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# MIMO - Science topic

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I am looking forward to simulating a scenario with a large number of transmit antennas in the gNB and multiple antennas in the UE as well, which is also known as MIMO. I am using NetSim simulator. How can I model MIMO and beamforming? And what is theoretically the effect on the system? How much is throughput or coverage expected to increase?
Prahlad Prabhu MIMO would give you higher antennas gains depending on the kind of algorithm used. Higher gains mean higher received signal strength and hence increased coverage.
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Am working on a two-element MIMO antenna, I extracted the multiplexing efficiency of my proposed antenna the result I have, is doubting because the values of the efficiency am getting is negative please can someone help me on how to extract the multiplexing efficiency?
Already my results are in dB. I can also post the result to you
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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.
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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Aamir Habib Would you know the average gain in each of the 2 layers when TM mode 3 - open loop spatial multiplexing - is used?
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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?
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 have bulit a MIMO system, Now I want to verify the effects of imperfect CIS on MIMO BER? As the depicted in the picture, I need help to construct this model in MATLAB.
This is just one of the many ways to model imperfect CSI, so make sure that you understand the underlying assumptions of the model that you have selected.
Your screenshot only reveals a part of the modeling assumptions that were made in the paper, so I cannot give any precise answer.
If you want to generate two complex Gaussian variables h1 and h2 with unit variance and correlation rho, then you can do that like:
a = (randn(1,1)+1i*randn(1,1))/sqrt(2);
b = (randn(1,1)+1i*randn(1,1))/sqrt(2);
c = (randn(1,1)+1i*randn(1,1))/sqrt(2);
h1 = sqrt(rho)*a + sqrt(1-rho)*b;
h2 = sqrt(rho)*a + sqrt(1-rho)*c;
These variables are made up by one part a that is equal and one part b or c that is independent.
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Hello everyone,
How to Plot ECC ,CCL, MEG, TARC, DG curves for MIMO antenna having more than two ports using HFSS ?
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Any body who wants to guide me???
Moreover, you can consider IRS (INTELLIGENT REFLECTING SURFACES) and NOMA in LTE networks.
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list of SCI journals that will take low time for responding
( related to MIMO antenna and computational Electromagnetic)
Dear Raj,
Unfortunately, most of non-open access journals are not responding fast and they push most of the time to reject. but you can try a relevant journal managed by Springer.
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Hi,
In many designs I saw that the ground of various MIMO elements are joined together by a thin line.
Is it mandatory to join the grounds of various MIMO elements, or it is not necessary.
hello mam,
Actually i was asking that i made a single element then copied it for times. the single element has a ground on the backside. so all 4 elements have backside ground. is it necessary to add these 4 grounds to each other to make a single ground over all 4 patch elements in mimo.
i have an answer from some professor that if they will not be added then it is not a correct mimo.
it it so, you have any idea.
regards
rahul
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Hi,
I am working on 4x4MIMO antenna design. My structure is radiating at 26Ghz. The feedposition of antenna elements are kept same and only 1 antenna is excited at present, but problem i am facing is that all 4 antenna elements(S11,S22,S33 and S44) should radiate at same resonant frequency. I have attached s parameter graph for reference. Can anyone guide me.
If you will use multiport feeding technique on a single antenna.Finally you will get different resonance .It is common and easiest way to find S11'S22''S12'S21 etc for single antenna or array of antenna.
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I'm planning on making a 4x4 Wireless antenna @ 3.5GHz. What should be the ideal return loss and VSWR values??
Hmmm. I guess I read that as "loss," rather than "return loss." Loss along the transmission line.
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We are trying to understand MIMO-SAR. Can anymore suggest good study resources for the same. Thank you.
Maybe you can take a look at the Tandem-X project of the DLR.
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Looking for alternative MIMO based automotive radars other than TI make with low cost and yet provide good performance.
I agree with Ara Abdulsatar Assim
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Can anyone help me in calculating MIMO parameters (ECC, DG, MEG, and TARC) from the fabricated S parameter values? I need to plot the comparison graph.
Have you gotten a way of doing that.? If it is yes please let me know
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Can anyone suggest to me method to design fuzzy inference system (FIS) for MIMO structural damage detection (i.e. data distribution on the membership function, parameters of MF, Generate rules ... etc )
In my system there are 3 inputs and 2 outputs:
Inputs: Relative 1st Natural frequency , Relative 2nd Natural frequency , Relative 3rd Natural frequency
Outputs: Crack depth ratio , Crack Length
Note: I tried to use "genfis" By MATLAB it didn't give me reasonable results.
Is it possible for you to share your data with me? I have some idea to solve your problem but I need your data.
I'm in touch by : ahmadi.v.1380@gmail.com
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Sir, Can I know about the how-to simulate discrete-time sliding mode control Matlab simulation. I am confused I am familiar with continues but sir I am interested in research on the discrete-time sliding mode control..so sir please share any MIMO type of example for simulation
For discrete-time sliding mode control, first, you need to define the size of the window. Apply the transformation/processing on those samples that come under the window. Slide the window by one sample and repeat it till reaches the last sample.
