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Wireless Communications - Science topic

This group has been created to bring together those interested in wireless communication research.
Questions related to Wireless Communications
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Wireless communication application
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There are many factors to consider when selecting the appropriate antenna type for a specific wireless communication application. Some of the most important factors include:
  • Frequency range: The antenna must be able to operate within the frequency range of the wireless communication system.
  • Gain: The antenna's gain determines how much power it can radiate or receive in a particular direction.
  • Polarization: The antenna's polarization must match the polarization of the wireless communication system.
  • Radiation pattern: The antenna's radiation pattern determines how the energy is distributed in space.
  • Size and shape: The antenna's size and shape must be compatible with the physical constraints of the application.
  • Environmental factors: The antenna must be able to withstand the environmental conditions of the application.
  • Cost: The cost of the antenna is an important factor to consider, especially for large-scale deployments.
In addition to these factors, the specific application may have other requirements that need to be considered. For example, a wireless communication system that is used for long-range communication may require an antenna with a high gain. A wireless communication system that is used for indoor communication may require an antenna with a omnidirectional radiation pattern.
By considering all of these factors, you can select the appropriate antenna type for your specific wireless communication application.
Here are some additional factors that may be important in some applications:
  • VSWR: The voltage standing wave ratio (VSWR) is a measure of the match between the antenna and the transmitter or receiver. A low VSWR is desirable, as it minimizes power loss.
  • Bandwidth: The bandwidth of the antenna determines the range of frequencies over which it can operate effectively. A wider bandwidth is desirable for applications that require frequency agility.
  • Durability: The antenna must be able to withstand the physical and environmental conditions of the application. For example, an antenna that is used outdoors must be able to withstand rain, snow, and wind.
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What is the Relation between Absolute Propagation Time (ns) with respect to the Received Power (dBm) at the UE end? I would appreciate if anyone can explain the two graphs and the parameters associated with these graphs. Thanks.
(Two files are attached for reference)
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From the graphs, in Run1, As the absolute propagation time increased to ~ 700ns, no signal was received. Also, the lower received power, (Pr) of -126.8dBm and higher path loss, (PL) of 156.8dB recorded is due to longer propagation path btw the Tx and Rx as compared to Run 2.
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Hi everyone,
I am trying to simulate an M-QAM MIMO system, and I want to decode the received signal using QR decomposition and sphere decoding.
However, I am struggling to understand how to implement the simple tree sphere decoding algorithm.
Here's my understanding so far:
Given a receive signal as follows:
1. Y = HX + N;
where X is a K x 1 vector consisting of K MQAM data symbols, H is a K x K fading matrix, N is a
K x 1 AWGN vector, and Y is the K x 1 received vector.
We perform QR detection as follows:
2. H = QR;
where Q is a K x K Unitary matrix, and R is a upper-triangular matrix with entry R(i, j),
with i and j being the row and column indices respectively.
Then we equalise the receive signal in 1. as:
3. Z = (Q^H)*Y = RX + (Q^H)*N , where Q^H is the Hermitian transpose of Q.
I now want to perform Sphere decoding based on 3. This is where I am struggling.
My best attempt at coding this algorithm in Matlab is shown below (Assuming K = 8, and assuming an arbitrary radius d):
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ## Inputs:
% # R, upper triangular matrix
% # Z,vector receive signal
% # d, radius of sphere
%
% ## Output
% # X_hat, vector of estimated symbols
n = 8; % n => K
d_vector = zeros(1, n);
d_vector(n) = d;
Upp_bound = zeros(1, n);
Low_bound = zeros(1, n);
minR = d_vector(n);
intersum = zeros(1, n);
intersum(n) = Z(n);
Upp_bound(n) = (d_vector(n) + Z(n))/R(n, n);
Low_bound(n) = (-d_vector(n) + Z(n))/R(n, n);
X_hat = zeros(1, n);
% ## begin algorithm
if Low_bound(n) > Upp_bound(n)
return null;
else
X_hat(n) = Low_bound(n);
end
k = n;
while X_hat(n) <= Upp_bound(n)
if X_hat(k) > Upp_bound(k)
k = k + 1;
X_hat(k) = X_hat(k) + 1;
else
if k > 1
k = k - 1;
d_vector(k) = sqrt(d_vector(k)^2 - (intersum(k + 1) - R(k + 1, k + 1)*X_hat(k + 1))^2);
intersum(k) = Z(k) - R(k, (k + 1):n)*X_hat((k + 1):n);
Upp_bound(k) = (d_vector(k) + intersum(k))/R(k, k);
Low_bound(k) = (-d_vector(k) + intersum(k))/R(k, k);
if Low_bound(k) > Upp_bound(k)
k = k + 1;
X_hat(k) = X_hat(k + 1);
else
X_hat(k) = Low_bound(k);
end
else
while X_hat(k) <= Upp_bound(k)
if minR > norm(Z - R*X_hat, 'fro')^2
X_est_vector = X_hat; %found X_hat
minR = norm(Z - R*X_hat, 'fro')^2;
end
X_hat(k) = X_hat(k) + 1;
end
k = k + 1;
X_hat(k) = X_hat(k) + 1;
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Please could you tell me where I might be going wrong.
Thank you.
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Here are the steps on how to perform sphere decoding on an MQAM MIMO system:
  1. Generate the transmitted signal. This can be done by generating complex symbols for each transmit antenna and mapping them to the corresponding MQAM constellation points.
  2. Add noise to the received signal. This can be done by generating a noise vector with the same dimensions as the received signal and adding it to the received signal.
  3. Perform QR decomposition on the received signal. This will reduce the received signal to a lower-dimensional space.
  4. Initialize the sphere radius. The sphere radius is a parameter that determines the size of the search space. A larger sphere radius will result in a higher probability of finding the correct solution, but it will also increase the computational complexity of the algorithm.
  5. Start the sphere decoding algorithm. The sphere decoding algorithm will start by finding the closest lattice point to the received signal within the sphere radius. This point will be the initial candidate solution.
  6. Expand the sphere radius. The sphere radius will be expanded until a point is found that is a valid MQAM symbol. This point will be the final solution of the sphere decoding algorithm.
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Hi everyone,
I am trying to derive the error performance of a wireless communications system, and I run into a series of Independent and Identically Distributed (i.i.d.) random variables (RVs) as follows:
Z = X_1 + X_2 + ... + X_N,
where N denotes the number of RVs being summed together. The distribution of each X_1, .., X_N may be Rayleigh, Rice, etc.
