A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels
Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general...
Saved in:
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/107228 http://hdl.handle.net/10220/16659 http://dx.doi.org/10.1109/TIT.2012.2218571 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-107228 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1072282019-12-06T22:27:06Z A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels Wen, Chao-Kai Pan, Guangming Wong, Kai-Kit Guo, Meihui Chen, Jung-Chieh School of Physical and Mathematical Sciences DRNTU::Science::Mathematics Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general type of fading distributions have to be included. The mutual information expression can be expressed as functionals of the Stieltjes transform through the so-called Shannon transform. Ideally, if the Stieltjes transform is known in the context of the large-dimensional RMT, then the problem is solved. However, it is difficult to derive the Stieltjes transform of the considered channel models directly, especially when the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. To overcome this, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large-dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels. As an application, we determine the capacity-achieving input covariance matrices for the MIMO-MACs and prove that the capacity-achieving input covariance matrices are asymptotically independent of the fading distribution. 2013-10-21T07:08:08Z 2019-12-06T22:27:06Z 2013-10-21T07:08:08Z 2019-12-06T22:27:06Z 2012 2012 Journal Article Wen, C.-K., Pan, G., Wong, K.-K., Guo, M., & Chen, J.-C. (2013). A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels. IEEE Transactions on Information Theory, 59(1), 329-352. https://hdl.handle.net/10356/107228 http://hdl.handle.net/10220/16659 http://dx.doi.org/10.1109/TIT.2012.2218571 en IEEE Transactions on Information Theory |
institution |
Nanyang Technological University |
building |
NTU Library |
country |
Singapore |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Science::Mathematics |
spellingShingle |
DRNTU::Science::Mathematics Wen, Chao-Kai Pan, Guangming Wong, Kai-Kit Guo, Meihui Chen, Jung-Chieh A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels |
description |
Using large-dimensional random matrix theory (RMT), we conduct mutual information analysis of a multiple-input multiple-output (MIMO) multiple access channel (MAC). Our channel model reflects the characteristics in small-cell networks where antenna correlations, line-of-sight components, and general type of fading distributions have to be included. The mutual information expression can be expressed as functionals of the Stieltjes transform through the so-called Shannon transform. Ideally, if the Stieltjes transform is known in the context of the large-dimensional RMT, then the problem is solved. However, it is difficult to derive the Stieltjes transform of the considered channel models directly, especially when the transmit correlation matrices are generally nonnegative definite and the channel entries are non-Gaussian. To overcome this, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or non-Gaussian independent entries coincide in the large-dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for non-Gaussian MIMO channels from the known results developed for Gaussian MIMO channels. As an application, we determine the capacity-achieving input covariance matrices for the MIMO-MACs and prove that the capacity-achieving input covariance matrices are asymptotically independent of the fading distribution. |
author2 |
School of Physical and Mathematical Sciences |
author_facet |
School of Physical and Mathematical Sciences Wen, Chao-Kai Pan, Guangming Wong, Kai-Kit Guo, Meihui Chen, Jung-Chieh |
format |
Article |
author |
Wen, Chao-Kai Pan, Guangming Wong, Kai-Kit Guo, Meihui Chen, Jung-Chieh |
author_sort |
Wen, Chao-Kai |
title |
A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels |
title_short |
A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels |
title_full |
A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels |
title_fullStr |
A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels |
title_full_unstemmed |
A deterministic equivalent for the analysis of non-gaussian correlated MIMO multiple access channels |
title_sort |
deterministic equivalent for the analysis of non-gaussian correlated mimo multiple access channels |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/107228 http://hdl.handle.net/10220/16659 http://dx.doi.org/10.1109/TIT.2012.2218571 |
_version_ |
1681034142152654848 |