The BEAST for maximum-likelihood detection in non-coherent MIMO wireless systems
(2010) IEEE International Conference on Communications, ICC 2010- 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:
https://lup.lub.lu.se/record/1523021
- author
- Hug, Florian LU and Rusek, Fredrik LU
- organization
- publishing date
- 2010
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- conference name
- IEEE International Conference on Communications, ICC 2010
- conference location
- Cape Town, South Africa
- conference dates
- 2010-05-23 - 2010-05-27
- 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
- 2016-04-04 13:47:37
- date last changed
- 2022-01-30 00:56:32
@inproceedings{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}}, booktitle = {{[Host publication title missing]}}, language = {{eng}}, title = {{The BEAST for maximum-likelihood detection in non-coherent MIMO wireless systems}}, url = {{https://lup.lub.lu.se/search/files/6206342/1613917.pdf}}, doi = {{10.1109/ICC.2010.5501872}}, year = {{2010}}, }