Now, I know that the Central Limit Theorem (CLT) can be applied assuming high N, such that the mean of Z becomes:
E[Z] = N*E[X_N],
and the variance of Z becomes:
Var[Z] = N*Var[X_N].
However, in my case, N is not high enough to use CLT. In fact, I am working with N values in the set N = {2, 3, 4, 5, 6, 7, 8, 9, 10}.
So my question is, if I am given the CDF (denoted by F_N(t)) and PDF (denoted by f_N(t)) of each X_1, ..., X_N, how do I evaluate the mean and variance of Z? Do you use some sort of convolution property, or something else?
Any help would be appreciated.
Thank you.
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The mean and variance of a series of random variables are two important measures that describe the central tendency and variability of the distribution of the random variables.
The mean of a series of random variables is also called the expected value and is denoted by E(X). It represents the average value of the random variables in the series. The formula for calculating the mean of a series of random variables X1, X2, ..., Xn is:
E(X) = (X1 + X2 + ... + Xn) / n
The variance of a series of random variables is denoted by Var(X) and represents the measure of the spread of the distribution of the random variables around their mean. The formula for calculating the variance of a series of random variables X1, X2, ..., Xn is:
Var(X) = E[(X - E(X))^2] = [(X1 - E(X))^2 + (X2 - E(X))^2 + ... + (Xn - E(X))^2] / n
The square root of the variance is called the standard deviation of the series of random variables and is denoted by σ. The standard deviation represents the typical distance that the random variables in the series deviate from their mean.
Both the mean and variance are important measures in probability theory and statistics, and they can provide valuable insights into the behavior of a series of random variables.
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What is difference between spin-transfer torque nano oscillators and spin-hall nano oscillators?
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Spin Transfer Torque Nano Oscillators (STNOs) and Spin Hall Nano Oscillators (SHNOs) are magnetic nano oscillators driven by spin currents. Still, they differ in how they generate and utilize this spin current.
  1. Spin Transfer Torque Nano Oscillators (STNOs): In STNOs, the spin current is directly injected into the magnetic layer from a spin-polarized current source. This causes a transfer of angular momentum to the magnetization, which can lead to a precessional motion of the magnetization if the current is strong enough. The frequency of this precession can be tuned by adjusting the current, allowing these devices to be used as microwave sources. STNOs can be realized in various structures, such as spin-valves or magnetic tunnel junctions.
  2. Spin Hall Nano Oscillators (SHNOs): SHNOs use the Spin Hall effect to generate a spin current. These devices pass a charge current through a heavy metal layer with a large spin-orbit coupling (such as platinum). The spin Hall effect causes the electrons to deviate from their original path and accumulate on the sides of the layer, creating a transverse spin current. This spin current can then be injected into an adjacent magnetic layer, leading to a precessional motion of the magnetization similar to that in STNOs. The advantage of SHNOs is that they do not require a spin-polarized current source, which makes them potentially more efficient and easier to integrate with conventional electronics.
In summary, the main difference between STNOs and SHNOs lies in the spin current generation and injection method. STNOs inject the spin current directly into the magnetic layer, while SHNOs use the Spin Hall effect to generate a transverse spin current. This can result in differences in efficiency, tunability, and integration possibilities.
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In optical wireless communication, there are two main system architectures:
• Intensity modulation and direct detection (IM/DD)
• Coherent modulation/detection
In IM/DD systems, the optical front-end can only detect the optical intensity while coherent receivers can detect both amplitude and phase.
Discuss how we can benefit from Integrated reflective surfaces in IM/DD and coherent optical wireless systems. For each system type, explain your underlying assumptions and any other essential information to support your arguments.
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In optical wireless communication, there are two main system architectures: Intensity Modulation and Direct Detection (IM/DD) and Coherent Modulation/Detection.
Intensity Modulation and Direct Detection (IM/DD): This architecture utilizes an intensity-modulated optical signal, which is transmitted through the channel. At the receiver end, a photodiode is used to detect the signal, and an electrical signal is recovered using a transimpedance amplifier. The recovered signal is then processed using digital signal processing (DSP) techniques to extract the transmitted information. This architecture is simple and cost-effective, but it has limited performance due to the presence of noise and other channel impairments.
Coherent Modulation/Detection: This architecture utilizes coherent modulation of the optical signal, which involves modulating both the amplitude and phase of the optical carrier. At the receiver end, a coherent detector is used to detect the signal, which involves mixing the received signal with a local oscillator signal. The electrical signal is then processed using DSP techniques to extract the transmitted information. This architecture provides better performance than IM/DD, but it requires complex hardware and is more expensive.
Both architectures have their own advantages and disadvantages, and their suitability depends on the specific application and performance requirements. IM/DD is suitable for short-range applications where cost and simplicity are important factors, while coherent modulation/detection is suitable for long-range applications where high performance is required but at a higher cost.
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Hi everyone!
I want to have expertise opinion abut these.
Will it be cost effective or not?
What about the signal strength (SNR) and system complexity?
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The short answer is no, a MIMO array is much more capable and the IRS will have to be very much larger to achieve similar performance: https://arxiv.org/pdf/2002.04960.pdf
This study only considers SNR, but MIMO arrays also have better multiplexing capabilities and can manage frequency-selective propagation environments.
IRS is rather a competitor to traditional relaying technology. You basically reduce the power consumption at the relay by making it a larger reflector. It is hard to compare the costs before there are any commercial products.
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Hi everyone,
I wanted to know how to perform Rician fading in matlab.
I have done Rayleigh fading, which is simply:
h = sqrt(1/2)*(randn(1, 1) + 1i*randn(1, 1));
From what I understand so far about Rician fading, it is computed in terms of a Rician factor K, and a scaling parameter Omega (I will assume Omega = 1). It is also computed in terms of Line-of-Sight components and Non-Line-of-Sight components.
So what i did was:
hLOS = sqrt(1/2)*(randn(1, 1) + 1i*randn(1, 1));
hNLOS = sqrt(1/2)*(randn(1, 1) + 1i*randn(1, 1));
h = sqrt(K/(K + 1))*hLOS + sqrt(1/(K + 1))*hNLOS;
I want to confirm if this is correct or not. If it is not, please could someone offer some guidance on how to correct this.
