Questions related to MIMO
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?
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?
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.
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?
How to Plot ECC ,CCL, MEG, TARC, DG curves for MIMO antenna having more than two ports using HFSS ?
thank you in advance
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.
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.
Thanks In advance.
Looking for alternative MIMO based automotive radars other than TI make with low cost and yet provide good performance.
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.
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.
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
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.
Is there MATLAB code to generate MIMO overlapping triangular membership functions with left and right shoulders?
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'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?
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.
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?
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.
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.
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?
Kindly support and advise
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?
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.
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.
How to Plot ECC ,CCLcurves for MIMO antenna having more than two ports in CST? For example in case of a 8x8 MIMO antenna ?
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.
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.
link to article :
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?
Please find attached plot and explain steps to get these kinds of curves in HFSS or in any other software like Origin Pro
Do cellular phones which support LTE have Diversity/MIMO antennas in the 700 MHz bands in addition to the higher bands?
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.
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.
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..
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.
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?
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?
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?
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.
But how about power calculation for a MIMO radar?
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?
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?
Hello all, I am working on MU-MIMO DL Block Diagonalization Precoding in Multi-Antenna receivers case and comparing it to zero forcing precoding.
I have following questions, could you please help in understanding.
1) In case of MU-MIMO Downlink, what is the actual cause of Multi-User interference or Inter-User Interference?
- Is it due to the fact that BS simultaneously transmitting signals to the users!
- due to Channel distortion!
I understand that in case of same channel, MUI can occur, but I also read that each transmit antennas at BS as a particular channel towards each receive antennas at the user side. Therefore in this case, how is MUI occuring? Is my understanding of channels between transmit and receive antennas correct?
2) In case of MU-MIMO downlink Zero Forcing Precoding, literature says its disadvantages are
i) inversion of ill-conditioned channel matrix causes noise amplification
How is noise amplification caused in precoding?
(However, In case of ZF Receiving, I understood Noise amplification problem, as h ->0, n/h -> Infinity )
ii) extra power might be required to transmit separate signals to closely spaced antennas of a single user, if channels of these antennas are highly correlated!
So, Block Diagonalization is preferred in multi- antenna recievers, as it only removes MUI and it does not suppress Inter-Antenna Interference as in case of ZF Precoding.
Could you please help in understanding disadvantages of ZF Precoding in MU-MIMO DL multi-antenna recievers case.
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)
Thank you for your helping
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 ?
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?
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!
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.
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?
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?
Where can I read about it?
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.
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?
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.
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?
I am little bit confused in reading the curve , outage probability (CDF) versus capacity. kindly explain.
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.
Please let me know your opinion on this paper.
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.
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.
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?
I am looking for a MATLAB code to implement channel prediction or channel state information (CSI) using a Kalman filter-based approach. Specifically, for a MIMO environment.