Science topics: Telecommunications EngineeringChannel Estimation

Science topic

# Channel Estimation - Science topic

Explore the latest questions and answers in Channel Estimation, and find Channel Estimation experts.

Questions related to Channel Estimation

In a signal is to be transmismitted, first pilot symbols are added and then through Mach-zehnder modulator it has to be transmitted for optical communication purposes. So, for pilot symbols, they have to be go through Mach-Zehnder modulator. The output of Mach-Zehnder modulator is E_out=cos(phi); Where phi= ((U+U_dc)/U_pi)*pi. and here, U_dc, U_pi and pi are constant. And U is the information after inserting pilots. This signal E_out is sent through the channel h.

Now, my question is that how should I estimate the channel because the pilot are gone through Mach-Zehnder modulator before the channel. and How should I extract the pilots and estimate the channel? Thank you.

According to my knowledge, the channel estimation is to analyze the channel realization based on the pilot signal, and the channel prediction is to obtain it based on past channel realizations.

Therefore, the channel prediction can be utilized when the pilot signal is contaminated. In other words, the channel prediction is only deserved in the situation when the pilot signals are crashed so that the channel estimation doesn’t work.

Did I understand right? Thank you for your valuable responses in advance.

I have read in a paper that a variable 'x' is the rotation angle of the user's antenna array with respect to moving direction. I am wondering how can we calculate this variable x?

Following information may not be useful but just in case it is necessary. Let us assume that BS has M ULA and user has N ULA.

It is known that for optimal RIS control, perfect CSI of all the links between BS and MS through the RIS is required. Therefore, channel estimation and corresponding message feedback methods will be needed at the BS/MS.

Few studies suggest a two-stage channel estimation approach for RIS-aided MIMO channels, using iterative re-weighted method for estimating channel parameters sequentially.

Will this be a good way to go about it, or there is/are better methods.

Let us say that we have a estimated channel at the BS, denoted by H_e, and actual channel, denoted by H_a. How can we calculate spectral efficiency of a SU-mMIMO system using this information?

More specifically, I have a BS equipped with 64 transmit antennas and a UE equipped with 1 receive antenna.

In OFDM, First we modulate our message signal and then we take the IFFT of that signal to load the message on subcarrirers. And after that the cyclic prefix is added to combat the frequency selective nature of channel. What should be the length of the cyclic prefix?

In the literature, I have seen path-loss models, e.g., air-to-ground (ATG). Such models obtain received power by using: Pr=Pt-PL. However, none of these models provide the method to obtain 'H'.

I am interested to obtain 'H' using UAV communication environment. For instance, 'H' must be the combination of line-of-sight (LoS) and non-LoS (NLoS) components.

Pilot signals are used for channel estimation in communication links. How much ratio of lengths can effectively represent channel estimate especially in OFDM case?

I am able to generate Rayleigh coefficients as per the following code (function) in python using H=(1/sqrt(2))*(randn(N)+randn(N)*1i)

def RAYLEIGH(d, etaa, num_symbols):

// Input arguments (Distance, pathloss exponent and samples required (depends on data if fast fading)//

c=1/(d**etaa);

h1 = np.sqrt(c); //(Pathloss is multiplied with Rayleigh coefficient)

h = h1*((np.random.randn(num_symbols)+1j*np.random.randn(num_symbols))/np.sqrt(2));

g = (np.absolute(h))**2; // Magnitude

return h.tolist(), g.tolist(); // Return as a list

How to generate the Rician Coefficients given d (distance), etaa (path loss exponent) on the same lines.

Hello all, I am working on MU-MIMO DL Block Diagonalization Precoding in Multi-Antenna receivers case and comparing it to zero forcing precoding.

I have following questions, could you please help in understanding.

1) In case of MU-MIMO Downlink, what is the actual cause of Multi-User interference or Inter-User Interference?

- Is it due to the fact that BS simultaneously transmitting signals to the users!

or

- due to Channel distortion!

I understand that in case of same channel, MUI can occur, but I also read that each transmit antennas at BS as a particular channel towards each receive antennas at the user side. Therefore in this case, how is MUI occuring? Is my understanding of channels between transmit and receive antennas correct?

2) In case of MU-MIMO downlink Zero Forcing Precoding, literature says its disadvantages are

i) inversion of ill-conditioned channel matrix causes noise amplification

How is noise amplification caused in precoding?