Thank you.
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I don’t think the code is correct since hLOS should only have a random phase, not a random amplitude.
You can write like this:
hLOS = exp(1i*2*pi*rand(1,1));
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How to filter input signal through lognormal shadowing model or kappa mu shadowing model by using a code which generates PDF in Matlab?
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To filter an input signal through a lognormal shadowing model or kappa mu shadowing model and generate a probability density function (PDF) in MATLAB, you can follow these steps:
  1. Generate the input signal that you want to filter.
  2. Define the parameters of the shadowing model that you want to use. For example, for the lognormal shadowing model, you will need to define the mean and variance of the underlying normal distribution. For the kappa mu shadowing model, you will need to define the shape parameter "kappa" and the scale parameter "mu".
  3. Use the built-in MATLAB function for the corresponding shadowing model to generate a shadowing factor sequence that has the same length as the input signal. For example, for the lognormal shadowing model, you can use the "random" function to generate a sequence of random variables from the underlying normal distribution, and then take the exponential of this sequence to get the corresponding shadowing factor sequence.
  4. Filter the input signal by multiplying it element-wise with the shadowing factor sequence.
  5. Generate the PDF of the filtered signal using the built-in MATLAB function "histogram".
Here's an example of how you might use this process for the lognormal shadowing model:
% Generate the input signal
fs = 1000; % sampling frequency
t = 0:1/fs:1-1/fs; % time vector
x = sin(2*pi*100*t) + sin(2*pi*200*t) + sin(2*pi*300*t);
% Define the parameters of the shadowing model
mu = 0; % mean of the underlying normal distribution
sigma = 1; % standard deviation of the underlying normal distribution
% Generate the shadowing factor sequence
shadowing = exp(mu + sigma*randn(size(x)));
% Filter the input signal
y = x .* shadowing;
% Generate the PDF of the filtered signal
nbins = 100;
histogram(y, nbins, 'Normalization', 'pdf');
xlabel('Signal Amplitude');
ylabel('Probability Density');
title('PDF of Filtered Signal');
==================
In this example, the input signal is a sum of three sine waves, and the lognormal shadowing model is used with a mean of 0 and a standard deviation of 1. The "shadowing" sequence is generated by taking the exponential of a sequence of random variables from the underlying normal distribution. The input signal is then filtered by element-wise multiplication with the "shadowing" sequence, and the PDF of the filtered signal is generated using the built-in "histogram" function.
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i have transfer functions from two different papers. are they same.
in H=exp(-alpha(f)*l)
here, do we have to put values of f or f is just an function related to alpha.?
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The attenuation coefficient, alpha is a function of frequency f.
alpha (f) means the attenuation coefficient in dB/m at frequency f.
Both the transfer functions are the same. The top function has the value alpha already substituted as 0.05 dB/km for 100 GHz.
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Machine learning, deep learning, wireless, 5G, 6G and communications
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I guess that most people who workin with deep learning are using Python. But I recommend you to make a search at https://paperswithcode.com
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For generating antenna pattern multiplexing when we apply dc biased sinusoidal voltage to the variable reactance of the Electronically Steerable Parasitic Array Radiator (ESPAR) antenna, the received signal appear as harmonic due to non linear properties resulting loosing signal components? which method can minimize the issue for making the antenna pattern more efficient?
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For creating the multiplexed antenna pattern when I am applying time varying dc biased voltage using antenna pattern multiplexing for creating multi-path antenna pattern which creating non linear signal at the receiver. Non linear characteristics is also required for this method but I have to minimize the it for getting getting better path diversity. For instance lots of researcher used support vector machine/ equivalent weight vector method for getting better results. My question is how I can get efficient results by using the mentioned methods ?
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In many books and papers, they used to use the complex form of the channel effect response. However, in reality, the signal is real values and also the channel.
Is it correct to use the following approximation? (in MATLAB)
% Rayleigh channel fading
eta = 4; %Path loss exponent
d = 200; %Distance from BS to the user
h_var = sqrt(d^-eta); % channel effect variance, mean is zero
h = h_var*randn(1,length(tx))/sqrt(2);
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Even you want to develop both the transmitter and receiver in your simulation, still you only need to do your simulation in baseband to include all effects of the channel. Actually, as I know, none one is doing such simulation in RF band, because all channel effects can represented in baseband simulation, while simulation in RF band is much expensive but provides none extra value.
Let's have a quick review of some concept. In general an RF signal (also referred to as bandpass signal) is:
P(t)=I(t)*cos(w*t)+Q(t)*sin(w*t),
where w is the angular frequency of the RF carrier, I(t) and Q(t) are the in-phase and quadrature-phase components of P(t) which is a real signal. For convenience, usually a complex signal S(t) = I(t)+j*Q(t) is defined and referred to as the baseband signal of P(t). Correspondingly, the bandpass (RF) signal can be expressed as:
P(t) = Real{S(t)*exp(j*w*t)},
where Real{x} stands for the real component of x.
With these notations, when the system (including the transmitter + channel + receiver) is studied, we only need to investigate the baseband signal S(t) and its changes (due to the channel).
In short, what you can do is as follows.
(1) Generate a sequence of baseband QAM symbols (denoted by S(t)).
(2) Generate a sequence of complex Rayleigh fading coefficients, such as
h(t) = a(t)+j*b(t), where a(t) and b(t) are independent Gaussian random processes with proper power spectral density depending on how fast the channel changes.
(3) The received baseband signal is r(t) = s(t)*h(t)+n(t), where n(t) is the optional noise. Your receiver then further works on this baseband signal r(t).
If you really want to see the waveform of the received RF signal, simple take the real component of {r(t)*exp(j*w*t)}.
Hope these helpful to you. Good luck.
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I just want to know the scope and difficulties for physical implementation at THz band. Is there any testbed to implement a simple measurement setup for THz communication system?
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They are several projects that already have been done exploring this topic. One of them, kindly, look into more detail about THOR Project (a joint EU-Japan project) on this issue.
for a good start before delving into this research, there is a webinar from ITU about this (Ultrabroadband communication and networking solutions to unleash the Terahertz Band) to grasp the current issue.
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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.
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If we see the series expansion of cos function then how can we find the linear interval? After first term every term is non-linear. I am attaching the series expansion of cos function. If I am missing something in this concept please let me know.
