Tracking time-variant cluster parameters in MIMO channel measurements
Author
Summary, in English
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.
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.
Publishing year
2007
Language
English
Publication/Series
Proc. ChinaCom 2007
Links
Document type
Conference paper
Topic
- Electrical Engineering, Electronic Engineering, Information Engineering
Keywords
- channel modeling
- multipath cluster
- MIMO
Conference name
ChinaCom2007
Conference date
0001-01-02
Conference place
Shanghai, China
Status
Published