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Multiuser Processing for Ray-Based Channels

Miller, Chelsea; Dmochowski, Pawel A; Smith, Peter J; Tataria, Harsh LU and Matthaiou, Michail (2018) IEEE International Conference on Communications (ICC) 2019
Abstract
The performance of linear multi-user multiple-input multiple-output (MU-MIMO) systems has been extensively studied for classical statistical channel models. In contrast, there is little analysis for ray-based models, which are physically motivated, feature prominently in standards and have been experimentally validated. Thus, we present a novel analysis framework for zero forcing (ZF) and maximal ratio combining (MRC) applicable to such models. Specifically, using a central result for averaging in the angular domain, we derive accurate expressions for ZF signal-to-noise ratio and MRC signal, interference and noise powers. The remarkably simple expressions offer the following insights into the effects of the propagation environment. While... (More)
The performance of linear multi-user multiple-input multiple-output (MU-MIMO) systems has been extensively studied for classical statistical channel models. In contrast, there is little analysis for ray-based models, which are physically motivated, feature prominently in standards and have been experimentally validated. Thus, we present a novel analysis framework for zero forcing (ZF) and maximal ratio combining (MRC) applicable to such models. Specifically, using a central result for averaging in the angular domain, we derive accurate expressions for ZF signal-to-noise ratio and MRC signal, interference and noise powers. The remarkably simple expressions offer the following insights into the effects of the propagation environment. While ZF is robust to parameters such as cluster and subray angle spreads, MRC interference is highly sensitive to them. We show that the performance scales linearly with the number of antennas, and that it degrades with narrow angle spreads and as the propagation moves toward the antenna end-fire. Finally, by evaluating the variance of the MRC interference, we observe that an approximation to the MRC SINR widely used for classical statistical models, is inaccurate in ray-based channels. (Less)
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author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
submitted
subject
keywords
Ray-based channel models, Massive MIMO, Zero-forcing combining, Maximum-ratio combining, Statistical analysis
host publication
IEEE International Conference on Communications (ICC) 2019
pages
7 pages
conference name
IEEE International Conference on Communications (ICC) 2019
conference location
Shaghai, China
conference dates
2019-05-20 - 2019-05-24
language
English
LU publication?
no
id
310daca2-68ae-46b5-85dc-9a2da33539a1
date added to LUP
2018-11-27 19:27:25
date last changed
2018-11-29 13:17:57
@inproceedings{310daca2-68ae-46b5-85dc-9a2da33539a1,
  abstract     = {The performance of linear multi-user multiple-input multiple-output (MU-MIMO) systems has been extensively studied for classical statistical channel models. In contrast, there is little analysis for ray-based models, which are physically motivated, feature prominently in standards and have been experimentally validated. Thus, we present a novel analysis framework for zero forcing (ZF) and maximal ratio combining (MRC) applicable to such models. Specifically, using a central result for averaging in the angular domain, we derive accurate expressions for ZF signal-to-noise ratio and MRC signal, interference and noise powers. The remarkably simple expressions offer the following insights into the effects of the propagation environment. While ZF is robust to parameters such as cluster and subray angle spreads, MRC interference is highly sensitive to them. We show that the performance scales linearly with the number of antennas, and that it degrades with narrow angle spreads and as the propagation moves toward the antenna end-fire. Finally, by evaluating the variance of the MRC interference, we observe that an approximation to the MRC SINR widely used for classical statistical models, is inaccurate in ray-based channels. },
  author       = {Miller, Chelsea and Dmochowski, Pawel A and Smith, Peter J and Tataria, Harsh and Matthaiou, Michail},
  keyword      = {Ray-based channel models,Massive MIMO,Zero-forcing combining,Maximum-ratio combining,Statistical analysis},
  language     = {eng},
  location     = {Shaghai, China},
  month        = {10},
  pages        = {7},
  title        = {Multiuser Processing for Ray-Based Channels},
  year         = {2018},
}