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The BEAST for maximum-likelihood detection in non-coherent MIMO wireless systems

Hug, Florian LU and Rusek, Fredrik LU (2010) IEEE International Conference on Communications, ICC 2010 In [Host publication title missing]
Abstract
Next generation wireless systems have to be able to efficiently deal with fast fading environments in order to achieve high spectral efficiency. Using multiple-input multiple-output (MIMO) systems and exploiting receive diversity, their spectral efficiency can be greatly increased. Commonly, the channel is estimated via training symbols, before the data detection is carried out based on the previously obtained channel estimate. While this significantly simplifies the process of data detection, it leads in general to suboptimal results. Thereby, a better approach is given by carrying out joint maximum-likelihood (ML) channel estimation and data detection.



In this paper, the BEAST — Bidirectional Efficient Algorithm for... (More)
Next generation wireless systems have to be able to efficiently deal with fast fading environments in order to achieve high spectral efficiency. Using multiple-input multiple-output (MIMO) systems and exploiting receive diversity, their spectral efficiency can be greatly increased. Commonly, the channel is estimated via training symbols, before the data detection is carried out based on the previously obtained channel estimate. While this significantly simplifies the process of data detection, it leads in general to suboptimal results. Thereby, a better approach is given by carrying out joint maximum-likelihood (ML) channel estimation and data detection.



In this paper, the BEAST — Bidirectional Efficient Algorithm for Searching code Trees — is proposed as an alternative algo- rithm for joint ML channel estimation and signal detection and its complexity is compared with recently published algorithms in this field. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
conference name
IEEE International Conference on Communications, ICC 2010
external identifiers
  • WOS:000290335604014
  • Scopus:77955371417
DOI
10.1109/ICC.2010.5501872
language
English
LU publication?
yes
id
7a65538e-da8a-48ad-9e17-c69deac0d751 (old id 1523021)
date added to LUP
2010-01-04 11:55:09
date last changed
2016-10-13 04:57:38
@misc{7a65538e-da8a-48ad-9e17-c69deac0d751,
  abstract     = {Next generation wireless systems have to be able to efficiently deal with fast fading environments in order to achieve high spectral efficiency. Using multiple-input multiple-output (MIMO) systems and exploiting receive diversity, their spectral efficiency can be greatly increased. Commonly, the channel is estimated via training symbols, before the data detection is carried out based on the previously obtained channel estimate. While this significantly simplifies the process of data detection, it leads in general to suboptimal results. Thereby, a better approach is given by carrying out joint maximum-likelihood (ML) channel estimation and data detection.<br/><br>
<br/><br>
In this paper, the BEAST — Bidirectional Efficient Algorithm for Searching code Trees — is proposed as an alternative algo- rithm for joint ML channel estimation and signal detection and its complexity is compared with recently published algorithms in this field.},
  author       = {Hug, Florian and Rusek, Fredrik},
  language     = {eng},
  series       = {[Host publication title missing]},
  title        = {The BEAST for maximum-likelihood detection in non-coherent MIMO wireless systems},
  url          = {http://dx.doi.org/10.1109/ICC.2010.5501872},
  year         = {2010},
}