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It is known that for optimal RIS control, perfect CSI of all the links between BS and MS through the RIS is required. Therefore, channel estimation and corresponding message feedback methods will be needed at the BS/MS.
Few studies suggest a two-stage channel estimation approach for RIS-aided MIMO channels, using iterative re-weighted method for estimating channel parameters sequentially.
Will this be a good way to go about it, or there is/are better methods.
If there is no specific channel structure, then you will have to transmit pilots using N different RIS configurations, in order to excite all dimensions of the channel.
However, if there is some channel structure (e.g., spatial sparsity) one might be able to reduce the pilot signaling.
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Is there MATLAB code to generate MIMO overlapping triangular membership functions with left and right shoulders?
You can manually construct the desired membership functions using some mathematical piecewise linear functions. However, if your intention is to find the degree of truth, there is a much easier way.
Use a fismf object, you can easily construct the desired membership functions. Next, use evalmf(mf,x) evaluates the membership function mf based on the input values in x, returning the degree of truth. For more info, please look up:
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MIMO Antenna design for energy harvesting
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I want to know how the detailed step-wise procedure to measure the ECC data from the far-field pattern. If the process needs to be conducted inside an anechoic chamber, collecting such a large amount of data for different orientations looks unfeasible.
i hope this link help u
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I've read many posts and papers, where both the terms are used interchangeably. For instance, I read this sentence somewhere: "Codebook-based precoding is a promising new technique of beamforming". Now here I'm confuse. Are the two terms same?
As stated in a blog post by Prof. Björnson, the two words can be used to denote the same thing. However, sometimes "beamforming" is used to denote the analog version (using phase shifters, one RF chain for all the antennas) while "precoding" denotes the fully digital one (one RF chain per antenna).
Here is the blog post for a detailed explanation:
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MMSE and sparse recovery methods both aim at improving the classical LS channel estimation by injecting prior information. On the one hand, the prior information is learnt over time for MMSE (it is assumed that the channel is a correlated gaussian with a covariance matrix learnt from previous channel estimates). On the other hand, the prior information is based on physics for sparse recovery methods (it is assumed that only a few paths contribute significantly to the channel so that provided the plane wave assumption holds, it can be expressed as a sparse linear combination of steering vectors).
I wonder if the two methods have already been thoroughly compared on realistic channels.
These are estimation methods that minimize the estimation error under different prior information.
MMSE estimation uses the exact channel distribution to find the estimator that minimizes the MSE.
If we don't know the exact channel distribution, but know that the channel is sparse in some domain, then compressive sensing methods can be used to find the best estimate that satisfy that condition.
Since the estimators build on different prior information, a comparison will be unfair. The MMSE estimator is better since it knows more about the channel.
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In MIMO channel estimation, the linear minimum mean squares estimator (LMMSE) yields better performance than the least-squares (LS) estimator. However, it requires knowing the channel covariance matrix (which constitutes prior information). In practice, the channel covariance has to be estimated based on previous channel estimations, but how are these previous channels estimated? With LS? With MMSE using an identity matrix as covariance? Anything else?
You can check out the work by Saeid Haghighatshoar and Giuseppe Caire, such as https://arxiv.org/pdf/2110.03324
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Dear all,
I have prepared a data set and I am seeking to apply my data on a multi-input multi-output algorithm. I appreciate it if you let me know how can I download such algorithms? I mean I have not come across any particular website that offers these types of ANN algorithms.
FYI, I am trying to tune an array of inputs onto an array of targets. So, please help me out with my query as declared above.
Thanks
Pleasure M. Zandie
Good Day.
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TARC stands for Total Active Reflection Coefficient
ECC stands for Effective Correlation Coefficient
DG stands for Diversity Gain
CCL Stands for Channel Capacity Loss
MEG Stands for Mean Effective gain
Four Port MIMO antenna consist of four radiators.
Don't get confused with the formulae given in the literature. They are defined for CCL or ECC or DG for any two port.
If you have eight port, you need to calculate 28 different values of CCL for different combinations of ports.
Let me explain in detail.
For 8 port MIMO, you have port 1, 2, 3, 4, 5, 6, 7, 8
You have to make different combinations of ports and calculate CCL for port12, then port13, then port14, 15, 16, 17, 18, 23, 24, 25, 26, 27, 28, 34, 35 and so on...
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Dear colleagues,
I am developing an HVAC MIMO model, through writing the state equations I am facing the attached state equation where in the third term, two states are multiplied in one term which creates a non-linearity in the system. The equation (part of eleven state equations) is attached
a,b,c and d are constants while (Tcc) is the cooling coil temperature, (Trfrgnt) is the temperature of the refrigerant (qsa) is the mass flow rate of the blown air, (Tra) is the temperature of the recirculated air and (To) is the ambient temperature
Now I face a problem of how to establish the A matrix (11 x 11), what is the best way of linearization of this particular equation? Do I need to linearize only the nonlinear term in the equation? Once linearized, does the linearized process effect the other state equations?
