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Although the FRFT has a number of unique properties, it cannot obtain information about local properties of the signal. In addition, the drawback of the short-time FRFT is that its time- and fractional-domain resolutions can not simultaneously be arbitrarily high. As a generalization of the WT, the FRWT combines the advantages of the WT and the FRFT, i.e., it is a linear transformation without cross-term interference and is capable of providing multiresolution analysis and representing signals in the fractional domain. Thus, the FRWT may be potentially useful in the signal processing community and will attract more and more attention.
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Dear researcher
I see in this book an important relationship to your question, I hope you read it.
Best regards
Digital Signal Processing Using MATLAB & Wavelets
by Michael Weeks
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  1. In the performance analysis of modern wireless communication systems like the scenario of factory automation with 5G URLLC, which path loss model will be suitable and appropriate to assume?
  2. Any comments on the recent empirical findings regarding the same?
Thanks in advance.
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Use log normal _shadowing path loss model in microcell region if factory area is confined within 500 meters of radius. You will have to consider the several case...
1_ within building
2_From building to open space.
3_within open space
3_open space with scatters etc
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I am using maximum ratio combing and selection combing techniques for C-NOMA. I want to compare the results for both techniques and also suggest me any other alternative combing techniques which give better results for C-NOMA.
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I would recommend to look at (cooperative) rate-splitting instead. This works much better than (cooperative) NOMA. See for instance
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I am trying to analyze the SEP for half duplex relay for UWB channel with PPM TH modulation. I am sure about the analytical part but my simulation results are not matching. I am confused about the decision variable I am using. I am referring the book 'Understanding ultra wideband radio fundamentals by Guerino Giancola'. On receiver side I am using correlation receiver with MRC(maximal ratio combining). I am unable to get any reference paper for this topic.
Thank you in advance
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Let me check on that.
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I am trying to analyze SEP for full duplex amplify and forward relay, for MPSK. I am done with the simulation part, but analytical part is not matching with the simulated values. Also the curve I am getting is not smooth in nature. I got the end to end SNR equation for same system model from a research paper. Using that expression in the Craig's formula for SEP, I am calculating the expectation of the integral, which should give me the ASEP. The relay stability factors puts a constraint on the gain.
I am stuck with the analytical part. Any help is appreciated.
Thank you
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Dear Mr. Shrotey.
This paper can help you: https://arxiv.org/pdf/1303.1795.pdf to improve your knowledge and synchronize between the results of simulation and analytic because in this paper be able formulas dan step-by-step to solve the problems.
Regards,
Syahrial Shaddiq, S.T., MIET., M.M.
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chapter 11(antennas) of this book: Field and Wave Electromagnetics: Pearson New International Edition - David K. Cheng. ?
So far I only found online versions limited to chapter 10
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Buy the book!
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please consider a frequency selective channel with 3 real coefficients {a1 a2 a3 for the three different frequencies} for example. should we apply average of the multi access  interference MAI as:   p(user2)*sum(E{a^2}) for the SINR calculation in a 2-user system under this channel? the expectation must be on the users or coefficients? thanks
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In LTE TDD special subframes are used for switching from downlink to uplink and contain three sections: DwPTS, GP, and UpPTS. But we don't have any special subframe for switching from uplink to downlink. Why?
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Hello Sir,
Yes you are correct, but that is not my question. Please check my attachment with the question.  In LTE TDD we switch from downlink to uplink and uplink to downlink. there is always a special subframe in between two symbols while switching from uplink to downlink but there is no subframe or guard period while switching from downlink yo uplink. why?
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Hello All,
I am working on a matlab simulator for SCFDMA PUSCH Uplink considering all the blocks and 3gpp standards. For the simulation purpose we always multiply transmitted signal with a factor because of size difference in DFT and IFFT (zero padding). 
For an example:
Let's assume we have the following situation for 1 OFDM subframe (all the parameters are according to 3gpp standard)
>DFT size = 816, (allocated resource blocks = 68)
>Modulation order = 2 (4 QAM),
>Total symbols after DFT and before resource mapping = 816*12 = 9792,
>Total resource block = 100, (zero padding while resource mapping)
>Total symbols after resource mapping = 1200*14 (816*2 dmrs and zero padding) 
>IFFT size = 2048, (zero padding before IFFT)
>Cyclic prefix (normal) length = 160*2 (for first symbols) + 144*12 (for others) = 2048
>Total symbols transmitted = 2048*14 (after IFFT) + 2048 (CP) = 30720
Now for the simulation purpose 
(i)
transmitted_signal = transmitted_signal*sqrt(ifftSize/(PRBAllocated*12))*sqrt(length(transmitted_signal)/(ifftSize*14))
(ii)
EsNo  = EbNo + 10*log10(DFT_length/IFFT_length) + 10*log10(modOrder);
Are both the statements valid? How?