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Hi everyone,
I am trying to derive the variance of a Random Variable as part of my research.
I wanted to check if I have simplified the expression in the word document attached correctly. In other words, if I take the variance of a sum or difference of linear combinations (series) of random variables, is that equal to the sums of the variances of the random variables, where the different series differ by a constant "alpha" (see the document). Note that the random variable in each series is the same random variable.
I tried deducing this by using a few properties of variance:
1. Var(aX) = a^2 Var(X)
2. Var([series from 1 to n of] X) = n Var(X)
3. Var(aX + bY) = a^2Var(X) + b^2Var(Y), where X and Y are independent RVs.
However, I am not sure if this correct. Please could you check if my understanding of this is correct.
Thank you.
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If X_1,...,X_N are independent random variables, then your derivation is correct.
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Hi everyone,
I am having a problem deriving the variance of a variable which is expressed in terms of a series expression where the sum term is a random variable.
I have attached a word document which illustrates the types of expressions which need to be evaluated in my derivation of the random variable in question. I hope this is clear enough to explain my problem.
I want to know how to evaluate these types of expressions, where the random variable is part of the series term.
Thank You
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If you sum up independent random variables, then the variance of the sum = sum of the variances.
In general, you can expand your sum expression into all the individual terms (products of a variable with itself and with all the cross-terms) and compute the expected value of the individual terms.
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Hi, I am working on UAV deployment in a wireless communication scenario to cache. Does anybody know how to simulate/deploy UAVs in Matlab using weighted or simple k-mean clustering? Below I also attached a snapshot.
Thank you
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Hi Imad,
Here are three links on the subject that will interest you, I think the first link provides a thesis, despite its date 2010 remains current.
Communication among UAVs Thesis in Computer Engineering Jun 2010
Chaoyou Dai Yifei Li Weiming Zhai
Here is a non-exhaustive list of tools can be used as UTSim, FlyNetSim, UAV Toolbox MathWorks including Matlab…
For more details about this subject i suggest you to see links and attached flile on topic.
Article Unmanned Aerial Vehicle Propagation Datalink Tool Based on a...
Best regards
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For polarization diversity in wireless communication, often dual orthogonal polarization antenna is considered. I want to study the effect of non-orthogonal polarization antennas on wireless communication channels.
how to analyze the non-orthogonal polarization antenna effect to make a proper study of polarization diversity in MIMO wireless communication.
kindly suggest any relevant theory or research papers.
Thank you.
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To bring back a non-othogonal polarization to the orthogonal case, you will find you have a Gram-Schmidt correlation matrix to diagonalise m(i,j)= E(Xi Yj). Apply the Gram-Schmidt algorithm, you orthogonalise the reference vectors and get back to orthogonal polarization. The advantage over the orthogonal case from start is that your system can be more stable (through the additional "coupling"). Does it help?
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We know that in the Han-Kobayashi region, the to be transmitted data is divided into two parts, private and common. So, what type of information does these parts contain ?
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As far as I know, there is no difference between the data content in the private and common messages. One simply takes the bit streams to be transmitted (representing any type of data), split them into a private part and a common part, and then encode them as Han-Kobayashi proposed.
The difference lies in that the common message is encoded so it can be decoded at other receivers than the intended one. The purpose of the decoding is not to access its data content (at the application layer) but to remove interference (at the physical layer), and thereby enabling decoding of the messages that are truly of interest.
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Hello everyone, I want the solution manual for this book if there anyone have the solutions kindly refer it to me , Best regards.
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i am
professor
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In the textbook of Tse "Fundamental of wireless communications", the LOS MIMO channel model is characterized by $H = \alpha \times a(\theta) *a(\gamma)^H $ where a(\cdot)$ is the steering vector. The model uses conjugate transpose while there are other papers use transpose. This has puzzled me a lot. I understand if the transmitt antennas and receive antennas are different, both of them seem to be right. But how about the full-duplex antennas, which one should be right?
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There should be a transpose, not a conjugate transpose.
The same applies when considering uplink/downlink reciprocity: The uplink channel matrix is the transpose of the downlink channel matrix, not the conjugate transpose. The phase represents a propagation delay, and it is the same in both directions, not inverted.
Authors like to ignore this to obtain a more convenient presentation in textbooks and papers where the uplink and downlink are more mathematically symmetric (I've done that myself). But I think this isn't the right way to go if we want people to learn how to implement things in practice.
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I'm looking for an advises regarding Machine Learning methods applied in Wireless books to solve some optimization problems.
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You welcome
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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.
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I would describe it somewhat differently. In my vocabulary, channel estimation refers to the general concept of utilizing received signals to estimate the channel response. The estimation could be based on pilots, pilots+data or only data (the latter two are known as semi-blind and blind estimation).
Since the channel is changing over time, we need to estimate the channel repeatedly in a wireless system. Normally, we are only trying to estimate the current channel response, but one could also try to estimate future channel responses. This is what we call channel prediction. To succeed, you need an accurate model of the time variations. A Kalman filter is one method that can be used to predict future channels.
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In the context of wireless communication, I have heard people asking will the AI/ML model be trained online/offline? I would like to know what is the difference between these two terms and how they can impact the system performance?
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Interaction with a tutor is a must within a full automated process handled by the digital revolution, the following research is an answer to your question where online interaction reinforces and accelerates learning:
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Hey guys,
Please suggest the list of scopus list of journals for VLSI, Wireless networks and Wireless communications (Unpaid and paid)
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Hello venkataraman,
You can check the following link for free and fast publication of scopus journals.
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Since the importance of Machine Learning (ML) is significantly increasing, let's share our opinions, publications, or future applications on Optical Wireless Communication.
Thank you,
Ezgi Ertunc
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Hello everyone, I need some channel models MATLAB code to simulate my Underwater Visible Light Communication algorithm. If anybody has some models then please provide. thank you.
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Hello sir,
please check our paper Statistical Studies Using Goodness-of-Fit
Techniques With Dynamic Underwater Visible
Light Communication Channel Modeling,
Hopefully, it will cover your needs.
Also, If you need anything related to codes, send me an email at afikky@ucsc.edu
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Most of the authors presented their work on Mach-Zehnder modulator in fiber optic communication. When I saw Mach-Zehnder Modulator on internet for buying it is showing it with fiber optic cable both sides of Mach-Zehnder Modulator(input and oupt side). My question is that can we use Mach-zehnder modulator for intensity modulation in wireless optical communication? Please help.
