I am wondering if the denominator@15:00 is really the same for every user. If I understood correctly, k' to K is the set of all users except the one whose SINR is being evaluated. In that case, the value of this expression is different for every user, although its notation might look the same due to the way it is shown here. I am wondering actually what don't we use the typical way of having i=1,K in the outer some and then k=1,K&K~=i in the inner sum in the denominator. Thanks for taking the time to answer the question btw! Really appreciate it.
What you are noticing here is that the interference term for user k contains interference from the user itself. This wouldn’t be the case in a situation with perfect channel knowledge, but the formulas that are used in this video are taking estimation errors into account. This leads to a kind of self-interference where the imperfect channel knowledge leads to an uncertainty in the detection so that each user is (slightly) interfering with itself. I believe that this is a phenomenon that one should expect in practice as well. However, the weak spot with the methods used in this video is that they rely strongly on the i.i.d. Rayleigh fading assumption that was made in previous videos. For other channel models, each user will also be subject to interference from other users, but the value of the denominator will nevertheless be (somewhat) different between the users.
There are many different formulas depending on what you want to achieve or measure. Lecture 2 in this series describe the basic formulas, so maybe it is what you are looking for. You can also check out Chapter 1 in “Massive MIMO networks”, which you can download from massivemimobook.com
May I ask 1-we have @15:40 a single constraint related to the etas then in @17:80 I see we have 2 different constraints one for X and the other for sum over X How was those derived ?
The constraint at @15:40 is an assumption. The additional constraint on Slice 7 is obtained from the lower left equation related to s at Slide 6. We introduce a new variable s that must satisfy the equality from Slide 6.
@@WirelessFuture thanks I got the X inequality in slide 7 but what about the sum over X inequality ? How did it becomes 1-s ?any hints I know that sum over xk is sum over etak times bk times s , how do we move from there to 1-s ?
@@yasserothman4023 You rearrange the equation in the lower left corner of Slide 6. Insert b_k \eta_k = x_k /s into the expression, and you will get s = 1/(1+ \sum_k x_k /s), which can be rearranged into \sum_k x_k = 1-s.
Respected Sir, Thank you very much for the lecture.So many things understood.Great explanation of the concept sir.
Thank you so much 🌷..
I would like to help me in how can I do simulation IRS because it is my graduation project , and I will be thankful forever..
You can find several code examples on IRS/RIS on this GitHub page: github.com/emilbjornson
@@WirelessFuture thank u so much ,sir
I am wondering if the denominator@15:00 is really the same for every user. If I understood correctly, k' to K is the set of all users except the one whose SINR is being evaluated. In that case, the value of this expression is different for every user, although its notation might look the same due to the way it is shown here. I am wondering actually what don't we use the typical way of having i=1,K in the outer some and then k=1,K&K~=i in the inner sum in the denominator.
Thanks for taking the time to answer the question btw! Really appreciate it.
What you are noticing here is that the interference term for user k contains interference from the user itself. This wouldn’t be the case in a situation with perfect channel knowledge, but the formulas that are used in this video are taking estimation errors into account. This leads to a kind of self-interference where the imperfect channel knowledge leads to an uncertainty in the detection so that each user is (slightly) interfering with itself. I believe that this is a phenomenon that one should expect in practice as well. However, the weak spot with the methods used in this video is that they rely strongly on the i.i.d. Rayleigh fading assumption that was made in previous videos. For other channel models, each user will also be subject to interference from other users, but the value of the denominator will nevertheless be (somewhat) different between the users.
Hi sir,I want to know to the formula for MIMO beamforming..Please suggest me the paper and formula.
There are many different formulas depending on what you want to achieve or measure. Lecture 2 in this series describe the basic formulas, so maybe it is what you are looking for. You can also check out Chapter 1 in “Massive MIMO networks”, which you can download from massivemimobook.com
@@WirelessFuture Thank you so much sir. I want to know, how can I used mimo antenna for Thz.. Please guide me sir.
May I ask
1-we have @15:40 a single constraint related to the etas then in @17:80 I see we have 2 different constraints one for X and the other for sum over X
How was those derived ?
The constraint at @15:40 is an assumption. The additional constraint on Slice 7 is obtained from the lower left equation related to s at Slide 6. We introduce a new variable s that must satisfy the equality from Slide 6.
@@WirelessFuture thanks I got the X inequality in slide 7 but what about the sum over X inequality ? How did it becomes 1-s ?any hints
I know that sum over xk is sum over etak times bk times s , how do we move from there to 1-s ?
@@yasserothman4023 You rearrange the equation in the lower left corner of Slide 6. Insert b_k \eta_k = x_k /s into the expression, and you will get s = 1/(1+ \sum_k x_k /s), which can be rearranged into \sum_k x_k = 1-s.
Respected Sir, Which paper to cite ? for expression of SIMO, MIMO expalined at the end of video.
Thnak you for the lecture sir! can you help me with your code files?
There are no code files related to this lecture. You can find the lecturer’s code files at GitHub.com/emilbjornson