[LabVIEW]NBFM/WBFM

Introduction: In this lab, I review the knowledge about the principle of narrow band frequency modulation and wide band frequency modulation. Then I used the LabVIEW to simulate the process and the result of narrow band frequency modulation and wide band frequency modulation. I also analysis the unexpected figure and strange impulse in the output figure. After that, I try to design a walkie talkie by using the knowledge of wide band frequency modulation. After completing designing the walkie talkie, I recorded some output files which were under different frequency deviation and signal noise ratio to compare the impact of these two parameters on quality of audio output. And I also find a method by using another element to get gaussian white noise quickly. Lab results & Analysis： The simulation of NBFM The principle of NBFM modulation and demodulation Figure 1 principle of NBFM modulation and demodulation Figure 2 principle of NBFM demodulation The program diagram: Figure 3 the program diagram The analysis of the result First of all, let’s check the parameters of the signals: The function and name of variables are noted on the top of the elements. Figure 4 parameters of the signals Figure 5 the waveform of baseband The signal is generated by us. It is a sinusoidal waveform function. Figure 6 FM waveform We can use Carson’s rule to compute the bandwidth of the signal, Δf=200HZ fm=2500HZ β=Δffm=0.08<<1 BW≈2fm=5000HZ We look at the figure we get, we can see the bandwidth of the signal is just like to equal to 5000HZ which we have computed by using Carson’s rule. Figure 7 recovered signal In this figure, I don’ know the reason of the impulse occurs near the zero, this impulse is too large. Figure 8 FM waveform in time domain This figure shows us the waveform of the FM signal in time domain. And we can put the baseband signal and FM signal in one diagram to compare them. The waveform obtained is consistent with our expectation, indicating that the simulation experiment is successful. Figure 9 compare The simulation of WBFM The principle of WBFM modulation and demodulation Figure 10 principle of WBFM modulation Figure 11 principle of WBFM demodulation The program diagram Figure 12 program diagram of WBFM   The analysis of the result Figure 13 BWFM waveform And by looking at the picture, we know that the bandwidth of the signal is about 40000HZ. We can use Carson’ rule to estimate the bandwidth of the signal. Δf=16000HZ fm=2500HZ β=Δffm=6.4 BW≈2(fm+Δf)=37000HZ The bandwidth obtained is consistent with our expectation, indicating that the simulation experiment is successful. Figure 14 BWFM waveform in time domain Figure 15 demodulated BWFM signal The wide band frequency modulated signal is in the figure 12. The wide band frequency demodulated signal is in the figure 13. And we can find that the amplitude of signal after demodulation is much larger than modulated signal. WBFM walkie talkie The program diagram: Because I have done the single side band walkie talkie, I think in some aspects, these two walkie talkies might work in the same way. So, I try to design this walkie talkie as same as the single side band walkie talkie. Figure 16 The program diagram The analysis of the result First of all, let’s check the parameters of the signals: The function and name of variables are noted on the top of the elements. Figure 17 parameters of the signals Figure 18 FM waveform in frequency domain The FM waveform in frequency domain is showing in the figure 18. We can see the center frequency is about 100000HZ.   Figure 19 FM waveform in time domain FM waveform in time domain is a sinusoidal waveform function Figure 20 demodulated signal in time domain  We can clearly see that the waveform in figure 20 has several peeks which indicate the response of the sound signal. Figure 21 demodulated signal in frequency domain We can see that the waveform in figure 21 has 12 peeks which indicate the response of the sound signal. Because the sound signal only has 12 words. This result indicate our simulation is successful in another aspect. Figure 22 impact of these two parameters I recorded some output files which were under different frequency deviation and signal noise ratio to compare the impact of these two parameters on quality of audio output. After compare these sound files, I find: The quality of output sound increases with the increase of frequency deviation. The quality of output sound increases with the increase of SNR. The beat frequency deviation in my experiment is 96000HZ. Feedback: the strange impulse of the signal: At the beginning of time domain, a strange impulse occurred in the figure. I try to reset the signal and change the type and orders of filter, but nothing changed. I can not to solve the question. the method to get gaussian white noise avoid waiting: when we want to generate gaussian white noise, if we use element like this the process will be very slow and the computer will be crash easily. This problem bothered me for a long time. In order to improve the speed of my experiment, I need to find another way to generate a gaussian white noise. I find that if we use this element the speed will be much faster than the element on the top. The SSB walkie talkie and WB walkie talkie: In the last lab I designed an SSB walkie talkie, so I compare difference between two walkie talkies. I find that in the same sample frequency, quality of the output file of the WB walkie talkie is much better than the SSB walkie talkie. I think the reason is the WB walkie talkie contains more frequency components than SSB walkie talkie. But the modulation and demodulation process of WB walkie talkie are much more complex than SSB walkie talkie.

Experience Review the knowledge about the principle of narrow band frequency modulation and wide band frequency modulation. I have a deeper understanding of the differences and connections between the two kinds of frequency modulation. Due to the confusion of the block diagram, it is difficult for me to find problems after I find the program had some problem. I’m going to lay out the lines and the components clearly and neatly. The method to avoid wasting time: I found a way to generate a gaussian white noise quickly.