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What are the areas in fading channels like TWDP,Rayleigh fading Channel,Nakagami fading Channels?? ....Is pursuing research in this area are limited?Is there is a chance of publishing many papers in this area?
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Wireless channels contain many propagation paths, with different amplitudes and phases. When a device moves around over distances proportional to the wavelength, the phases are changes rapidly and the paths might add constructively or destructively. If one models this statistically, you get a fading distribution.
Depending on the amplitude mix of the paths, different kinds of fading distribution appears. If all paths are equally strong, you will get Rayleigh fading. If one path is much stronger than the others, while the remaining ones are equally strong, you will get Rician fading.
If the fading doesn't match any of those categories, then the Nakagami distribution is a popular one that allows for fine-tuning a distribution to fit with measurements.
It can be of interest to evaluate the performance of communication systems in different fading scenarios, but one should avoid designing algorithms that are too reliant on a particular fading scenario because the distribution can change rapidly in practice. We talked about this in my podcast: https://ma-mimo.ellintech.se/2022/01/19/episode-25-what-models-are-useful/
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I am keen to do research in wireless communication. I would like to know research idea in this field.
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You may develop a novel security protocol or privacy preserving protocol and demonstrate that it is efficient as well
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Both deep learning (DL) and quantum computing (QC) are emerging technologies in the realm of research. Please share your valuable opinion on the following concerns.
  • How DL can be merged with QC?
  • What are the current challenges in the DL-based QC?
  • What are the tentative application areas for DL-based QC?
Thanks for your time.
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Dear Anik Islam Abhi.
Look the link, maybe useful.
Regards,
Shafagat
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hello, I'm looking for a simple model for a Reconfigurable Intelligent Surfaces-assisted wireless network that provides a vision for channel modeling for these systems.
no matter whether deep learning, machine learning, or convex optimization approach is applied to solving the problem and no matter that it is python or Matlab code
very thanks
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DearAlireza Qazavi,
In order to get expertise in writing MATLAB codes for your Optimization problems, you first need to follow this manual:https://www.matlabexpo.com/content/dam/mathworks/mathworks-dot-com/images/events/matlabexpo/us/2018/master-class-solving-optimization-problems-with-matlab.pdf
Moreover, by googeling your queries you may find bundle of techniques and examples. Practice it, and get its expert.
Good Luck
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For Intensity modulation, I am using external modulation. I want to simulate mach-zehnder modulator based intensity modulation. Please suggest simulation in MATLAB
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If you found something more about MZ coding. Please inform.
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I have an antenna module which is a planar phased array for 5G NR FR2 base station (i.e., BS type 2-O). I need to perform over-the-air (OTA) radiated conformance testing to verify the performance of antenna module to obey specification of 3GPP. Hence, I study the attached file 3GPP TS 38.141-2 V16.10.0.
For transmitter test (chapter 6), the module is connected with a signal generator (SG), which make NR signals generated by SG are emitted through antenna module. And then, a feed antenna is regarded as receiving end and connected with a signal analyzer (SA) and spectrum for analysing received signal. On the other hand, for receiver test (chapter 7), exchange the placements by module and probe antenna to perform conformance testing. The complete transmission and received process run in a CATR or anechoic chamber.
  • How to set parameters (ex. total output power, modulation, operation band, bandwidth, frame structure, single or multi-carrier, subcarrier spacing, TDD or FDD, and so on) of measuring equipment (SG and SA) for radiated conformance testing according to TS 38.141-2? Would you mind take an simple demonstration for test items in section 6.2 and 7.3?
In my opinion, the section 4.7.2, 4.9.2 and Annex A is strongly relate to the setup. (I am confused about difference between intention of test configurations and test model respectively in section 4.7.2 and 4.9.2. I googled other materials (others attached file) about this issue, there is only description about Test Model NR-FR2-TM in test procedure rather than Test Configurations NRTC )
I have some base of communication system but I am short of practical experience of RF measurement, so this problem maybe is not an easy question to answer. If you cannot answer by a few words, please provide clues or direction of search.
Many thanks for your answer.
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Ryan Kao Based on your attachments I assume you are procuring R&S instruments. If you look at section 2.4 in both the attachments, it lists the devices and options required to conduct the tests. [This is just one example from the documents.] Typically, such capablities are licensed software options that must be purchased with the instrument (or even a later purchase of license can enable the feature!).
It will be best to go through the instrument documents, which are quite tedious to navigate through; you may save some time by reaching out to the vendor application engineer (AE) who might be interacting with your department/ institute. Understanding the test environment system is key to correct test execution and measurement.
If you need some insight into RF measurements in general, you may want to check out my book "RF & μWave Measurements: For Design, Verification and Quality Control", which is available at select amazon marketplaces. However, it does not address your 3GPP specific question.
/rgds
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If so, we're hiring! - come and join our team to help develop the next generation of intelligent, wearable drug delivery devices. Research opportunities now available in microsensor integration, transdermal delivery and microfludics, and system electronics/communications. Further details available from Dr Conor O'Mahony - feel free to discuss with us!
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That's a very interesting research domain. I wish you best of luck
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In your opinion, what are the interesting and worthy topics for research in wireless communication systems?
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Thesis topics Wireless Systems 2019-2020
5G telecommunications: on-body massive MIMO virtual array design.
A visible light positioning network planner.
Accurate indoor human positioning system by combining Visible Light and Radio Frequency signals.
Agricultural IoT data collection for wide crop fields.
Regards,
Shafagat
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Could any one please help me in suggesting some resources where I could find a comparison curve between signal strength after Multi Path propagation effect with respect to obstacle positions between transmitter and receiver.
After conduction some experiment I found that the effect was greater near Rx or Near Tx but lesser when the obstacle is in same distance from Rx and Tx. Why such phenomena happens?
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I suppose this depends on what kind of obstacles you are considering and how they are affecting the signals. If you consider an object that is scattering the signal, then the pathloss will be proportional to (d_1*d_2)^2 where d_1 is the distance from the transmitter to the obstacle and d_2 is the distance from the obstacle to receiver. For a given total propagation distance d_1+d_2, it follows that the pathloss is at its smallest when the scattering object is close to the transmitter or the receiver.
