Tracking time-variant cluster parameters in MIMO channel measurements
Czink, Nicolai ; Tian, Ruiyuan LU ; Wyne, Shurjeel LU ; Tufvesson, Fredrik LU
; Nuutinen, Jukka-Pekka
; Ylitalo, Juha
; Bonek, Ernst
and Molisch, Andreas
LU
(2007)
ChinaCom2007
- Abstract
- This paper presents a joint clustering-and-tracking
framework to identify time-variant cluster parameters for
geometry-based stochastic MIMO channel models.
The method uses a Kalman filter for tracking and predicting
cluster positions, a novel consistent initial guess procedure that accounts for predicted cluster centroids, and the well-known KPowerMeans algorithm for cluster identification. We tested the framework by applying it to two different sets of MIMO channel measurement data, indoor measurements conducted at 2.55 GHz and outdoor measurements at 300 MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the... (More) - This paper presents a joint clustering-and-tracking
framework to identify time-variant cluster parameters for
geometry-based stochastic MIMO channel models.
The method uses a Kalman filter for tracking and predicting
cluster positions, a novel consistent initial guess procedure that accounts for predicted cluster centroids, and the well-known KPowerMeans algorithm for cluster identification. We tested the framework by applying it to two different sets of MIMO channel measurement data, indoor measurements conducted at 2.55 GHz and outdoor measurements at 300 MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the measured scenarios. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/961347
- author
- Czink, Nicolai
; Tian, Ruiyuan
LU
; Wyne, Shurjeel
LU
; Tufvesson, Fredrik
LU
; Nuutinen, Jukka-Pekka
; Ylitalo, Juha
; Bonek, Ernst
and Molisch, Andreas
LU
- publishing date
- 2007
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- channel modeling, multipath cluster, MIMO
- host publication
- Proc. ChinaCom 2007
- conference name
- ChinaCom2007
- conference location
- Shanghai, China
- conference dates
- 0001-01-02
- external identifiers
-
- scopus:43949132338
- DOI
- 10.1109/CHINACOM.2007.4469589
- language
- English
- LU publication?
- no
- id
- 2d11ce83-8b74-49cc-b82d-e0258155e6dd (old id 961347)
- alternative location
- http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04469589
- date added to LUP
- 2016-04-04 13:06:17
- date last changed
- 2022-02-28 21:44:17
@inproceedings{2d11ce83-8b74-49cc-b82d-e0258155e6dd,
abstract = {{This paper presents a joint clustering-and-tracking<br/><br>
framework to identify time-variant cluster parameters for<br/><br>
geometry-based stochastic MIMO channel models.<br/><br>
The method uses a Kalman filter for tracking and predicting<br/><br>
cluster positions, a novel consistent initial guess procedure that accounts for predicted cluster centroids, and the well-known KPowerMeans algorithm for cluster identification. We tested the framework by applying it to two different sets of MIMO channel measurement data, indoor measurements conducted at 2.55 GHz and outdoor measurements at 300 MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the measured scenarios.}},
author = {{Czink, Nicolai and Tian, Ruiyuan and Wyne, Shurjeel and Tufvesson, Fredrik and Nuutinen, Jukka-Pekka and Ylitalo, Juha and Bonek, Ernst and Molisch, Andreas}},
booktitle = {{Proc. ChinaCom 2007}},
keywords = {{channel modeling; multipath cluster; MIMO}},
language = {{eng}},
title = {{Tracking time-variant cluster parameters in MIMO channel measurements}},
url = {{http://dx.doi.org/10.1109/CHINACOM.2007.4469589}},
doi = {{10.1109/CHINACOM.2007.4469589}},
year = {{2007}},
}