Refer this article that discusses the method to plot signal space constellations, for the various modulations used in the transmitter. Rate this article: 45 votes, average: 3. COM, PP. Provide your answer by showing calculations. Rayleigh channel used BER curves to show performance…plz i need it urgently…. So my solution for you is at first upsample of your NRZ data arbiary 4,8 or 16 , … and then taking convolution of upsampled data and root raised cosine filter will have give simply the output that you need.
Becasue simply there is more bits to be wrong. But in the cost that your bit rate is the smallest possible in your system. Please check page in the PDF version. I just bought the ebook and copied the same code for the two m files. I got the same error message. Can you check whether the code in your file declares the following on line 11, just before the switch statement on line 12 as shown below?
I mean, in the program provided by you, I changed one of the input parameter in NRZEncoder function from polar to unipolar. If you use unipolar encoding the constellations in trasmitter and receiver will be different. QPSK modulation has to be simulated with polar encoding. Because of that your bits are detected wrongly.
In QPSK, the inphase component is multiplied by cos and quadrature component by sine. So there is no problem here. But when you use unipolar encoding, the inphase or quadrature phase components take value 0 or 1. It is not recommended to use unipolar encoding for QPSK modulation as it defeats the basic architecture of the modulation itself. I tried this code using unipolar and polar line coding.
With polar coding, the reconstructed bit sequence was okay. Can anyone tell me the reason for this behaviour? For this purpose, a NRZ encoder is used. I am trying to understand this program. I unable to understand what is the need for NRZ encoder. Can you explain me please…. I have tried to modify your code to produce the required result but no success.
I also need a polarised version of the odd and even bits plotted. The two modulated signals are then combined to produce QPSK. At the other end, a plot of the two demodulated signals and the instantaneous waveforms of the integrators. That is equation is for non-offset QPSK. Step 3. The quartic spectrum of the received signal is calculated. If the discrete spectral lines at the quadruple frequency do exist, the modulation of the received signal will be determined as QPSK.
Simulation is performed to verify the correctness and effectiveness of the modulation recognition method. The simulation parameters are set as follows: the carrier wave frequency is Because the reasonable threshold values are closely related to the simulation parameters above, a large number of simulations and tests are performed to get the prior knowledge and the threshold values are set as , , and.
According to the first step of the recognition process, three feature parameters are calculated. Firstly, the maximum value of the spectral power density of the normalized-centered instantaneous amplitude is calculated. Figure 2 shows a plot of versus SNR. It can be clearly found that the calculated feature parameter of QPSK signal can meet the condition of.
It can be clearly found that of QPSK signal can meet the condition of , while the of MSK cannot, which prove the rationality of the threshold.
Secondly, the standard deviation of the absolute value of the centered nonlinear component of the instantaneous phase, evaluated over the nonweak intervals , is calculated.
Figure 4 shows a plot of versus SNR. It can be clearly found that he calculated feature parameter can meet the conditions of. Thirdly, the average value of the instantaneous amplitude is calculated. Figure 6 shows a plot of versus SNR. It can be found that the of QPSK signal can meet the condition of , while the of 16QAM cannot, which prove the rationality of the threshold.
According to the second step of the recognition process, the modulation of signals can be determined. Figure 8 indicates that a discrete spectrum line does exist at the quadruple frequency of the QPSK signal, while Figure 9 indicates that no discrete spectrum lines exist at the quadruple frequency of the 8PSK signal. By calculating the ratio of the maximum peak value to the mean value in the quartic spectrum and comparing it with the threshold values, it can be determined whether the signal is QPSK modulated or not.
Another simulation is performed to test the performance of the proposed method which is influenced by the number of phase sample space. As a comparison, a simulation of the method based on fourth-order cumulants under the same conditions is also performed. Figure 10 indicates that, for the method based on fourth-order cumulants, when the number of the phase sample space is more than , QPSK signal can be recognized effectively, while when the number of the phase sample space is less than , it is difficult to distinguish between QPSK modulation and 8PSK modulation.
This is because the method based on fourth-order cumulants can work well only when the sample values of phase space appear with a similar probability, which cannot be guaranteed when the number of phase sample is insufficient. Figure 11 indicates that, for the proposed method, the QPSK signal can be distinguished easily form the 8PSK signal as long as the number of phase sample space is greater than So the proposed method can still work well when the number of the phase sample space is less than as long as it is larger than 30 , which cannot be done by the method based on fourth-order cumulants.
This is because, in the proposed method, the quartic spectrum peak proportion is calculated by energy integration, which does not need a large number of phase samples. Simulations are also performed to test the correct recognition rate of the proposed method and the traditional method based on feature parameters.
The result is shown in Figure We presented a modulation recognition method of QPSK signal which is based on the combination of feature parameters and the quartic spectrum analysis. Firstly, three vital feature parameters of signal, which are the maximum value of the spectral power density of the normalized-centered instantaneous amplitude , the standard deviation of the absolute value of the centered nonlinear component of the instantaneous phase, evaluated over the nonweak intervals , and the average value of the instantaneous amplitude , are extracted.
Secondly, the extracted parameters are compared with the thresholds. Lastly, by analyzing the quartic spectrum peak proportion, the recognition of QPSK modulation can be realized. The simulation results show that the proposed method can recognize QPSK modulation effectively in less phase space samples and it is much more accurate than the traditional method based on feature parameters. The authors declare that there is no conflict of interests regarding the publication of this paper.
This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Chih-Cheng Hung. Received 05 Nov Revised 21 Apr Accepted 19 May The generation of a constellation diagram requires an oscilloscope with XY display capability, an external sampling clock, and a persistence display.
The measurement requires access to the in-phase, I, and quadrature, Q, signal components as well as the symbol clock. X-Y cursors read both the X and Y voltage values and the resultant output waveform phase angle and magnitude radius. The angle is the phase of the output signal relative to the in-phase component.
The radius is the magnitude of the composite signal displayed as the distance from the center of the display to the cursor location. Note that the timing of the symbol clock may need to be tweaked to assure that the signal waveforms are sampled at the correct time. Some scopes have the ability to use analog or color graded persistence to highlight the data states that occur most often. Finally, it is worth briefly discussing how to generate QPSK signals for testing.
An alternative means of generating QPSK signals is with a programmable arbitrary function generator and suitable software. For example, suppliers such as Mathworks have software packages that communicate with instruments through interfaces and drivers. They generally allow the creation of waveforms in terms of sine and cosine functions, phase shifts, amplitude settings, and so forth.
The resulting program to generate a specific waveform gets fed to a function generator through IO libraries — the Agilent IO Libraries Suite is one example. You must be logged in to post a comment. This site uses Akismet to reduce spam.
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