(However, In case of ZF Receiving, I understood Noise amplification problem, as h ->0, n/h -> Infinity )

ii) extra power might be required to transmit separate signals to closely spaced antennas of a single user, if channels of these antennas are highly correlated!

So, Block Diagonalization is preferred in multi- antenna recievers, as it only removes MUI and it does not suppress Inter-Antenna Interference as in case of ZF Precoding.

Could you please help in understanding disadvantages of ZF Precoding in MU-MIMO DL multi-antenna recievers case.

Thank You.

Geometry Based Stochastic Channel Modelling or Spatial Channel Modeling (SCM) methods such as:

- 3GPP-SCM, SCM-E

- Winner (I and II)

- QuaDRiGa

produce channel coefficients as a matrix H (may on path or at time) between transmitter and receiver antennas.

Then H associated with CSI matrix. But CSI of real channel assume several components (CQI - SNR, PMI, RI). Also, it is referred sometimes as a matrix of complex numbers (gain and phase)...

How generated channel coefficients are related with CSI?

How Channel State Information (CSI) could be obtained from matrix generated by Spatial Channel Modeling?

Where can I read about it?

I have a complex channel matrix 'H' and I want to quantize it in such a way so that the quantization error is minimum. In particular, how to select the dynamic range (the maximum and minimum interval) of the quantization level. Also, what is the best way of finding the appropriate values of each quantized point?

A MATLAB code-based supported answer would be icing on the cake. :)

-------

Below is what I am doing, at the moment, but it is not the best way.

**Example:**

*H =*

*[-0.9767 + 1.0234i, -1.0477 - 0.4223i;*

*1.0364 + 0.0454i , 0.0095 - 0.4758i;*

*0.2724 - 0.4980i , -0.4430 - 0.7466i;*

*-0.7302 + 0.7945i , -0.2508 + 0.1906i]; %Matrix H having 4 rows and 2 columns*

*H = H.';*

*H = reshape(H,1,[]);*

*partition = [0:1]; % Quantization levels*

*codebook = [0:2]; %Values for each quantized point.*

*[index,Q_H_real] = quantiz(*

**real(H)**,partition,codebook); % Quantized real of H.*[index,Q_H_Im] = quantiz(*

**imag(H)**,partition,codebook); % Quantized img. of H.

*Q_H=Q_H_real+i*Q_H_Im;*

*%Fig. 1. Plot real part of H and its quantized version**stem(real(H),'b');*

*hold on*

*stem(Q_H_real,'r')*

*legend('Original (Real Part) H','Quantized (Real Part) H')*

*title('Quantization of Real Part of H')*

*%Fig. 2. Plot Imaginary part of H and its quantized version*figure;

*stem(imag(H),'b');*

*hold on*

*stem(Q_H_Im,'r')*

*legend('Original (Img Part) H','Quantized (Img Part) H')*

*title('Quantization of Imaginary Part of H')*

Attached is the outcome of the above code. There is a huge quantization error.

I am new to ML/DL domain and my area of research is channel estimation in Massive MIMO. I would like to know a few things:

1) Can we use ML/DL algorithms to get optimal channel estimates in Massive MIMO?

2) Are they computationally efficient so that we could implement them in the real world?

3) What is the best way for me to start in this area?

please advise...

Thanks

I have a channel matrix, H, which contains the complex entries. I want to obtain the quantization of this matrix. For instance,

H=[1+2i,2-3i;5+3i,9-8i]; Q(H)?

Where Q is a quantization function. I found something on google but it is only to deal with a vector having real numbers. The code is given below.

t = [0:.1:2*pi]; % Times at which to sample the sine function

sig = sin(t); % Original signal, a sine wave

partition = [-1:.5:1]; % 2 bit quantizer

codebook = [-1.5:.5:1]; % 2 bit quantizer

[index,quants] = quantiz(sig,partition,codebook); % Quantize.

plot(t,sig,'x',t,quants,'.')

legend('Original signal','Quantized signal');

I need to implement the closed form solution in order to compute DFE Filters coefficient in matlab/Octave. Attached you can find the theoretical part that I've found in Proakis...

But I am having a hard time computing the Feedforward filters.

Any help will be really appreciated

I am doing a simple channel estimation using Least squares algorithm in a SISO system using QAM modulation.

