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 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.
Original language | English |
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Title of host publication | Proc. ChinaCom 2007 |
DOIs | |
Publication status | Published - 2007 |
Externally published | Yes |
Event | ChinaCom2007 - Shanghai, China Duration: 0001 Jan 2 → … |
Conference
Conference | ChinaCom2007 |
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Country/Territory | China |
City | Shanghai |
Period | 0001/01/02 → … |
Subject classification (UKÄ)
- Electrical Engineering, Electronic Engineering, Information Engineering
Free keywords
- channel modeling
- multipath cluster
- MIMO