%Initialising MSE arrays for different SNR observations:

To initialize MSE (Mean Squared Error) arrays for different SNR (Signal-to-Noise Ratio) observations, you can use the following code in Python:

import numpy as np

# Define SNR values
snr_values = [5, 10, 15, 20]

# Define number of experiments
num_experiments = 10

# Initialize MSE arrays
mse_arrays = []
for snr in snr_values:
    mse_arrays.append(np.zeros(num_experiments))

# Print the shape of the MSE arrays
for i in range(len(snr_values)):
    print("MSE array shape for SNR = {}: {}".format(snr_values[i], mse_arrays[i].shape))

In this code, we first define the SNR values we want to observe and the number of experiments we want to perform for each SNR value. Then, we initialize a list `mse_arrays` to store the MSE arrays for each SNR value. We loop through each SNR value and initialize a 1D array of zeros with length equal to the number of experiments, and append it to `mse_arrays`.

Finally, we print the shape of each MSE array to verify that they all have the same length as the number of experiments.