Improving clustering performance using multipath component distance

Czink, Nicolai; Cera, P.; Salo, J.; Bonek, E.; Nuutinen, Jukka-Pekka; Ylitalo, J.

doi:10.1049/el:20063917

Cited by 75 publications

(27 citation statements)

References 3 publications

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“…MC distance (MCD) [18] including powers of propagation paths is used as a metric for calculating the multidimensional distances between propagation paths and cluster centroids. Propagation paths are set to the clusters based on the shortest MCD.…”

Section: Cluster Parametersmentioning

confidence: 99%

Parametrization and Validation of Geometry-Based Stochastic Channel Model for Urban Small Cells at 10 GHz

Roivainen

Kyösti

Dias

et al. 2017

IEEE Trans. Antennas Propagat.

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Abstract-We derive a full measurement-based parametrization for a 3-D geometry-based stochastic channel model for an urban microcell type of environment at 10 GHz. The measurements were performed with a vector network analyzer and dual polarized virtual arrays at the bandwidth of 500 MHz. The proposed parametrization is validated by channel simulations. Multiple-input multiple-output channel is reconstructed from the measured data and compared with channel generated by the quasi deterministic radio channel generator using the proposed statistical parameters. The capacity and eigenvalue distribution are used as the validation metrics, showing almost perfect match between the reconstructed and simulated channels.Index Terms-3-D, geometry-based stochastic channel model (GSCM), multiple-input multiple-output (MIMO), polarization, statistical parameters, validation, virtual antenna array.

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Section: Cluster Parametersmentioning

confidence: 99%

Parametrization and Validation of Geometry-Based Stochastic Channel Model for Urban Small Cells at 10 GHz

Roivainen

Kyösti

Dias

et al. 2017

IEEE Trans. Antennas Propagat.

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“…Because of simplicity elevation, polarimetric path weight and Doppler are not considered. In order to evaluate the similarity between multipath components and cluster centroids based on the different parameters of the data model, the weighted multipath component distance (MCD) is used [9] [8], , · , .…”

Section: A Data Model and Multipath Component Distance Measurementioning

confidence: 99%

“…A so-called cluster power threshold (CPT) is introduced within the IG, to define the stop criteria based on the minimum power allowed for the smallest cluster. The delay scaling factor (DSF) [9] is introduced to additionally weight the delay in the calculation of the MCD with the IG and clustering. However for fixed CPT and DSF the described approach always converges toward the same centroids, and therefore IG is considered as deterministic procedure.…”

Section: E Deterministic and Random Initialization 1) Initial Guess mentioning

confidence: 99%

“…A concept based on hierarchical clustering is presented in [8]. The K-means approach is extended in [9] to include power and dimension weighting, in the form of the multipath component distance (MCD) [10]. Furthermore a procedure for centroid tracking and a deterministic algorithm to estimate the number and positions of the initial centroids can be found in [5].…”

Section: Introductionmentioning

confidence: 99%

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Clustering of MIMO Channel Parameters - Performance Comparison

Schneider

Bauer

Narandzic

et al. 2009

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference

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Novel channel models as from COST 273 and IST-WINNER projects are models to evaluate the performance of multi-antenna concepts under link-level and system-level. For consistent performance evaluation the channel models needed to be parameterized by multipath parameters based on measurements. It seems these parameters can be grouped into geometrically co-located paths, so called clusters. The reliability and reproducibility of the estimated parameter groups, depend inter alia on the decision criterions, initialization and the chosen cluster algorithm itself. In this paper the focus is to analyse the performance of different clustering algorithms and initialization stages. Furthermore an improved initialization approach is presented.Index Terms-Multipath clustering, spatial channel modelling, high resolution multipath parameter estimation, geometry-based stochastic channel model, generic multi-path cluster MIMO channel I 978-1-4244-2517-4/09/$20.00 ©2009 IEEE

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“…Scatterer scenario. a recent paper, k-means algorithm with a different distance metric is used to identify clusters from measurement data [11]. From Figs.…”

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confidence: 99%

On the Number of Clusters in Channel Model

Akkaya

Tunc

Aktas

et al. 2006

2006 IEEE Ninth International Symposium on Spread Spectrum Techniques and Applications

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Abstract-Typically, scatterers in an environment are not spread and rms delay spread (RDS). We use a geometric distributed uniformly but rather observed to occur in clusters. channel model based on COST259 for an outdoor radio enviIdentification of clusters is an issue under discussion. To this ronment. Note that in the COST259 model, NoC is a random end, we study the effect of number of clusters on channel .. ' model through computer simulations. We focus on a geometric parameter. However, in this paper, fixng a scatterer scenario, stochastic directional channel model based on COST259. Fixing we group the scatterers into varying NoC and compute the a scatterer scenario, we calculate root mean square delay and rms angular and delay spreads of the corresponding channel angular spreads when scatterers are grouped into varying num-model.bers of clusters and study how sensitive these parameters are to the number of clusters used in this channel model. II. STRUCTURE OF THE SYSTEM I. INTRODUCTION We consider a circular cell of radius Rceii where base station For the improvement of existing mobile radio systems, real-(BS) is placed at the center of the cell (origin). Mobile station istic channel models are required. In this work, we concentrate (MS) can be assumed to be located at any point in the cell. on a geometric stochastic channel model based on COST259 We assume a number of scatterers are distributed over the cell [1]. In this model the ray paths are coming from the scatterers area. These scatterers are grouped into clusters. Each scatterer distributed in the cell. Number and the density of scatterers inside the cluster represents a single multi-path component. differ according to the environment. From the experimental The geometrical representation is given in Fig. 1. data, it is observed that scatterers tend to occur in groups [2]. Therefore, the scatterers are grouped into clusters. Handling the channel by clustering of scatterers enables us to get a more simplified approach and physically appealing picture.Clusters of scatterers in a propagation environment are typiFar cluster m cally identified through channel measurements. To estimate the of scatterers location of clusters throughout the cell, one approach is to use BS the angle of arrival data from the measurements. The clusters are then specified according to their contribution to the total R received power [3]. According to measurement results, cluster cell locations and parameters such as propagation delays, angles of arrival and departure for the scatterers within each cluster can be modeled using appropriate probability distribution functions. Afterwards, the impulse response of the geometry based stochastic channel model is evaluated using these parameters [4]. COST259 is one of the geometric channel models and its parameters are based on the number and the location of at tranmoite s.ign alav the bas saion daies clusters, and the existence of visibility regions [5]. Since the athemblsaiowthaprcurdlywihdpns clusters, and .. of..visibility.region se...

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Improving clustering performance using multipath component distance

Cited by 75 publications

References 3 publications

Parametrization and Validation of Geometry-Based Stochastic Channel Model for Urban Small Cells at 10 GHz

Parametrization and Validation of Geometry-Based Stochastic Channel Model for Urban Small Cells at 10 GHz

Clustering of MIMO Channel Parameters - Performance Comparison

On the Number of Clusters in Channel Model

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