Thank you sir, I will be sending you an email right now.
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I have five rectennas and want to collect the output DC power in a series connection but when I do that only the first rectenna work. In the case of balanced and unbalanced. the connection is attached below.
I agree with Mr Fritz caspers .
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Hi,
I want to know how to get SNRinstantaneous/SNRaverage for SISO (1x1) and MIMO (4x4) during simulation in HFSS or CST to plot the Cumulative Distribution Function (CDF) and get the diversity gain.
Is there any formula for this?
With Regards,
Suhas D
There are three methods from which you can extract the diversity order of a communication system for any fading model.
Method 1(Analytical, the best): Derive the CDF or the SER of the system. Define the instantaneous SNR= SNR_0 |h|^2, where SNR_0 is the SNR without fading or average SNR. Keeping SNR_0 is the most important aspect. Do not normalize it to 1 and become clueless. Considering Rayleigh fading as an example with the CDF F(SNR)= 1-exp(-SNR/SNR_0). Now, find an expression of the CDF at high SNR. We need to be careful which SNR should be high SNR or SNR_0. It is SNR_0 and not SNR. Actually, for the outage probability, SNR becomes the threshold SNR. Let us take a limit of F(SNR) when SNR_0 tends to infinity. For diversity analysis, it is important to find an expression with each term as a function of SNR_0. Using exp(-x)= 1-x for x tends to zero, we get F(SNR, SNR_0 to infinity)= SNR/SNR_0. Taking the negative exponent, the diversity order for Rayleigh fading is 1. In general, representing the CDF in terms of SNR_0 at high SNR and taking the powers of a dominant term gives the diversity order. For example, let us consider for a general fading F(SNR, SNR_0 to infinity)= a*(SNR/SNR_0)^M+ b*(SNR/SNR_0)^N, the diversity order is min (M, N).
Method 2(Simulation experiments, Slope): Plot the CDF versus SNR_0 at a given SNR. Let us consider a general CDF representation F(SNR, SNR_0)= function(a, b, M, N, SNR, SNR_0). Suppose the diversity order is min(M, N). Plot 1: For a fixed M, N, an increase/decrease in the parameters a, b may shift the plots but the slope will remain the same. Plot2: For fixed values of a and b, and M<N: There will be no change in the slope if a, b, or N changes. However, a decrease in M changes the slope showing the dependence. If you increase M such that M>N, the slope is determined by the N.
Method3 (Code, Slope): Generate CDF (at a given SNR threshold) versus SNR_0. Keep SNR_0 as high as possible. Save data of SNR_0 and corresponding CDF in a vector. Express the data in log-scale, and then find the slope. A Matlab code is here:
Vector_SNR_0=10*log10[{SNR_0}];
Vector_CDF= 10*log10[{CDF}];
Slope=( Vector_SNR_0(end)- Vector_SNR_0(end-1))/(Vector_CDF(end)-Vector_CDF(end-1)).
The estimate of diversity order is accurate as long as you keep SNR_0 as high as possible and the resolution of data points is as low as possible.
Thanks for reading the long post. The explanation as given above is in general nature and may clarify few doubts on the diversity order.
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Hi, I am working on L1 adaptive control where I need to find the Right Interactor matrix for my MIMO transfer system G(s). I am following the algorithm given in "A simple state-space design of an interactor for a non-square system via system matrix pencil approach." by Xin Xin and Tsutomu Mita. I have attached the figure for clarification.
I need to understand how Equation-19(In the figure) gives me a state-space model since there is Laplace variable 's' being used instead of just constant Matrices A, B, C, D. I have all the block matrices A11, A12, A21, A22, and H, however, I could not find this representation anywhere and hence failed to get Z(s).
Any help would be appreciated.
Thank you Simone Pozzoli
I tried that too but I could not succeed to reproduce the results of the example given in section 5.
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I am presently implementing MIMO OFDM for a high speed underwater modem??
The original Sch and Cox algo isn't based on guard length (GL). The GL based algo are effective only when CFO is very small (less than 0.5 normalised CFO i.e freq deviation divided by the subcarrier spacing). The limitation is due to the large time difference between te GL and rear-part of the OFDM symbol. As GL has to be correlated with the rear-part, the huge gap between them limits the amount of CFO estimation. The Sch and Cox can compensate large CFO by using a different way in which two special OFDM symbols are generated and used as preambles. The first symbol consists of two identical halves and correlation is checked between two halves. The CFO range can be further increased by making 4 quarters of that OFDM preamble and performing the correlation among them
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Please any one can suggest the selection criteria for power normalization and precoding methods in single user MIMO and multi user MIMO systems. Please also give the reasons.
