Tnx sir!! I found it useful.

]]>No particular paper comes to my mind, but here is a related solution for the case when the users have different SNRs:

https://arxiv.org/pdf/1709.07722.pdf

In some of the plots, we are showing the average SNR on the horizontal axis. When we are changing this SNR we are really just changing the power.

]]>The points you mentioned are right that’s why I am confused with those papers considerations. I will use transmit power for spectral efficiency plots since the received SNR depends on the user. But it is good if I have your works that you used Tx power for spectral efficiency plots. Could you help me how can I access the papers? ]]>

I’m not sure what paper you are referring to but I agree that the transmit power is not equivalent to the SNR in those cases. What I probably have done in some papers is to plot how the spectral efficiency varies when the transmit power varies. Every value of the transmit power will then lead to a different SNR for every user, but all of them will increase as we increase the transmit power and this can be used to show basic behaviors.

]]>I am not aware of any Matlab code that is carrying out this, but I recommend you to have a look at the Quadriga model: https://quadriga-channel-model.de

We use it in Section 7.7 of the book “Massive MIMO networks” http://massivemimobook.com to carry out simulations.

]]>Thank you ]]>

We didn’t select the spatial correlation models in that paper based on real-world modeling, but in order to cover different properties:

1. Spatially uncorrelated

2. Spatially correlated but all eigenvalues are non-zero

3. Spatially correlated and some eigenvalues are zero or close to zero (spatial sparsity).

The reason was to demonstrate that category 2 is sufficient to prove the unlimited capacity (i.e., linearly independent correlation matrices), while some previous works have required category 3 to show somewhat similar results (i.e., spatially orthogonal matrices). Our main claim is that our result turns the unlimited capacity result from being a special case, to actually being the normal case.

Since you ask about real-world models, let me say that 3GPP is normally considering models that resemble the “local scattering model” from Chapter 2 of my book Massive MIMO networks, but with six clusters instead of one. We use such a model in the paper “Massive MIMO with Spatially Correlated Rician Fading Channels” (https://arxiv.org/pdf/1805.07972.pdf)

]]>Thanks and kind regards,

Felipe

]]>Addition in decibel scale corresponds to multiplication in the power domain.

7 dBi means that you get a ~5 times stronger signal transmitted in the main direction from the antennas, as compared to an isotropic antenna that radiates the signal equally strongly in all directions. When using 8 such antennas, you get 5*8 = 40 times stronger signal than with one isotropic antenna. If you covert 40 to dB-scale, you get 10*log10(40) = 16.

]]>7dBi is gain of one antenna so for 8 antenna why it is not 7dBi *8=56 dBi

why we are adding 10*log10(8), what it indicate? ]]>

7 dBi + 10*log10(8) = 16 dBi

This is the beamforming gain that you can get if the signals from all 8 radiating elements are received in phase (constructive interference). This only happens if you are lucky and stand on the right place in the cell. By turning each radiating element into an antenna, you can control the phase of the transmitted signal and achieve the beamforming gain wherever the user is!

]]>1 antenna, 8 radiation elements (7 dBi each), 1 transceiver chain, will give 16 dBi gain.

How? Kindly explain sir.

]]>Zero-forcing is one of the many processing schemes that are considered in the book Massive MIMO Networks. You can download the simulation code at https://massivemimobook.com

]]>I don’t have a chart and I’m not sure what you had in mind for that chart either, but if you want to learn about new research directions for Massive MIMO in a less technical way, I recommend you to read this paper:

Emil Björnson, Luca Sanguinetti, Henk Wymeersch, Jakob Hoydis, Thomas L. Marzetta, “Massive MIMO is a Reality – What is Next? Five Promising Research Directions for Antenna Arrays,” Digital Signal Processing, Submitted for review. https://arxiv.org/pdf/1902.07678

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