I am new to this and I do not know what kind of results I have to plot.

I wrote a Matlab code which estimates channel using Least squares algorithm and plots the average mean square error between original channel and estimated coefficients Versus the SNR.

I am attaching the Matlab code and the plot for your reference. Kindly let me know what kind of results that I have to plot.

Ready to give more details if you want.

What are the different methods or techniques with which we could evaluate the performance of channel estimation algorithms in wireless communications systems, especially in Massive MIMO OFDM systems?

Hello, i'm having a project where I must implement ofdm simulation with mmse estimator for the rayleigh channel. Although the estimation seems tolerant, i'm getting no improve with ber, even for simulation of 10000 symbols.

I have attached the paper i'm trying to implement, with the matlab code and some representative figures to see exactly what i'm doing

I can't understand if i'm missing something very important when estimating the channel or when using specific pilot symbols or in somewhere else..

Thanks in advance,

Anastasia

In

**mmWaves electromagnetic channels (≥30GHz),****the channel models might be known, but they are too complex and/or change too fast to estimate with reasonable accuracy**?why the channel models changes too fast ?

In wireless Communication, what is the deference between the two terms;

1- Channel Estimation

2- Detection/ Decision making

QuaDRiGa (QUAsi Deterministic Radio channel GenerAtor). Is it possible to apply QuaDRiGa channel models for simulating massive MIMO.

In [1] the authors consider linearly independent channel covariance matrices, however, they do not mention if it is symmetric or not.

Therefore, I'd like to know if the covariance matrix for that case is symmetric.

[1] Emil Björnson, Jakob Hoydis, and Luca Sanguinetti,"Pilot contamination is not a fundamental asymptotic limitation in massive MIMO", IEEE International Conference on Communications (ICC), May 2017.

Thanks and Kind Regards,

Felipe

In TDD, by having K users and length of pilots=K, we would have orthogonal pilots which results in no pilot contamination. By reducing the length of pilots, we can transmit more symbols and of course we would have pilot contamination. So is there any trade off between the length of pilots and number of data symbols? Given (length of pilots+number of data symbols=total number of symbols in each coherence time). Thanks!

i have collect emg signal from 2 extensor and 2 flexor muscle for 10 motion. Is that each muscle represented one axis and input for classifier? I means i have 10 motion x 4 features ( one muscle for 1 features) for input of classifier, is that correct?

1、 signal power

we compare different method BER-SNR SER-SNR. Send signal must pass a channel

the signal power is the signal power before channel or after channel?.I guess before

someone try to normalize channel .like fir channel we set some taps then h/norm(h)

other type channel the same way. However this cannot ensure the power is same before and

after channel. Another way channel is usually fading channel so the normalize is redundant or not?

cp occupy part of energy we can ignore it?

Does we consider the processing as a linear system input output? Like channel cp is the middle processing. We do not need to consider as signal power or not?

2、 in ofdm normalize the power for example qpsk 16qam the coefficient are sqrt(2)

sqrt(10) the average power of all subcarriers symbols is 1.this is due to the distribution of the qam signal like 16qam 1+3j 1-j 1+j… the average is sqrt(1+9) does it ?

3、 complex noise is not exist in math we say the amplitude is abs(s),the phase is angel(s)

someone try to assign the ofdm signal to be conjugate so the symbol after ifft is real type then

noise type is real. However others does not then noise power divide into equal two parts this may have effect on the figure in SNR-BER?

4、 sparse channel in sparse channel estimation someone know the of the channel does this .of course we must sure Ncp>Nh we can assume the length of Nh is Ncp or much longer is ture?

5、 The practical system there are many procedure like synchronization interweave etc. in simulation we may ignore it? Practical system we use a few symbols to do channel estimation for extreme conditions we use all subcarriers to do estimation ,of course we assume the channel is time invariant or slow variant. Traditional typical pilot type should be useless

Thanks

A signal is transmitted by the transmitter and multiple rays arrive at the receiver as a result of reflections (from the flat obstacles), diffraction (from the edges of obstacles) and scattering.

How do I find the number of clusters in this condition?

For the Relaigh fading model, I want use four multipath components, each component has its delay and gain. Can the gain in dB of any of the individual multipath components be positive value? I can understand that it can be zero or maybe negative indicating reduction in its amplitude due to delay and effects of channel, but I can not understand how it can be positive. The overall signal resulting from the four components at the receiver may be with positive gain. This is reasonable, due to constructive addition of maybe inphase components, but how can a single component be with positive gain?