In single-user MIMO, one should apply SVD precoding (including water filling). There is just one way to do that.
In multi-user MIMO, the power allocation must always be selected to optimize some performance criterion that involves all the users, for example, maximum sum rate. The precoding normalization isn’t particularly important as long as the power allocation is done properly since the end result will always be the same.
A common mistake is however to assume “equal power allocation” but forget to normalize every precoding vector to have the same norm, which is needed to truly get equal power allocation. The actual power that is allocated to a user is the product of the power parameter and the squared
norm of the precoding vector.
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STATE SPACE EQUATION,
dx/dt = AX+BU+tPd;
Y=CX+DU.how to find out the transfer function of the system with disturbance matrix?
Hi...
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How to Plot ECC ,CCLcurves for MIMO antenna having more than two ports in CST? For example in case of a 8x8 MIMO antenna ?
Normally ECC should be calulated from Farfield patterns:
Go to post processing, Farfield antenna propetries, Farfield results, Select MIMO Envelope Correlation Coefficient, Select All Settings, Top Left go to browse result and select 1, Then on right hand side click on MIMO tab and in Second farfield result again click on browse result and select second antenna. Ok and then evaluate.
At a time you will get ECC between any two ports.
Hope this helps...
all the best!!!
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I have designed an aggregated plant (P) which is a MIMO system. I have designed an optimal controller (H_inf) that optimally minimized the norm of the closed-loop system. I would like to analyze the characteristics of the overall designed model using some tools. For example, I would like to analyze the convergence speed or trajectory of poles movement. What tools should I use that can analyze my model in the best way possible.
Abbas Thajeel Alsahlanee Thank you for your response. However, my model is a newly designed model, and there is no information about this model in literature. For this reason, I am looking for a generalized way to learn and implement that method on my model to find the convergence speed.
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So the two patch antennas are designed to resonate at the frequency 5.8GHz. now to enhance the mutuel coupling bewteen them, they put a structure of 3 SRR that act kinda like a filter for certain frequencies therefore better isolation will be obtained. now for the reconfiguration part, is kinda confusing for me, they add 6 pin diodes to enhance gain, how is that? if anyone could explain me the theory behind it or even link some refference that could help me understand this design, i'd be greateful.
Dear Oussama Zeggai , as I understand by different activation of pin-diodes can be changed the level of mutual coupling between two patch antennas. In the general case, two connected patch antennas will have more gain than only the one patch antenna. With help of CSRR with different shorting, you can change mutual coupling and can change the common effective surface of two antennas elements
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I mean the observer gain can be time-varying or updated with an adaptive law. I want to learn some cases or theories on this thesis. Can anyone offer some references or study cases?
Maybe you can consider the recursive least squares algorithm (RLS) which allows for (real-time) dynamical application of least squares (LS) regression to a time series of time-stamped continuously acquired data points. As with LS, there may be several correlation equations and a set of dependent (observed) variables. RLS is the recursive application of the well-known LS regression algorithm, so that each new data point is taken in account to modify (correct) a previous estimate of the parameters from some linear (or linearized) correlation thought to model the observed system. For RLS with forgetting factor (RLS-FF), adquired data is weighted according to its age, with increased weight given to the most recent data. This is often convenient for adaptive control and/or real-time optimization purposes. A particularly clear introduction to RLS/RLS-FF is found at: Karl J. Åström, Björn Wittenmark, "Computer-Controlled Systems: Theory and Design", Prentice-Hall, 3rd ed., 1997.
Application example ― While investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS algorithm with forgetting factor (RLS-FF) to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer, while giving increased weight to most recent data. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter. The proposed (adaptive) control algorithm compared favourably with PID. Simulations assessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay. This investigation was reported at (MSc Thesis):
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Please find attached plot and explain steps to get these kinds of curves in HFSS or in any other software like Origin Pro
Find below steps to plot in Origin:
1. In workbook, give frequency readings under A(X), ECC under B(Y) and DG under C(Y)
2. Select All, go to Plot Tab, in multi-Panel/Axis, select "Double-Y".
3. Required graph will be plotted, you can then scale the axis as per your requirement.
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Do cellular phones which support LTE have Diversity/MIMO antennas in the 700 MHz bands in addition to the higher bands?
I would like to pay your attention to the pdf found in the link:https://www-file.huawei.com/-/media/corporate/pdf/mbb/2020/4x4-mimo.pdf?la=en
They speak about user equipment having up to 4 antennas. We developed MIMO antennas for the mobile phone:
Best wishes
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Dear researchers,
I am looking forward to simulating a basic MFAC controller on a simple MIMO system. So far, I have been able to implement the MFAC on a SISO system, but even have not yet been able to simulate MIMO plant with good tuning and response. The procedure I have followed is discussed in the book "Model Free Adaptive Control Theory and Application" by Zhongsheng Hou and Shangtai Jin, Chapter 5.
I have added the file I have been working on.
Any help would be appreciated.