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Hello
I am going through some digital communication literature, where some research papers have used the following formula to calculate the sum rate
Sum_rate=Summation (log2(1+SINR(i)) eq.1
My confusion is as follows:
1. Why is bandwidth(B) not included i.e. B.log2(1+SINR)
2. What will be the units (e.g. bps/Kbps/Mbps etc) if eq.1 is used.
Thanks
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That sum rate expression is measured in bit/s/Hz. If you multiply with the bandwidth, you get it in bit/s.
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Hello, i am searching for some new innovation points in 6G about networking slice recently.
I want to know what updates will be made to network slicing in 6G, which cannot do well in 5G.
(Maybe about architectures, methodologies, algorithms, and combination with other 6G new technologies.)
Can you recommend me some good ideas, innovation points or papers( and white papers), thank you.
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Dear Zhenkang Zheng,
the future virtual 6G networks in the form of 6G slices will definitely be set up as "Software-Defined and Open Programmable 6G Slices". The idea of SRv6 Network Programming as the basic principles for the programmability of 6G slices is specified in the Internet Standard RFC 8986.
I have attached a short list of sources of information.
Best regards and good luck
Anatol Badach
European Vision for the 6G Network Ecosystem; DOI: 10.5281/zenodo.5007671
Petar D. Bojović, Teodor Malbasic, Goran Martić, Zivko Bojovic: „Toward 6G networks: Dynamic management for multi-slice QoS“
Wen Wu, Conghao Zhou, Mushu Li, Huaqing Wu, Haibo Zhou, Ning Zhang, Xuemin (Sherman) Shen, Weihua Zhuang: AI-Native Network Slicing for 6G Networks
Amin Shahraki, Mahmoud Abbasi, Md. Jalil Piran, Amir Taherkordi: „A Comprehensive Survey on 6G Networks: Applications, Core Services, Enabling Technologies, and Future Challenges“
European Vision for the 6G Network Ecosystem
Jie Dong: SRv6 based Network Slicing
Gaps, features and enablers for 5G/6G service management and orchestration
RFC 8986: Segment Routing over IPv6 (SRv6) Network Programming
SRv6 – IPv6 Segment Routing Figure 009239: Basic idea of SRv6 Network Programming
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Recently, I focus on UAV-enabled air-to-air wireless communications, in which the UAVs are flying at an altitude of 60~150 m. In this case, which channel model is more applicable? Intuitively, I think the LoS channel is suitable since there should be no obstacles at this high altitude. Is my idea correct? Could you provide me with some references, thank you very much for your help!
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Dear Jiahui Li ,
If there is no obstacles in the path between the transmitter and the receiver and the medium is air then then the channel is for sure a line of sight.
If there is reflection from the ground to the receiver then there will be multi path effect. In this case one would model the channel by Rician model.
Both model are treated intensively in the literature. I think by skilled antenna directing to the UAV one can reduce the reception of the reflected radiation from the ground.
Best wishes
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Please guide me about the difference between SINR threshold and Minimum discernable signal.
From my search I have come across the following.
1) A signal can be decoded if the SINR of the received signal is higher than the SINR threshold. Does it mean that we should not be concerned about the minimum required power, and that if the received signal satisfies the SINR threshold, it will be successfully decoded?
2) I also have come across the idea of minimum discernable level. For instance -70 db is considered acceptable for some types of communication.
Which of the two I should follow. As in the first case, I get very low transmit powers and still satisfy the SINR threshold, while the transmit powers in the 2nd case are way too high compared to the first case.
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Dear Gul Lakhta ,
There is something basic which you you would like to consider. From the point view of the signals, the interference signal acts in a similar way to noise. When it is added to the symbol signal it will cause that it may be falsely detected if its value is increases such that the combined signal will be located in the neighboring decision regions of the adjacent symbols in the constellation diagram. So the interference and noise have the same effect in the symbol error.
Therefore one speaks not only from S/N ratio but also from the S/(N+I).
The noise has Gaussian amplitude distribution while the interference may not have such distribution because of the nature of the interference.
The interference signal sources are normally known and then they can be subtracted from the composite signal. If after interference cancellation there will be residual interference it will be treated as a noise concerning its effect on the symbol error. In the sense that it will set the S/I +N minimum level to achieve specific bit error rate. You see if N=0 then S/I will set this minimum.
Hope I could answer satisfactorily you question.
Best wishes
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Recently, I have seen in many papers reviewers are asking to provide computation complexities for the proposed algorithms. I was wondering what would be the formal way to do that, especially for the short papers where pages are limited. Please share your expertise regarding the computational complexities of algorithms in short papers.
Thanks in advance.
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You have to explain time and space complexity of your algorithm
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I need to write code in MATLAB to find the impulse response of infrared optical wireless communication (IROWC) in the diffuse distribution (wide beam transmitter and wide FOV receiver) ...
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Aparna Sathya Murthy , thank you very much for your help ....
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Game theory is a very promising technique to achieve optimal outcomes and can be applied to almost all concepts. I am trying to explore game theory for future purposes. However, as a beginner, I couldn't get very good resources regarding game theory.
Please share your resources (i.e., video or blog tutorial, research paper) regarding game theory, which covers the following things.
1. How to apply game theory?
2. How to prove optimal gain after applying game theory, (e.g., proving Nash equilibrium).
3. What are the state-of-the-art game theory techniques?
Thanks in advance.
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Anik Islam Abhi For example forecasting and game theory are both effective tools to support finance-related decision-making and reduce the impact of risk factors.
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While reading the literature regarding performance analysis of RIS-Assisted Wireless communication Systems I went through the following terms: Achievable Rate, Sum Rate, Ergodic capacity. How do understand them?
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The ergodic capacity is the theoretically maximum data rate (bit/s) that one communicate at over a fading channel, where the fading processing is an ergodic process. More precisely, this is the largest rate for which the probability of decoding errors goes to zero as the length of the transmission goes to infinity.
An achievable rate is any data rate below the capacity. It is normally associated with a particular (suboptimal but practical) way of transmitting.
The sum rate is the summation of the achievable rates of multiple concurrent transmissions, for example, different users that are multiplexed.
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Please help me with the following formulation.
I want to calculate transmit power that can satisfy a given SINR threshold on the receiver side.