I'm studying on "identification of channel coding". the received data encoded by one of the following channel coding, 'convolutional' , 'LDPC' , 'Reed-Solomon' and 'BCH'. i should extract a feature among the received data to identify the kind of channel coding blindly and decide that which one of the above channel coding is used for channel coding. I would like to help me to find a feature for blind identification of mentioned channel coding. Your help will be much appreciated. Looking forward to hearing back from you.

I'm trying to implement the EVD-based channel estimation algorithm in massive MIMO using Matlab. The channel matrix G models fast fading H, large-scale fading D (shadow fading, path loss) by the formular: G = H*sqrt(D). But most of the papers on this method assume that we know D, and choose D = diag(0.98 0.63 0.47) or D = diag(0.98 0.85 0.75) for 3 UE as the papers below.

My first question is how can I determine these values of large-scle fading matrix?

And, if 3 UEs have similar positions, the coefficients can be similar or the same. The EVD-based algorithm cannot resolve the multiplicative factor ambiguity because of the invertible matrix. How can I resolve this problem?

References: H.Q. Ngo and E. Larsson, "EVD-based channel estimation in multicell multiuser MIMO systems with very large antenna arrays", Proc. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 3249-3252, 2012.

Umut Ugurlu et. al, Coordinated optimization of EVD-based channel estimators in multi-cell massive MIMO networks, Communication Workshop (ICCW), 2015.

when we use pilot channel estimation in matlab，the following questions：

suppose Ncp,Nch,Npi,Nall represent the length of CP,Channnel impulse response(CIR),pilot length, all subcarriers length,respectively.

1.the order of P/S and add CP

some one say before S/P，we add cp，while others are opposite.

we must ensure Ncp>=Nch

2、the order of add pilot ，most paper say that this step is after S/P,，that

means we must add pilot in parallel data，in matlab simulation we user

set A index of pilot ，but if we do this in parallel data we must convert the index to fit the parallel data.

3、the relationship of Npilot ，Nall，in matlab firstly we produce random bit data they a few steps，we have a index of pilot they we replace orign index

modulate data by the pilot data。so in fact the Npilot data is included in all data ，may this call replace ，when use insert that means add extra data，the length is increase ，does it？

4、how to estimation ，most paper select the pilot data，Xp，Yp，these are serial data。I guess we may estimate in serial data，some paper is in parallel data？

there are different type pilot ，if we use interpret function in matlab we

must sure the beginning and the ending data must be pilot data，only in this way can every data be equalization ，does it？

if estimation in parallel data comb-type or block-type may be invalid?

5、cp ，when we do experiment we find is the cp data is zero ,it cannot affect data if not,some data can not use.

6\pliot Does pilot must be bigger than modulate data or less or donnot mind？

7、SNR the signal power is must be the signal after channel，not before channel.

when calculate ser or ber does pilot data should be remove from data?

8\ MMSE comparsion we must cal h_est,but Npilot is nor equal to Nch,

so if u want compare different ways ,does we need to interpert the different

way like h_ls,h_mmse,h_ch to the length of the receive signal?

i try find all ways i can access but no ebook or paper refers the bellowing

questions

may i need total exact of 802.11 or DVB ofdm matlab code?

what operation is performed after channel estimation by using the estimated channel information at receiver?

Hello, Which neural networks topology is better for OFDM channel estimation? and how to implement it?

I am using some resource allocation schemes to find out the capacity. I need to find out the outage capacity. According to p(outage)=1-p(C<Cthreshold). How do I select Cthreshold and find p?

I need to know the origin/root cause of the CEE.

e.g. We know that one of the causes is imperfect channel estimation. But I want to know what are the factors that lead to this imperfect channel estimation.

The warning message displayed on Matlab When I choose Least square estimation LS = S' * inv( S * S' ) to achieve the channel estimation, Matrix is singular to working precision. ". Then the estimation is incorrect. If I increase the training length, the number of this cases appearance declines.

And where I can find a good references about channel estimation in WiMAX or generally?

Hi all,

I am designing a loop filter of DPLL in 802.11ac OFDM system. The DPLL tracking is achieved by pilot symbols inserted into OFDM symbols.