Do I need to consider CP and DMRS effect in second expression? 
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According to me we always subtract the modulation factor. Let suppose modulation order is 2 (4 QAM), that means we are mapping 2 bits to 1 complex symbol. Energy of 2 bits is given to 1 complex symbol.
(It means if there is error in 2 bits then only one symbol will be wrong)
So in linear scale 
Es/No = (Eb/No)*(1/2) ;
and in dB 
Es/No = Eb/No + 10*log10(1/2) = Eb/No - 10*log10(2) 
Here is the explanation from Matlab: http://in.mathworks.com/help/comm/ug/awgn-channel.html
Please correct me if I'm wrong. 
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As for linear ZF or MMSE detector, the coefficient of soft output is that symbol power over symbol interference power plus white noise power. 
As for non-linear ZF-SIC or MMSE-SIC detector, the coefficient of soft output is that symbol power over symbol interference power plus not only white noise power but also error propagation power of decision feedback.
Does anyone know how to calculate this factor of error propagation power of decision feedback, and get a correct LLR value.
Many thanks
Jiajun Zhu
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Calculating LLRs requires generating a set of vector symbol candidates. Once you have a set of candidates, the LLRs can be computed the same way as a sphere decoder does it. How to generate such set? As an starting point, I'd suggest you to try the following approach:
1. Calculated the the ZF/MMSE equalized vector symbol.
2. Before performing hard decision, find the K closest constellation vectors to  your   ZF/MMSE equalized vector symbol.
3. Then you will have K+1 candidates from which you can compute the LLRs. (K obviously depends on the size of the constellation set).
Hope this helps.
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I am studying how to establish a 802.11ac multipath channel for my WiFi simulation.
As we know, the impact of channel is equivalent to that the transmit signal go through a "channel filter". Therefore, the specification provides the coefficients of channel taps to us so that we can build the "channel filter".
However, It is based on that the sampling rate of baseband signal is equal to that of the "channel filter".
The problem I found that my baseband signal has the sampling rate of 80MHz, but the channel tap spacing is 5nsec (100MHz). There are different sampling rates!
Then, how can I do for the mismatch of sampling rates, and get a correct simulation?
Kind Regards
Jiajun Zhu
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Rate matching is accomplished by resampling the baseband signal by an interpolation of  5 divided by four, that is for every four  samples are substituted by five samples..You can assume that the fifth sample i an average value of the other four  and inserted in the middle of then, that i two samples before and two samples after.
Wish you success
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I would like to generate CSCG in MATLAB with zero mean and certain variance. I would be pleased to know all the possible ways of generating CSCG noise.
Thanks
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I agree with answers above, but if "var" should be the true variance then you write it as
sqrt(var/2)*(randn(1,N)+1i*randn(1,N))
so that the real and imaginary parts each contribute to one half of the variance.
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If we were able to estimate the noise power blindly for a conventional energy detector (CED), does that shift the CED from semi-blind to fully blind detector?
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As an ED needs information about the noise level it can not be considered as fully blind.
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if we have 2 devices like walky talky and mobile phone, for a particular situation, is it possible the mobile to use the channel allocated to walky talky to send data/information ?
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In principle, why not?
Walkie talkies typically operate either in the citizens band, high HF, or in the VHF spectrum. Whereas mobile phones operate at high UHF, above 700 MHz to the GHz ranges. So that's one problem. The antenna requirements, for small hand held devices with no external antenna, would make use walkie talkie frequencies quite inefficient.
But mobile phones are already multiband devices, after all, so why not (in principle)? Mobile phones already detect frequencies available at any given location, and pick the best one to use. So I don't think there's anything intrinsically impossible about designing a "mobile phone" to use other frequency bands than those allocated to cellular. However, the spectrum allocation authority in your country would most likely not allow this.