My formulation is as follows:
SINR=Received_Power/(Interference+Noise) Eq. 1
If SINR Threshold (SINR_th) is known, we can get the Required_Received_Power and thus the "Required_Transmit_Power" power as follows:
SINR_th=Required_Received_Power/(Interference+Noise) Eq. 2
We know that
Received_Power=Transmit_Power/Pathloss; Eq. 3
OR
Required_Received_Power=Required_Transmit_Power/Pathloss; Eq. 4
Substituing "Required_Received_Power" in Eq.2 with the right hand side of Eq.4, we get
SINR_th=(Required_Transmit_Power/Pathloss)/(Interference+Noise) Eq. 5
OR
Required_Transmit_Power=SINR_th x (Interference+Noise) x Pathloss Eq. 6
Please advise whether this formulation is correct or not as I am getting wrong results.
Thank you!!!
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Muhammad Hamza El-Saba Thank you very much for your answer.
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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?
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The length of the cyclic prefix should be at least equal to the number of channel taps minus one, so that the linear channel filtering appears as if it is a cyclic convolution.
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Thanks in advance
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you can use ns3, have look at the documentation.
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I am curious about what happened to the atomizer software by Buckheit J. (http://statweb.stanford.edu/~wavelab/personnel/) and if it is available somewhere.
Sadly I found only 2 dodgy sites that require a login to download the MATLAB code. Does someone have any information on where to get it from?
Alternatively, if there are other toolkits that have implemented this code please let me know, it does not have to be MATLAB, any language is fine for me :).
Thank you.
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Old news but I found it here (http://sparselab.stanford.edu/atomizer/) if anyone finds this post while searching for it.
I've also downloaded the ZIP for posterity. Anyone feel free to get in touch if you need it. I plan on keeping it forever.
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Hi everyone, hope you are perfect!
today I am going to have a plea of you to lend me a hand if is possible.
as an wireless communication researcher, I come across many phrases that I have no idea about them. one of them is "optical front-hauls" and another one is "grant-free Transmission"!!!
If you can give me an imagination or illustration, what would it be?
Thanks for making time in advance.
#communication #5gtechnology #6G #wirelesscommunications
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Adding to the colleague U. Dreher , you can see the definition of the optical front-hauls in the paper at the link: https://www.3coptics.com/News/9.html
The optical from haul is the optical fiber link between the base band unit and the remote radio unit. It transmits the base band signal to the the radio unit.
grant-free Transmission" is the transmission in which the quality of service is not granted. This is the bit rate and the bit error rate.
Best wishes
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How can I calculate the bit error rate of orbital angular momentum for wireless communication? I found this paper 'Error probability on the detection of orbital angular momentum detection' but in this paper, they are calculating the probability of the detection of the OAM mode like if the information were carried by the mode. While I am interested in the case where OAM modes are used to create orthogonal channels.
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Perhaps you can use generic tables to calculate that.
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How do we represent zero interference on db scale?
I am using the following formula for calculating the Received_Power_TH (TH stands for threshold). Received_Power_TH is the power of the received signal required for successful decoding. I am using the following formulation.
SINR=Received_Power - Interference - Noise Or
The Recieved_Power that satisfies the SINR_TH (or in other words the minimum Received_Power required for successful decoding) is given as follows
SINR_TH=Received_Power_TH - Interference - Noise Or
Received_Power_TH =SINR_TH + Interference + Noise
However, i get the following results which seem counter intuitive.
Assuming SINR=25, Interference=-50, Noise= -95
Received_Power_Th =25-50-95=-120
Now if we do not have any interference e.g. in a case when there is only one node transmitting, we get the following
Received_Power_TH=25-95=-70
This result seems counter intuitive as Received_Power_TH for successful reception in case of no interference should drop below the value we get when we have interference whereas in this case the Received_Power_TH is equal to -120 in case of interference and it increases to -70 when there is no interference.
Many thanks
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Zero is equal to -infinity in dB scale, since log10(0) = -infinity.
An issue with your computations is that the interference and noise power should be added together in linear scale when computing the SINR, not dB scale. So if the interference is -50 dBm and the noise is -95 dBm, the interference+noise is 10^(-5)+10^(-9.5) mW, which you can then convert to dBm as 10*log10( 10^(-5)+10^(-9.5) ).
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While going through the Literature I found that the performance analysis of wireless communication System was done by us one of the following three methods:
1. Moment Generating Function-Based performance Analysis
2. PDF Based performance Analysis
3. Characteristic Function-Based performance Analysis
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Agree with Dr. Omid M Kandelusy
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I want to know about ML algorithms that are used to train the data set: e.g. in routing protocols in WSN.
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data mining techsnique
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Apart from experiments available for WiFi, IoT, LTE etc., we also want our students to work on experiments on 5G. Does someone have a set of pre-built NetSim experiments which we can quickly adopt for our course? If not, any suggestions on what kind of experiments can be done would be valuable? Thank you.
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NetSim support a range of technologies, including the capability to interface Matlab and C++ programs. So if you want to integrate 5G in a Netsim experiment, you will need to deploy the 5G experiment in Matlab then interface it with NetSim for full deployment. I hope this answers your question.
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Is it possible to employ Beamforming for interference mitigation when two smart phones are communicating directly without any relay (such as Access Point, Base Station or some other kind of relay nodes) in between. We assume that many pairs of smart phone pairs are communicating directly and may interfere with each other. Therefore beamforming (if possible) will help reduce interference.
Much appreciated in advance.
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Yes, some smart phones have MIMO capability and so they can be used to realize your proposal.
Best wishes
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I need to know about the appropriate tools for modeling, simulation, and analysis of wireless communication systems other than MATLab.
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Castallia, Contiki OS, OMNET ++, TinyOs are few are the options to simulate WSN. NS3 can be used but it is not designed for WSN as such.
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IRS can create higher beamforming gain with the help of an “intelligent” reflector. But, this brings other practical issues.
How does BS/AP synchronize with the reflector about the amplitude and phase?
where IRS is the intelligent reflective surfaces, BS is the base station and AP is the access point.
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The basic algorithms for phase configuration and channel estimation are described in our tutorial article: https://arxiv.org/pdf/2102.00742.pdf
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In wireless communication channel modeling, capacity is calculated based on computed H matrix and SNR value.
H matrix is calculated through a different models - COST..WINNER..etc.
I am wondering how to calculate SNR at different locations of UE In a cell?
many papers assume a certain SNR before capacity calculation.