Ehsan Badfar The Simulation file has been added in advance. Reference inputs are not tracked at all.
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Hello everyone,
I often meet in scientific articles on MIMO systems the term "rich scattering environment". What exactly is meant by this term?
For example, in the context:
Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins.
Hope you are well!
In the context of MIMO system means that the the radiation changes directions randomly many times such that the signal becomes orthogonal to the originally transmitted signal coming out of the antenna. It is so that the signal paths through the scattering rich medium will cause the signal transmitted through them to become uncorrelated. It then realizes the multipath diversity.
Best wishes
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Hi
I am curious to know which channel model is popular for 5G MIMO cellular network (sub 6GHz)?
I heard about COST2100 being the one of them... I could not find good papers providing parameters for modelling ...can anybody provide the source for the parameter of COST2100..
Thank you.
with regards,
Laxmikant Minz
Good luck
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Which modulation technique is best for the 5G ( MIMO based antenna system) wifi application ? usually OFDM is recommended for this application. Since there is some disadvantages of OFDM.
• OFDM is sensitive to Doppler shift - frequency errors offset the receiver and if not corrected the orthogonality between the carriers is degraded.
• Sensitive to frequency timing issues.
• Possesses a high peak to average power ratio - this requires the use of linear power amplifiers which are less efficient than non-linear ones and this results in higher battery consumption.
• The cyclic prefix used causes a lowering of the overall spectral efficiency.
NOMA has shown many advantages and was extensively studied, but I don't think it is currently standardized for the 5G system.
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Hi,
I am working on a Multiphonic acoustic echo cancellation algorithm. It seems that it is quite complexed and computative for smartphones. I just wanted to know if there are any research papers that implemented low complexity and low computative MIMO or SIMO acoustic echo cancellation algorithm on smartphones?
Regards,
Jaleel S.Jameel Thank you for the link. But my scenario is quite different from a typical AEC. It's more like a surround system acoustic echo canceller in a distributed cluster of Smartphones.
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lots of antennas have been designed on ferrite substrate. but i am finding it difficult to find the MIMO antennas based on ferrite substrate. May any one of you please guide to locate the relevant research please?
Thank you for sharing this question
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For Ns = 1, where Ns is the number of streams per user, the beam steering vectors are calculated by finding the array response vectors corresponding to the largest effective channel gain, i.e, finding the path that maximizes abs(abs(Ar(:,r)'*H*At(:,t))) where Ar and At are functions of the receive and transmit antenna array responses, respectively.
My question is, how to calculate the beam steering matrices for the case in which Ns> 1?
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• CSISO= f(frequency) : plot(f, CSISO) ?
• CMIMO= f(frequency) : plot (f, CMIMO) ?
Implementing this in Matlab is rather simple, but you need to figure out what formulas that you want to implement. First, you need to a channel model for the MIMO cases (for which the SISO case is a special case). Is it a deterministic or random channel? If they are random, what is the distribution. What is the SNR? Next, you need to connect the channel model to the frequency. Basically, you need to figure out the reason for the frequency dependency.
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Power of a transmitted radar signal, s[n] can easily be calculated as : P=(1/N) s[n] ' * s[n]
where s[n] is an N*1 vector and ' denotes hermition.
If S[n] is an L*M matrix of transmitted data, how to calculate its total power?
1. Is it true to use this formula: P= (1/LM)S[n]*S[n]' ?
2. How about to use "trace". if we use "trace", which one is correct?
a- P=trace(S[n]*S[n]')
b- P=1/(LM)*trace(S[n]*S[n]')
what do L and M mean?
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Normally for fair comparison we assume equal transmission power and equal rate. If rate can not be made equal how can be compare two different rate channel codes?
Satheesh Monikandan Balakrishnan , For two different systems I think it is when log2(M_a)*R_a = log2(M_b)*R_b, where M is the modulation order and R is the code rate. Than you will have for both curves an equal horizontal shift between SNR and Eb/N0 of 10log10(log2(M)*R).
In case your question is about when the SNR and Eb/N0 curves will be equal then it is when log2(M)*R = 1.
I hope it help.
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Hello all, I am working on MU-MIMO DL Block Diagonalization Precoding in Multi-Antenna receivers case and comparing it to zero forcing precoding.
1) In case of MU-MIMO Downlink, what is the actual cause of Multi-User interference or Inter-User Interference?
- Is it due to the fact that BS simultaneously transmitting signals to the users!
or
- due to Channel distortion!
I understand that in case of same channel, MUI can occur, but I also read that each transmit antennas at BS as a particular channel towards each receive antennas at the user side. Therefore in this case, how is MUI occuring? Is my understanding of channels between transmit and receive antennas correct?
2) In case of MU-MIMO downlink Zero Forcing Precoding, literature says its disadvantages are
i) inversion of ill-conditioned channel matrix causes noise amplification
How is noise amplification caused in precoding?
