Virtual Labs

Which of the following statements best describes the behaviour of the Mean Squared Error (MSE) in the learning curve for the LMS algorithm for AR process (from the figure)?

a: The MSE increases over time b: The MSE remains constant over time c: The MSE decreases rapidly initially and then stabilizes d: The MSE fluctuates randomly with no clear trend

What is the purpose of the phi variable in the simulation?

a: It represents the different angles for plotting the SNR pattern b: It determines the number of antennas c: It sets the number of Monte Carlo runs d: It defines the SNR values

What does the plot generated by the MVDR function represent?

a: The array response for different angles b: The noise power distribution c: The average MVDR beamformer output in dB over various angles d: The signal-to-interference ratio

The AR process is based on frequency domain analysis and it contains:

a: Only poles b: Only zeros c: Both poles and zeros d: Unity transfer function

As the order of AR model is increased, the characteristic of mean square error:

a: Increases b: Decreases c: Remain constant d: First decreases and then increases

Which of the following statements best describes the behaviour of the Mean Squared Error (MSE) in the learning curve for the RLS algorithm (from the figure)?

a: The MSE increases over time b: The MSE remains constant over time c: The MSE decreases rapidly initially and then stabilizes d: The MSE fluctuates randomly with no clear trend

Convergence rate of LMS is fast.

a: True b: False

What is the limitation of the LMS algorithm?

a: Low convergence rate b: Low signal to noise ratio c: Both of above d: None of above

What does the random walk behaviour plot indicate in the context of adaptive filtering (Hint: observe the figure given below)?

a: It shows the variation of the input signal over time b: It compares the actual filter weights with the true value over iterations c: It displays the desired output signal d: It shows the fluctuation of the mean squared error over iterations

After performing the experiments, by comparing the result of LMS and RLS, which one gives better result?

a: RLS based equalizer b: LMS based equalizer

Adaptive Signal Processing