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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 In Proc. ChinaCom 2007
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:
author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
channel modeling, multipath cluster, MIMO
in
Proc. ChinaCom 2007
conference name
ChinaCom2007
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
2008-01-31 10:35:05
date last changed
2016-10-13 04:53:03
@misc{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},
  keyword      = {channel modeling,multipath cluster,MIMO},
  language     = {eng},
  series       = {Proc. ChinaCom 2007},
  title        = {Tracking time-variant cluster parameters in MIMO channel measurements},
  url          = {http://dx.doi.org/10.1109/CHINACOM.2007.4469589},
  year         = {2007},
}