Wireless Communications - Science topic

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:

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:

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.

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.

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.

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));

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:

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.

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.

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

Amipara Manilal D

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 ?

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.

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.

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.

If X_1,...,X_N are independent random variables, then your derivation is correct.

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.

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

link.springer.com/chapter/10.1007%2F978-3-319-13823-7_25 -

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

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?

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.

i am

professor

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.

You welcome

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.

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:

Hello venkataraman,

You can check the following link for free and fast publication of scopus journals.

Our new paper.

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

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/

You may develop a novel security protocol or privacy preserving protocol and demonstrate that it is efficient as well

Dear Anik Islam Abhi.

Look the link, maybe useful.

Regards,

Shafagat

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

Also some examples : https://www.mathworks.com/help/optim/ug/optimization-toolbox-tutorial.html

Moreover, by googeling your queries you may find bundle of techniques and examples. Practice it, and get its expert.

Good Luck

Thanks Aparna Sathya Murthy Anil Kamboj

If you found something more about MZ coding. Please inform.

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

That's a very interesting research domain. I wish you best of luck

Dear Omar ALzoubi ,

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

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.

That sum rate expression is measured in bit/s/Hz. If you multiply with the bandwidth, you get it in bit/s.

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

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

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

You have to explain time and space complexity of your algorithm

Aparna Sathya Murthy , thank you very much for your help ....

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.

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.

Muhammad Hamza El-Saba Thank you very much for your answer.

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.

you can use ns3, have look at the documentation.

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.

Dear Arash Darabi ,

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

Perhaps you can use generic tables to calculate that.

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) ).

Agree with Dr. Omid M Kandelusy

data mining techsnique

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.

Dear Tariq Islam ,

Yes, some smart phones have MIMO capability and so they can be used to realize your proposal.

Best wishes

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.

The basic algorithms for phase configuration and channel estimation are described in our tutorial article: https://arxiv.org/pdf/2102.00742.pdf

Dear Laxmikant Minz ,

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

Dear Yukinari He ,

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

Matlab UAV toolbox should be a very powerful tool.

Guidance models and navigation, trajectory and waypoint following:

UAV flight path using translations and rotations

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

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.

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

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

I think it will depend on the complexity of the algorithm and the performance of the hardware in terms of CPU.

You can create the channel directly with Matlab using the "comm.RicianChannel" then apply this filter to your signal

Muhammad Abbas Khan Thank you