, 1); % generate random bits
bpsk = 2*bits - 1; % BPSK modulation
This will generate a vector bpsk with values of either -1 or 1, representing the BPSK signal.
To simulate a source-destination direct link, you can add some noise to the signal using the awgn function. Here's an example code snippet:
snr = 10; % signal-to-noise ratio in dB
noisy_bpsk = awgn(bpsk, snr); % add noise to the signal
This will add Gaussian white noise to the bpsk signal with a signal-to-noise ratio of 10 dB, and store the result in noisy_bpsk.
To predict the received data at the destination, you can use machine learning algorithms such as neural networks or support vector machines. You will need to train your model on a set of known input-output pairs, and then use the trained model to predict the output for new input data.
You can read more about the 'randi' and 'awgn' functions here:
Hope this helps!