(However, In case of ZF Receiving, I understood Noise amplification problem, as h ->0, n/h -> Infinity )
ii) extra power might be required to transmit separate signals to closely spaced antennas of a single user, if channels of these antennas are highly correlated!
So, Block Diagonalization is preferred in multi- antenna recievers, as it only removes MUI and it does not suppress Inter-Antenna Interference as in case of ZF Precoding.
Thank You.
1) The cause is that multiple signals are transmitted at the same time and frequency, but with different spatial directivity. In theory, zero-forcing and block-diagonalization can cancel that interference but in practice, the cancellation will be only partial due to imperfect channel state information.
2) You are right, it won't lead to noise amplification. I have also noticed how this term is misused in the literature. What is meant is that interference suppression between users with very similar channel will lead to a large loss in signal power at the desired receiver. Hence, the SNR becomes bad. For this reason, zero-forcing is only recommended when you have high SNRs and well separated channels. Otherwise, there are regularized zero-forcing methods that perform better.
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Is Massive MIMO different from a pashed array antenna? Or that both names belong to the same antenna or same the structure for the array antenna in 5G ??
Yes, they are different.
A phased array can only perform analog beamforming: You can transmit one signal using a single angular beam, which must be the same for the entire band. It is useful to improve the SNR in line-of-sight scenarios where you only want to serve one user at a time.
Massive MIMO makes use of digital beamforming: You can transmit multiple signals at the same time using different beams, you can change the beams over the frequency domain, and adapt them to multipath fading. This is what 5G is making use of for the moment. It is great for spatially multiplexing many users.
You might be interest in some of the episodes of our podcast: https://ma-mimo.ellintech.se/2020/10/21/new-podcast-wireless-future/
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Hi,
I'm trying to optimize a PI controller for a MIMO system called Wood-Berry (WB). I need to introduce a stable region to algorithm in order to have a reasonable search space. Do you know any paper or publications which has done it before so i can check if i didn't make any mistake?
(I found two stable for g11 and g22 as figure but it's not working some how)
Dear Mr.Lafifi,
I really appreciate your help. I know i should study the papers so can recalculate it, but by chance, do you know any other papers (like first one) that has stable region for "VL (Vinante–Luyben) model?
with best Regards
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Hi all,
Currently, I'm working on my Thesis as applying a Multi-Objective Optimization method on a MIMO system, so error (IAE) of outputs become minimum. Average and tolerance of IAE is chosen as objective function.
First, just main outputs (Y1 and Y2) have been considered to optimizing. But there were some answers which have a bad interaction although their main-outputs seems great. Probably because outputs cancel each other out, so there is not any sign in main-outputs.
Now my question is:
Is it right to consider main (Y1,Y2) and sub-outputs(Y12,Y21) together as optimization objective?
Can i claim that Controller is handling interaction between loops and error of Outputs simultaneously ?
Thank you very much, that helped a lot
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Which frequency band is the most suitable for RIS deployment? Have RIS-aided systems the same advantages for low frequency bands such as 800 MHz/2 GHz and high frequencies such as mmWave/THz bands?
Felipe Augusto Pereira de Figueiredo: There are hardware concepts for all of these ranges. I think it is too early to say where it will be most useful, but here are my speculations: At lower frequencies, there is existing relaying technology that is rather competitive and the propagation conditions are quite good. I think it is at mmWave frequencies and above that the RIS technology can do something new: 1) add extra propagation paths to sparse channels; 2) replace the need for large antenna arrays, since large arrays are complicated to build; 3) conventional relays might not be available.
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I would like to know if there is an expression that shows the (maximum) channel capacity of a downlink multiuser MIMO channel when imperfect CSI is assumed.
Any references in this direction would be useful for me. Thanks!
I can give you a conceptual answer and then you can build on it.
The ergodic channel capacity C is determined by the Shannon theorem such that
C=B log2 ( 1+ r/ N)
B is the bandwidth,
r the recieved signal power at the input of the receiver
N is the noise power
We can express r in terms of the channel gain h and S the sent signal power suh that r=S/h. The channel gain h in case of imperfect estimation can be expressed by h +dh
where dh is the error in determining h
C/B= channel spectral density= log2( 1+ Sl(h+dh)N))
Finally SD= log2( 1+ S(1+dh/h) /hN)
= log2( 1 +r/N ( 1+dh/h),
So the noise will increase by the relative channel error.
One can apply this formula on all MIMO channels and sum sup for all cahnnels.
Best wishes
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Is there any specific design challenges for MIMO antenna in 5G applications?
I think one major challenge is the intercoupling between the adjacent antennas as they are closed packed as possible in order to accommodate as large number of antennas as possible in the smallest possible space.
This problem gets more acute in the receiver.
An other problem is to control the radiation nulls and the the direction of the major loop for antenna arrays.