How one can calculate SNR in an urban macro environment in presence of scatterers with different BS-UE positions in distance and angle?
I am looking for SNR calculation throughout a cell for MIMO wireless channel using directive antennas.
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The colleagues above gave satisfactory answer. The ultimate answer of your question is to make survey test at all intended points where one one can measure directly the recieved signal strength. This means one has to determine the spatial distribution of the received signal at the coverage area.
May be the best way is to determine such distribution empirically.
But this may be very tedious.
The other solution is to use use models of large scale fading and small scale fading to obtain statistical channel models.
The last possible solution is to make an electromagnetic solution for the signals emitted from the antennas in the real propagation space with districted scatters.
Best wishes
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In 5G, the mixed-numerology is introduced to support diverse QoS requirements. It is also mentioned in the spec that simultaneous transmission and reception with different numerologies in CA is possible.
However, there is few materials mentions how this is achieved. Does a UE requires multiple antennas to transmit with resources of different numerology at the same time? Or it can be done by a single antenna? If the latter is the truth, how does a UE deal with different SINR values generated by resources with different numerologies? (different SINR values lead to different modulation scheme, how can this be done with single antenna?)
p.s.
I have found one paper that is related to my question:
It seems that a UE can deal with resources with different numerology simultaneously, with different modulation scheme. Yet, this paper does not show how this is achieved by the smartphone.
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It would be nice from you that you define the relation between the communication symbols and numerology??
This is the first time that I hear from such vocabulary in electrical communication systems.
What I know is the different symbols resulting after modulating the binary data.
There are binary symbol where every symbol contains either binary 0 or 1.
There is the the 2-bit symbol representing values from 00,10,01 and 11.
There is 3-bit symbol which has 8 states.
and so on.
This method of M-ary symbols help control the number of bit hertz.
And one can send more data in the same bandwidth by using higher order modulation.
It is known that every channel condition or S/N ratio can allow specific symbols.
If it mean symbols then yes one can control the order of modulation of the symbol.
Best wishes
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I'm looking for the best tool for UAVs' path planning; also the tool should support wireless communication.
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Matlab UAV toolbox should be a very powerful tool.
Guidance models and navigation, trajectory and waypoint following:
UAV flight path using translations and rotations
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How fast does the traffic load of WiFi network change? What is the average time that the WiFi traffic load remains constant for different traffic types e.g. internet browsing, video streaming, voice call, e.t.c ?
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Your question is not highly dependent and specific on wifi. It seems more important to think in terms of network traffic.
If the WiFi hub considered serves a home, you need to build scenarios:
-family members: children, parents, flat sharing people
-working hours: if working from home, how many videoconferencing sessions ,average duration, data upload and download between home location and organisation VPN
-outside of working hours: sessions of gaming, movies ("Netflix" or equivalent, movie of 120 minutes or more etc), music streaming. Browsing, YouTube, etc...
You may run a number of differently populated scenarios to explore and understand...
This may show you the limit of WiFi and take you into fine access...
As to how to picture for yourself what happens in homes during the current era of Covid19, you may benefit from browsing this study/model:
How does it work for you?
Let me know
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I am trying to obtain closed-form expressions of outage probability for a user in a given cell. Here I am including inter-cell interference (from other adjacent cells) in the expression of SINR. Now I need to obtain the closed-form expressions of outage probability over a fading channel like Nakagami or Rayleigh. Please suggest any information regarding this. thanking you
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It is generally hard find closed-form expressions. It always the same math involved:
Say that the SINR is S/(I+sigma^2), where S and I are random due to fading. Then the outage probability when communicating at the rate x is:
Pr{ log2(1+S/(I+sigma^2)) < x } = Pr{ S/(I+sigma^2) < 2^x - 1 }.
If you know the distribution of S/(I+sigma^2), then you can compute this directly.
If you only know the PDFs of S and I individually, then you need to rewrite it as
Pr{ S/(I+sigma^2) < 2^x - 1 } = Pr{ S - I (2^x - 1) < sigma^2 (2^x - 1) }.
Now "sigma^2 (2^x - 1)" is a constant, but you need to compute the PDF of "S - I (2^x - 1)", which can be done via integration. The joint PDF of the sum of random variables is obtained by convolution.
At the end of the day, the chance that you get a nice PDF of "S - I (2^x - 1)" so that you then compute the probability is closed form by integration over the PDF from 0 to sigma^2 (2^x - 1) is not so big.
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I want to know how can we implement nakagami fading in MATLAB and get the small scale fading coefficients for this nakagami distribution in MIMO-OFDM channel. Any MATLAB code would be helpful. Are these coefficients time varying and how to get these coefficients for a certain time interval?
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try this code:
"function H=nak_m(m,Nr,Nt)
n=zeros(Nr,Nt);
for i=1:2*m
n=n+randn(Nr,Nt).^2;
end
n=n/(2*m);
phi=2*pi*rand(Nr,Nt);
H=(n^0.5).*cos(phi)+j*(n^0.5).*sin(phi);"
I hope it helps
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I am try to build a system model where intelligent reflecting surface is used to enhance the receiver's SNR depending on the position of the receiver.
Based on what I read, a signal is scattered at the atom level and constructive/ destructive superposition is used to direct the signal to a certain user. My question is: Can the signal be directed to a user behind the IRS?
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Sure, if two users are located in the same angular direction but at different distances, the reflected beam can be partially directed to both of them. You can find an example of this on Figure 10 of https://arxiv.org/pdf/2002.04960.pdf
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Dear all,
We all know about the fast and rapid developments in machine learning algorithms. But the question is that if we want to implement them for low latency (less than 1 ms) applications using wireless communications, is it possible?
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I think it will depend on the complexity of the algorithm and the performance of the hardware in terms of CPU.
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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.
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You can create the channel directly with Matlab using the "comm.RicianChannel" then apply this filter to your signal
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In addition to the path made by 5G (small cell coverage, massive mimo, mm-waves, etc ), 6G is going to merge other areas such as bio-medical, artificial intelligence, robotics, etc.
What are the key enabling technologies of the 6G networks?
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How can I estimate the propagation loss between an unmanned aerial vehicle (UAV) or drone, and user equipment (UE) on the ground? is it safe to assume a line-of-sight scenario? how about the drone's altitude? is there a specific air-to-ground formula I can use?
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