Mobile Station Spatio-Temporal Multipath Clustering of an Estimated Wideband MIMO Double-Directional Channel of a Small Urban 4.5 GHz Macrocell

Materum, Lawrence; Takada, Jun‐ichi; Ida, Ichirou; Oishi, Yushi

doi:10.1155/2009/804021

Cited by 27 publications

(22 citation statements)

References 20 publications

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“…The drawback of KPM is that it is necessary to predetermine the number of clusters, and the result totally depends on it. In practice, the optimum number of clusters is usually determined by any evaluation method such as KimParks index, Calinski-Harabasz, Davies-Bouldin index, and score fusion [61]. The KPM result is not very robust and subject to the normalization of the modified parameter vector, threshold setup, and so on.…”

Section: Mpc Extraction and Automatic Clusteringmentioning

confidence: 99%

Multi-Dimensional Radio Channel Measurement, Analysis and Modeling for High Frequency Bands

Kim

Takada

Saito

2018

IEICE Trans. Commun.

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SUMMARY In order to utilize higher frequency bands above 6 GHz, which is an important technical challenge in fifth generation mobile systems, radio propagation channel properties in a large variety of deployment scenarios should be thoroughly investigated. The authors' group has been involved in a fundamental research project aimed at investigating multipleinput-multiple-output (MIMO) transmission performance and propagation channel properties at microwave frequency above 10 GHz from 2009 to 2013, and since then they have been conducting measurement and modeling for high frequency bands. This paper aims at providing comprehensive tutorial of a whole procedure of channel modeling; multi-dimensional channel sounding, propagation channel measurement, analysis, and modeling, by introducing the developed MIMO channel sounders at high frequency bands of 11 and 60 GHz and presenting some measurement results in a microcell environment at 11 GHz. Furthermore, this paper identifies challenges in radio propagation measurements, and discusses current/future channel modeling issues as future works.

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Section: Mpc Extraction and Automatic Clusteringmentioning

confidence: 99%

Multi-Dimensional Radio Channel Measurement, Analysis and Modeling for High Frequency Bands

Kim

Takada

Saito

2018

IEICE Trans. Commun.

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“…As mentioned before, the Kim-Parks index is a normalized version of the Davis-Bouldin index [16][17][18]. The Kim-Parks index is based on an over-partition measure function and an under-partition measure function, which depend on the inter-cluster separation and the intra-cluster compactness, respectively.…”

Section: ) Kim-parks Indexmentioning

confidence: 99%

Extracting specular-diffuse clusters from MIMO channel measurements

Quitin

Oestges

Bellens

et al. 2011

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

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In previous work, it has been observed that the specular and the diffuse component are linked in the angular domain. The idea of adding a diffuse component to each specular cluster has been proposed to model the speculardiffuse channel. In this paper, an approach is proposed to treat the specular and the diffuse measurement data simultaneously, with a clustering algorithm that is applied jointly on the specular and the diffuse component. The output of the clustering algorithm gives clusters that are characterized by their specular and their diffuse component. Measurement results are presented based on double-directional measurements, and parameter values are extracted using the proposed methodology.

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“…Details of this measurement site could be found in [6]. The channel sounding campaign was done with the use of the RUSK-Fujitsu channel sounder [7].…”

Section: A Channel Soundingmentioning

confidence: 99%

“…[2], [3]), and then their clusterization (e.g. [4]- [6]), from which various channel models and properties can be derived by the subsequent treatments. The data treatment, however, depends on the goals.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric beamforming comparison of the multipath clusterization of ML estimates and the measurement data of a MIMO macrocell channel

Materum¹,

Takada²,

Ida³

et al. 2009

2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications

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Clusters produced by multipath clusterization were comparatively evaluated using the directional power spectrum of the measured channel, which was obtained by polarimetric beamforming due to the dual-polarized nature of the channel sounding data. The clusters were from the results of the clusterization of maximum-likelihood-based path parameter estimates of a macrocell channel. The approach provides a way to render the physical meaning of clustering results in line with the measured channel. The result shows some slight offset from the measured channel, nevertheless, the cluster locations were comparable.

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Mobile Station Spatio-Temporal Multipath Clustering of an Estimated Wideband MIMO Double-Directional Channel of a Small Urban 4.5 GHz Macrocell

Cited by 27 publications

References 20 publications

Multi-Dimensional Radio Channel Measurement, Analysis and Modeling for High Frequency Bands

Multi-Dimensional Radio Channel Measurement, Analysis and Modeling for High Frequency Bands

Extracting specular-diffuse clusters from MIMO channel measurements

Polarimetric beamforming comparison of the multipath clusterization of ML estimates and the measurement data of a MIMO macrocell channel

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