We tried to treat the problem of coupling in the following paper:
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Intelligent Refelecting Surface (or Intelligent Reconfigurable Surface), IRS, consists of a large number of passive elements that are able to reflect the incident signal passively. The size of each element may depend on the wavelength of the signal to be reflected. My question here is how much should be the size of the IRS element in order to successufly reflect a signal with a wavelength \lambda.
The typical size of the elements being used in experiments is lambda/10 to lambda/5. The reason is that you want each element to scatter the signals without a directivity, so that the combined effect of having many elements will determine the directivity of the "reflected" signal.
I also explain this at 26:00 in the following video:
You can also listen to the Episode 3 in my podcast "Wireless Future", we we discuss this. It is available on YouTube and in podcast apps.
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Hi All,
Ideally speaking, for an array with N elements, the number of nulls that can be placed after adaptive techniques follows No of adaptive nulls = N - 1.
Is this true for a MIMO based array as well? Does N = number of virtual array elements for MIMO based array?
In short, can we conclude that in a MIMO based array, we can place more adaptive nulls in the beam pattern due to more virtual elements (in comparison to physical elements)?
Can someone direct me to relevant literature with this analysis?
Kind Regards
Anum
In any case the Number of adaptive nulls in any MIMO Array can't be more than N-1.
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Geometry Based Stochastic Channel Modelling or Spatial Channel Modeling (SCM) methods such as:
- 3GPP-SCM, SCM-E
- Winner (I and II)
produce channel coefficients as a matrix H (may on path or at time) between transmitter and receiver antennas.
Then H associated with CSI matrix. But CSI of real channel assume several components (CQI - SNR, PMI, RI). Also, it is referred sometimes as a matrix of complex numbers (gain and phase)...
How generated channel coefficients are related with CSI?
How Channel State Information (CSI) could be obtained from matrix generated by Spatial Channel Modeling?
H matrix always includes only the complex channel coefficients between transmitter and receiver antennas. Channel models (spatial, stochastic, or deterministic) try to estimate that matrix.
CSI, on the other hand, is the set of information that the receiver calculates using that real H matrix coefficients. It measures SNR, determines the correct precoder matrix, rank, layer, etc. based on the network's algorithm, and reports to the transmitter with their corresponding indicators in a CSI reporting.
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multiuser MIMO can be implemented by using block diagonalization technqiue
Sorry,
there was 2 typos in my previous post :
1) y = H A s + n with n is a (Nr*Nfft) x 1 vector representing the additive noise.
2) Minimizing the power of the error e = (s - s_hat) so the MSE is :
J = E[ Trace(exe')]
produce the optimum MMSE equalizer :
W_MMSE = inv(Ryy) x H x A
when considering A = I ---> the power loading is P = A^2=I which means that each user is transmitting with the same power on the channel.
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Hi everybody,
I'm doing a project for my BoS thesis in Electronics Engineering.
What I have to do is to control a quadcopter (of which I have the mathematical model, both linearized and non linearized) with a state feedback controller and then add integral action to make it follow a pre-established path.
I managed to out the state feedback scheme in Simulink, found a state feedback matrix with the 'place(A,B,p)' function in MatLab but I don't know which reference I have to give to the system to make it follow a certain trajectory (a circumference for example).
If someone could help me in any way I'll be very grateful.
Thanks a lot in advance and have a nice day.
I should had been more clear, basically there's a problem with your schema, or better, with its meaning. Here's a scheme which doesn't take into account linearization, while yours *must* do it.
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The exact phrase: The N-bit overhead of CSI feedback in FDD Mu-Massive MIMO
I know that FDD is used for predetermined grid of beams with user equipments reporting their preferred beam. So, user equipments return the code of predetermined grid, but this is only one code... (or it send the whole matrix of predetermined grid?)
May be this phrase incorrect?
If you transmit a grid of beams in the downlink, then the user can measure the resulting SNR for each one of them. A codebook is then used for the user to indicate which beam it prefers. It doesn't have to be one of the beams that was transmitted, but could be a linear combination of multiple beams (since the chance is small that the channel is perfectly aligned with just one of the predetermined beams).
If N-bit feedback is used, then a codebook with 2^N entries can be used to inform the base station of the preferred beam.
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I think the microwave software studio, abbreviated CST.
We used it to design MIMO for LTE mobile communication system.
Best wishes
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Optimization theory in communication is a promising area. Please suggest some articles, that focus exclusively on optimization, problem formulation and solving it. Feel free to share good tutorials too.
Please suggest articles in related areas only.
Thank you
Dear;
The following documents and links give details :
Algorithms for Convex Optimization
Regards.
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i am calculating the SNR for a SISO channel matrix H as below:
snr_linear = (tx_power .*(abs(h0).^2));
how do i change it for MIMO scenario? now that my H matrix is 2x2 MIMO channel?
The answer depends on what you mean with SNR in the MIMO scenario. One can compute the SNR from one transmit antenna to one receive antenna in exactly the same way.