Importantly, the coefficients of loop filter significantly impact the PER ( packet error rate ) of the OFDM system.

So, does anyone know that how to design or determine the coefficients of the loop filter is able to maximize the performance of the OFDM system.

Many thanks

Jiajun Zhu

Suppose we have two sensors which are close to each other and the first has its own measured signal value, how this sensor can have an idea, margin or estimate of the signal measured by the second sensor in the proximity, knowing that these sensors does not know the original signal power emitted by the station or physical phenomenon. Obviously, I am not asking for the solution where the second can send the measured value to the first sensor.

Thanks in advance

can anyone suggest a way as how to approach in analysing a frequency selective Fading channel characteristics using matlab?

Currently, my friend is working with adaptive channel estimation for high mobility, Zero Forcing, MMSE, ML estimation.

Since the channel estimation for high mobility is complicated, we are moving to non linear estimation like, ML estimation. But this estimation is also complicated.

He needs better ideas to improve the estimation with less complexity.

When a channel is not perfectly estimated. (Imperfect CSI)

If channel gain RHO=modulus of h square ~ alpha*beta.

Hi all,

In linear MIMO ZF or MMSE, the LLR is calculated by detected signal power over both interference noise power and white noise power.

In nonlinear MIMO ZF-SIC and MMSE-SIC, the interference noise is cancelled by decision feedback. However, the cancellation may be incorrect, if the decision in the previous layer is error. Hence, the LLR, I think that, is calculated by detected signal power over both white noise power and error decision feedback variance.

However, I dont know how to calculate this LLR.

Does anyone know the solution or provide some references to me?

I am working on channel estimation in Massive MIMO for millimeter wave, I know that coherent time has reduced due to higher frequency, but still in doubt about number of symbols can send.

When we use subspace based blind channel estimation technique for MIMO-OFDM channel estimation, we divide the singular vectors of the received signal into noise subspace and signal subspace, and declare the matrix which minimizes the dot product of itself with noise subspace as the estimated channel, similar to all other blind techniques this procedure also does not give the true channel but gives upto a matrix multiple of the true channel, what are the techniques available to remove such an ambiguity? ,I do not wish to use any pilot sequences

Since there are very large number of antennas at the transmitter and receiver, we may need to estimate large number of channels, correspondingly we may need very large length pilot sequence to ensure orthogonality among pilot signals used for different transmission paths.

In OFDM transmission, we usually use a Frequency Domain Equalization by IFFT/FFT for channel estimation and equalization.

But some authors could use Time Domain Equalization also. Could you please provide some details to help me to understand how they proceed to do that? Thank you :-)

Hello, I am looking for the recent works, thesis and publications that deal with channel estimation (wireless system) based on neural network. Thank you

Land Mobile Satellite Channel Modeling

Right now what i am doing is, i am extracting my pilots before AWGN and estimating the channel at receiver. But i wonder if there is some other way to obtain perfect CSI? Any hints and suggestions would be a great help. Thanks

Please point out some papers which are relatively easy for starters particularly focusing on the problem formulation and mathematical background

I need a theoretical analysis of channel estimation in mimo-ofdm system.

The power line channel is an FIR filter with the coefficients being the weighting factors. The OSTBC combiner block has a channel estimation port.

When using channel measurements on simulation, it is common to have impulse responses with a ringing effect due to truncation in the frequency domain (due to the measurement itself). This ringing in the impulse response, however, induces bit detection errors that don't normally occur with regular impulse responses (given the bit loading algorithm guarantees a low BER). My question is: where in the DMT chain such an impulse response would be problematic, as far as simulation is concerned?

**EDIT**: I previously called these impulse responses with ringing as non-causal. However, non-causal in this context is not strictly non-causal by definition (an impulse response in which a given sample depends on future samples), but an impulse response in which the amplitude goes first negative before reaching the positive peak, showing energy before being excited. This is a terminology that commonly appears for channel models, in which the RCLG parameters can be causal or non-causal (depending on the model). To avoid confusion, I decided to edit and remove this term. The point is, the impulse response I`m using has this ringing effect, an oscillation before the peak. I believe the fact that this can cause detection errors is a known issue and that's why I`m asking it here. My goal is to understand why I can't use such an impulse response for time-domain simulations, or, in case I can, what type of pre-processing (e.g. freq-domain windowing) should I apply.

