Thank you professor Björnson for this very informative presentation. I worked in radar signal processing, mainly direction finding and model order estimation, and I it doesn't make sense to me that we rely on the eigenvalues of the data covariance matrix, R, to tell something about the correlation of the channel (the eivenvalues tell us if there is signal or not and how many are there). Unless the columns of the array response matrix (aka array steering matrix) or the dominant eigenvectors are orthogonal, the channel can not be uncorrelated (i.e. linear independence is not enough to claim uncorrelatedness). The reason for this is that the spatial/angular distance between the multipath components received by the array of the base-station is a key factor: the larger the angular distance, the smaller the correlation and vice versa. The only way to guarantee complete uncorrelation is when the angles, from which these multipath components are received, are designed in someway that makes the corresponding eigenvectors orthogonal, and this happens with probability close to zero in realty. I could be missing something.
The matrix R is not the data covariance matrix but the spatial correlation/covariance matrix of the channel vector. It represents the distribution of the multiple path components. You can read more about this in Section 2 of our book which is available for download here: massivemimobook.com
SumitChakravarty The link is to the summer school and not to the slides. The slides are not available online since they are outdated, but you can find a new version of the slides at massivemimobook.com
I have a question, is it correct that the element wise-MMSE channel Estimation has the same performance with matrix MMSE Estimation in case of uncorrelated Rayleight fading channel?
Thanks, sir, for all the videos and blogs. Sir, what is the meaning of average sum Spectral efficiency for the DL, in the book Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency. sum spectral efficiency means summing the SE of all the UEs in a cell but what is the meaning of average here.
Thank you for reading our book! The word “average” refers to that we are dropping users at random locations, compute their sum SE, and repeat it over and over again. We then compute the average to get a measure that doesn’t depend on the specific user locations in one drop but the average that we can expect to see over many drops.
Hello, I am Hieu, from Vietnam, I am purchasing PhD degree and I am very interested in massive MIMO topic. I found this video is very helpful. Could I get the power point presentation material in this video? It'll be very helpful for me. Thank you very much!!
Thank you professor Björnson for this very informative presentation.
I worked in radar signal processing, mainly direction finding and model order estimation, and I it doesn't make sense to me that we rely on the eigenvalues of the data covariance matrix, R, to tell something about the correlation of the channel (the eivenvalues tell us if there is signal or not and how many are there). Unless the columns of the array response matrix (aka array steering matrix) or the dominant eigenvectors are orthogonal, the channel can not be uncorrelated (i.e. linear independence is not enough to claim uncorrelatedness). The reason for this is that the spatial/angular distance between the multipath components received by the array of the base-station is a key factor: the larger the angular distance, the smaller the correlation and vice versa. The only way to guarantee complete uncorrelation is when the angles, from which these multipath components are received, are designed in someway that makes the corresponding eigenvectors orthogonal, and this happens with probability close to zero in realty. I could be missing something.
The matrix R is not the data covariance matrix but the spatial correlation/covariance matrix of the channel vector. It represents the distribution of the multiple path components. You can read more about this in Section 2 of our book which is available for download here: massivemimobook.com
HI, can you provide access to the slides, please? the link is not working
SumitChakravarty The link is to the summer school and not to the slides. The slides are not available online since they are outdated, but you can find a new version of the slides at massivemimobook.com
I have a question, is it correct that the element wise-MMSE channel Estimation has the same performance with matrix MMSE Estimation in case of uncorrelated Rayleight fading channel?
Yes, they are equivalent in that special case.
Thanks, sir, for all the videos and blogs.
Sir, what is the meaning of average sum Spectral efficiency for the DL, in the book
Massive MIMO Networks:
Spectral, Energy, and Hardware Efficiency. sum spectral efficiency means summing the SE of all the UEs in a cell but what is the meaning of average here.
Thank you for reading our book! The word “average” refers to that we are dropping users at random locations, compute their sum SE, and repeat it over and over again. We then compute the average to get a measure that doesn’t depend on the specific user locations in one drop but the average that we can expect to see over many drops.
@@WirelessFuture Thanks, sir. Great explanation. I understood now.
Hello,
I am Hieu, from Vietnam, I am purchasing PhD degree and I am very interested in massive MIMO topic. I found this video is very helpful. Could I get the power point presentation material in this video? It'll be very helpful for me. Thank you very much!!