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讲解 CAN202 Analogue and Digital Communications I Coursework AY202324辅导 Matlab语言

CAN202 Analogue and Digital Communications I Coursework AY202324

Instructions:

1.   100 marks are available from this coursework (20% towards the total mark of CAN202)

2.   Please submit one PDF file that contains your answers and CORRECT student ID.

3.   Release date of the coursework: Friday 5th April 2024.

4.   Due date of the coursework: 23:59, Monday 6th  May 2024.

5.   There are 11 questions in total. Answer ALL questions.

6.   If asked, support your answer with figures. Make sure the figures are READABLE.

7.   Appendall necessary codes at the end of the to-be-submitted PDF or at the corresponding answers.

8.   No generative AI may be used when completing the coursework.

9.   Learning outcome accessed: A, B, E.

10. The usual late-submission policy may apply (e.g., 5 marks deduction per working day).

The questions begin:

Figure 1 shows a periodic triangular waves(t), where the period is T seconds.

Figure 1 A periodic triangular wave

Q1 Show sufficient steps to verify that a Fourier series representation of s(t) in Figure 1  is

where c0  = 0 and  (Hint: It maybe easier to find the Fourier series representation of  and then find  where the latter comes from a property of Fourier series that links the Fourier series coefficients between a periodic signal and its integration)        (10 marks)

Q2 Rewrite the Fourier series of s(t) in the following form,i.e.,

where you need to determine Dn  and θn.                                                                  (5 marks)

Then, suppose there is a bandlimited modulating signal m(t) whose bandwidth is  Hz. Plot the frequency spectrum of m(t)s(t). You may assume that the frequency spectrum of m(t) has the shape in Figure 2.       (10 marks)

 

Figure 2 An illustration of the frequency spectrum of m(t)

Q3 We can generate a double-sideband suppressed carrier amplitude modulated (DSB-SC AM) signal based on m(t) × s(t) and an appropriate bandpass filter (BPF), where the carrier frequency is T−1  Hz. Draw a diagram that realizes such a DSB-SC AM scheme, where the functions of all components in the diagram must be specified.          (5 marks)

In the second part of the assignment, we will scramble the frequency components of a piece of soundtrack that somehow disguise the sound, and then we descramble and restore the soundtrack. We will use the soundtrack in “ handel.mat” . By typing the command “ load handel” in MATLAB, you will find two variables in the workspace,i.e., “y” and “ Fs”, where     “y” contains samples of the soundtrack and “ Fs” specifies the sampling frequency in Hz that gives rise to the samples in “y” . We may treat “Fs/2” as the bandwidth of the soundtrack.

After loading “handel”, we may play the soundtrack using the following command: “ player = audioplayer(y, Fs); play(player);”

If your loudspeaker works, you should hear “ hallelujah, hallelujah, …” .

Figure 3 shows a simple scrambler that scrambles the frequency spectrum of “y” and give rise to “z” . Figure 4 shows a descrambler that would, ideally, restore the frequency spectrum of “y” from “z” .

Figure 3 A scrambler

Figure 4 A descrambler for the scrambler in Figure 3

Q4 In Figure 3, assume y(t) is bandlimited to Fs/2 Hz. Sketch the frequency spectrum of z(t).                         (10 marks)

Q5 Show with illustrating figures that the system in Figure 4 can restore y(t) from z(t).   (10 marks)

We can demonstrate the above in MATLAB, where we will scramble “y” and descramble “z” . Specifically, we calculate the discrete Fourier transform (DFT) of various discrete-time signals in the above scrambler/descrambler system and observe the frequency spectra. We  will also play the soundtrack of z(t) in Figure 3 andy(t) in Figure 4 for verification. The DFT can be calculated using the “fft” function in MATLAB.

The “fft(y)'' returns frequency domain samples from frequency zero to Fs − T0 −1 , where Fs is the sampling frequency of “y” and T0  is the duration of the time-domain signal. Because the  DFT samples are periodic, one may use the command “fftshift( fft( y ) )” to swap the two halves of “fft(y)”, such that the zero-frequency component appears at the center of the vector returned by “fftshift( fft( y ) )” .

One thing to note is that the DFT calculation is related to the sampling frequency. In Figure 3, we need to multiply y(t) with a carrier signal that has a frequency ofFs, and the resulting signal y1 (t) would have the largest bandwidth (counting from frequency zero) among all signals in the systems of Figures 3 and 4. To use DFT correctly for y1 (t), we need to have a sampling frequency that is at least twice as much as the bandwidth of y1 (t) (again, counting from frequency zero). However, the soundtrack from “handel” is not sampledata sufficiently high frequency, so we need to upsample the soundtrack first. The upsampling can be done using the command “resample(y, Fs_new,Fs)”, where Fs_new is the new sampling frequency that is sufficiently large.

Please answer the following questions based on MATLAB programming.

Q6 Load “handel” in MATLAB and play the soundtrack. Calculate the DFT of the soundtrack samples using “fft”. Then, plot the discrete frequency spectrum as calculated from the DFT, where you should use the command “fftshift” to rearrange the results from “fft”. Label the frequency values of the samples from the frequency spectrum.          (5 marks)

Q7 Perform upsampling to the vector “y” from “handel”, where the new sampling frequency  is Fs × 2. Play the upsampled soundtrack and make sure that it sounds the   same as that in Q6.                                 (5 marks)

Q8 Generate samples of y1 (t) (refer to Figure 3), where you may need to sample a correct carrier signal with the correct sampling frequency. Plot the DFT ofy1 (t) in MATLAB and  label the frequencies; explain whether this plot meets your expectation.      (10 marks)

Q9 Perform lowpass filtering to the samples of y1 (t) and obtain samples of y2 (t). The sampled impulse response of the lowpass filter may come from a truncated sinc pulse that approximates an ideal lowpass filter with a bandwidth of Fs. You may then use “conv” to perform. convolution (or, equivalently, the filtering operation in the time- domain). Plot the DFT ofy2 (t).         (10 marks)

Q10      Following Figure 3, obtain samples of the scrambled soundtrack z(t) in MATLAB. Plot the DFT of z(t) and play the time-domain samples. Comment on what you hear from z(t).                         (10 marks)

Q11      Following Figure 4,descramble z(t) and obtain y′ (t). Plot the DFT ofy′ (t) in MATLAB. Also, play the samples of y′ (t). Comment your observations.              (10 marks)





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