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Land Mobile Satellite Channel Modeling
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"THREE STATE FONTAN LMS CHANNEL MODEL" does not include:
  1. Polarization
  2. MIMO
  3. Deterministic evolution of the channel
  4. Possibility to include 3D Polarimetric antennas at RX and TX
  5. Spatially correlated propagation parameters
  6. Possibility to reproduce / resimulate measurement tracks
  7. Deterministic transition between segments with different propagation properties (like NLOS to LOS)
  8. A reference implementation is available.
Cheers,
Kai
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In general adaptive time-varying filters are used for acoustic echo cancellation (AEC) and active noise suppression. In non-linear AEC we assume there is low or no noise when cancel the echo with adaptive filters. My concern is to find algorithms or adaptive filter structures that can manage both the issues simultaneously without any performance degradation.
Kindly help me in getting some new idea in this direction.
Regards,
A.Kar
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To get some ideas you might want to look at work done on deghosting on marine seismic data. Loot at seg.org or eage.org. There are several approaches that are described. The most common is to apply an inverse filter. However, you may also find a finite difference algorithm that might be suitable. Even though the theory is simple, a practical implementation in 2 or 3-D might be rather tricky.
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Can I use ftp to send data to remote locations using GSM links.
Since 2G/3G links are not always good, what kind of file transfer protocols can best suite the task of sending/receiving data using the 2G and 3G links?
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FTP is ok to transfer files on whatever IP network. If you need security, use SFTP or SCP.
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The reference I've read indicates that the minimum training frames should be greater or equal to the number of transmitter antennas. Herein, the training frames denote that S = [s1, s2, s3, ... sN], where si is a nTx*1 vector and N is total number training frames. 
When I choose Least square estimation LS = S' * inv( S * S' ) to achieve the channel estimation, I find that sometimes Matlab displays "Warning: Matrix is singular to working precision. ". Then the estimation is incorrect. If I increase the training length, the number of this cases appearance declines.
So, what is the problem in this case? How can I determine the training length to avoid the problem in reality?
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On the basis of required accuracy of the channel estimation, optimal training sequences of minimum length are determined and it is given by
NP ≥ NT (L + 1) + L
Where, Where, Np = the number of training symbols per transmit antenna and per frame.
The training sequences very much effect the channel capacity, and by implementing the Optimal training sequence length improved MSE and capacity both.
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Having been around in one form or another for almost thirty years, software defined radio (SDR) is still in its infancy, relegated to the research lab or the hobbyist study. Every year, more and more publications declare that some recent advances in computing power, or microprocessor architecture/augmentation, have paved the way for future systems to be completely software defined. Yet, mass market products are, invariably, hardware-defined.
What is the hold-up? Is it really limited computing power or lack of resources? Is it simply not a profitable venture?
Should we accept that, despite our best intentions, the world simply does not want, or need, SDR? Is it time to accept that SDR is just a research convenience, an interesting side-project, and re-focus our efforts on pushing the boundaries of hardware-defined receivers?
Or perhaps the momentum is still gathering, and there really is a future in SDR?
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I agree with Johannes that this "pure" view is not very practical. SDR was always envisioned as a slow evolution from completely unconfigurable systems (i.e., hardware-defined systems) to reconfigurable systems (defined by software which may be uploaded to effect different personalities). For instance, see Mitola 99 "Software Radio Architecture: A Mathematical Perspective", the block diagram in that work is far more complex than what you are calling an SDR. I would suggest that at today's processing capabilities and expectations of extremely (or even moderately) high data rates from consumers the ultimate goal that you describe is not in reach. But this thing you speak of is not the one-and-only definition of SDR.
To me, the advantage of an SDR is its reconfigurability and its flexibility. Why else would you want one? So, here's my question to you. If you have a truly reconfigurable radio (say something even better than the prototyping platforms on the market today) that can be reprogrammed (by a host computer or its own "intelligence") to modify its carrier frequency, data rate, modulation format, etc... Albeit based on some "custom" but reprogrammable hardware. What is the difference whether we call it massively reconfigurable hardware-defined radio (MRHDR?) or software-defined radio (SDR)? Aren't the end goals of both the same? What more does the platform you describe offer us above and beyond this reconfigurability? I would argue that the amount of software and programmable hardware that goes into platforms currently available merits the name software-defined radio as it is the software that gives the radio its definition (operating parameters, signal characteristics, etc). Most of the SDR research that interests me would not be classified as SDR by your definition. I think this definition is too narrow. After all, I would suggest that our personal computers are software-defined even though they may have custom hardware attached (e.g., high performance video cards).