You can also consider the SNR of the MIMO system for a particular transmission method. You select a precoding vector at the transmitter and a combining vector at the receiver. This will effectively turn the channel into an SISO channel and you can now compute the SNR.
By the way, you need to divide by the noise power as well. I recommend my video about SNR computations: https://youtu.be/OA4viERrlzA
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I am little bit confused in reading the curve , outage probability (CDF) versus capacity. kindly explain.
Which curve are you referring to?
The outage probability is the probability that the rate that we have selected happens to be below the capacity, because we had to pick the rate without knowing the capacity. A larger rate leads to larger outage probability.
Here is a movie where I explain this:
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In MIMO optical wireless, locating all photodiodes (PDs) in one side of the smartphone or user equipment may lead to high correlation between the elements of channel gain matrix. This will cause ill-condition channel matrix and high BER. Hence, the aim is to break this symmetry. Multi-directional Receiver (MDR) is one practical solution that has been investigated in the following paper and compared with the traditional method of locating all PDs on one side of the user's device.
I would suggest to utilize the angle diversity receiver (ADR) with narrow field of view angles. This will have many benefits either in enhancing the channel correlation r reducing both ISI and IUI.
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I wonder if any of the colleagues are conducting research on MIMO or Massive MIMO using Altair's "winprop" software. If so, let us open a dialog and share our experience in using this tool.
Best regards
Hello Sir,
Regards
Anil Sharma
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performance of video streaming over mmwave using NS3, what are the algorithms
Reliable Video Streaming over mmWave with Multi Connectivity and Network Coding
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Hi all, In case of designing a 4 port MIMO antenna, while performing the simulation, is it necessary to check all the source magnitudes to 1, or is it ok, if I go with default settings where only 1 source is assigned magnitude 1 and rest all 0. which option is correct ? Does it have any effect on S parameters (reflection and isolation).
Second question, what if i need to calculate total gain of my MIMO antenna. For observing farfield radiation pattern, then should i assign magnitude as 1 for all the 4 sources? Or else just proceed with the default settings.
You need to understand the difference between an antenna array and MIMO antenna setup. In MIMO simulations you suppress mutual coupling which is correctly identified when only one antenna is excited and its effects is observed on the other antenna. For MIMO performance parameters you look for ECC in particular to check the level of mutual coupling. Secondly you also look for TARC which caters for the effects of all active antennas at same time. Since MIMO setup work in a different way you normally look for MEG of MIMO setup. For a multipath fading environment MEG represents the ratio of the power received by the MIMO antenna to the power received by an isotropic antenna thus you include radiating elements efficiencies along with S param to calculate MEG. Also DG is an important parameter which is not there for an array setup.
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Is it necessary to have continuous ground plane for 2 patches in 2 element MIMO antenna? Can I use two separate ground planes for two patches?
Dear Punna,
To build an array one uses basically separate antennas. In case of patch antennas the ground plane can be separted or continuous. The main issue is how to feed these antennas. Normally they are fed to achieve a specific phase shift between them. This is determined by the type of the MIMO.
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Good morning. Now i am designing MIMO antenna for Ultraband Applications. Can anyone suggest how to plot ECC (Envelope correlation Coefficient) Using HFSS 15 software.
Hi Dear
You can calculate the ECC following these steps, to plot ECC vs Freq plot in HFSS calculated from S parameters:
In the project manager window right click on results and then select 'Output Variables'.
Provide Name to the parameter and then insert the expression: (mag(conjg(S(1,1))*S(1,2)+conjg(S(2,1))*S(2,2)))^2/((1-mag(S(1,1))^2-mag(S(2,1))^2)*(1-mag(S(2,2))^2-mag(S(1,2))^2))
Click on Add and then click on Done.
Then again right click on results --> Create Modal Solution Data Report --> Rectangular Plot --> Under Category option select 'Output Variables' --> Select Name of the parameter that you have given in 2nd step.
after you add the DG with this expression:
10*(sqrt(1-ECC^2))
the same for TARC
best regards
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Hello everyone ,
i am designing a multi band MIMO antenna . Can anyone suggest how to plot Total Active Reflection Coefficient (TARC) and Diversity Gain (DG) Using HFSS 15 software.
Hi Dear Asif
first, you need to calculate the ECC following these steps to plot ECC vs Freq plot in HFSS calculated from S parameters:
In the project manager window right click on results and then select 'Output Variables'.
Provide Name to the parameter and then insert the expression:  (mag(conjg(S(1,1))*S(1,2)+conjg(S(2,1))*S(2,2)))^2/((1-mag(S(1,1))^2-mag(S(2,1))^2)*(1-mag(S(2,2))^2-mag(S(1,2))^2))
Click on Add and then click on Done.
Then again right click on results --> Create Modal Solution Data Report --> Rectangular Plot --> Under Category option select 'Output Variables' --> Select Name of the parameter that you have given in 2nd step.
after you add the DG with this expression:
10*(sqrt(1-ECC^2))
the same for TARC
best regards
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