Tracking time-variant cluster parameters in MIMO channel measurements

Nicolai Czink, Ruiyuan Tian, Shurjeel Wyne, Fredrik Tufvesson, Jukka-Pekka Nuutinen, Juha Ylitalo, Ernst Bonek, Andreas Molisch

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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.
Original languageEnglish
Title of host publicationProc. ChinaCom 2007
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventChinaCom2007 - Shanghai, China
Duration: 0001 Jan 2 → …

Conference

ConferenceChinaCom2007
Country/TerritoryChina
CityShanghai
Period0001/01/02 → …

Subject classification (UKÄ)

  • Electrical Engineering, Electronic Engineering, Information Engineering

Free keywords

  • channel modeling
  • multipath cluster
  • MIMO

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