Bit loading seems to be correct, ISI/ICI seems to be controlled, transmission PSDs are apparently correct, detection implementation is correct and assumes perfect channel knowledge. I don't immediately see where it is problematic. I also cannot explain why the bit errors occur at the lower frequencies.

**EDIT2:**Problem solved. The impulse responses had small but non-negligible ringing in its last samples, close the FFT size. Hence, when I was truncating the impulse responses to 99% of the energy, they were continuing with a length close to the FFT size. This was impractical for the cyclic prefix to cover enough dispersion such that ISI could be controlled. Hence, what was indeed constraining me was ISI/ICI, in contrast to what I said before. Ultimately, I took the ISI/ICI PSD into account in the bit loading computation and solved the problem. I won't delete the question because it can be helpful for someone.

Best regards to everyone who tried to help.

Hello everybody, I want to realize a simulation on Matlab with cosimulation on VPI to make a plastic optical channel estimation that allows me to identify and compensate distortions. Does anyone know about a model for plastic optica fiber (POF)?.

I have read several papers and they show the way but there are a lot of doubts about the generation of the preamble in the TS based on Chu or complemetary Golay sequences. How can I perform this simulation acording with this kind of binary sequences or if anybody can make a citation of a reference where I can understand better the procedurement. I appreciate any support!

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?

As for training sequence, we can use the known simples to estimate the channel and then cancel ISI. However, in reality, there are not only frequency-selective channel but also carrier frequency offset.

In that case, channel estimation does not work in my simulation when I introduce carrier frequency offset. Probably, I need to cancel the carrier frequency offset firstly for promising reliable channel estimation.

However, the phase has been distored by frequency selective channel. Hence, the normal methods to estimate frequency offset does not work.

How can I cancel the carrier frequency offset before channel estimation ?

Otherwise, how can I combine carrier frequency offset estimation and channel estimation at the same time?

Many thanks

DS-CDMA is divided into uplink and downlink communications.

As for uplink, the users suffer from different channel impulse responses (CIRs). How the receiver estimates these CIRs and cancel it?

As for downlink, does the receiver only know own signature waveform? If say yes, how does it uses multiuser detection to cancel CIR and multiuser interference (MUI)?

LMS and RLS are popular methods for channel estimation. However, in the DS-CDMA system, there are not only CIR but also MUI. How can we let LMS and RLS adapt to this situation?

Many thanks.

I wanna to establish a inverse modelling to get a equalizer to combat the multipath channel. However, I found a problem that the training of LMS algorithm is very unstable under the Rayleigh multipath channel generated randomly.

So far, I found that in some coefficients of Rayleigh multipath the training seem to be stable, but in some coefficients of Rayleigh multipath the training is bad.

I think that the step size of LMS algorithm is a critical point, but I can't find some references to give an accurate value of the step size. Normally, they only give a range of the value which I have followed.

Does anyone know the problem?

Does My model perhaps is incorrect?

Many thanks

Jiajun

I want to design a Decision Feedback Equalizer (DFE) to combat the multipath channel.

Does anyone know some references about the principles of Decision Feedback

Equalizer (DFE) ?

Further, What is the advantages of DFE compared with normal equalizer?

Many thanks

Hi,

I have the delay profile and losses for indoor channel model A. How can I make time varying using Monte Carlo method?

Thanks.

Complex equalizers are needed in GSM while LTE resort to simpler equalization techniques due to its narrow band sub-channels. How to explain this difference in a simple way?

Can wavelet transforms be applied to adaptive filter applications like linear prediction, echo cancellation, equalization, channel estimation etc?

Done via a single transmit antenna (like in spatial modulation) with minimum number of rx antennas.

BER calculation every time I use the Rayleigh multipath-fading simulator in Simulink (v2012a MATLAB) it induces a BER of about 50% in my communications model. I have tried changing all of the parameters in the simulator, but nothing seems to help. Even if I reduce the numbers of paths to one, and increase the gain dramatically it still produces the same result. Does anyone have experience with this simulator?

When I am thinking about communication channels, like wireless ones, how is the statistical overview and modeling of the channel obtained? Mainly we are dealing with all random manners here? How researchers could expect the received signal and give it a mathematical model? I have a problem about understanding the gap between research and practical.

There are some ways to calibrate the path-loss exponent in wireless sensor networks, using anchor nodes. But they work only in localization.