Modeling Outdoor Macrocellular Clusters Based on 1.9-GHz Experimental Data

Oestges, Claude; Clerckx, Bruno

doi:10.1109/tvt.2007.900391

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…Considering all Tx positions, the distribution is close to lognormal. This is in agreement with what has been reported in [25] for a short-range macrocellular environment at 1.9 GHz. The mean values 2 of σ τ for Tx1, Tx2, and Tx3, are 0.20, 0.24, and 0.28 μs, respectively.…”

Section: Delay Spreadsupporting

confidence: 93%

Urban peer-to-peer MIMO channel measurements and analysis at 300 MHz

Eriksson

Linder

Wiklundh

et al. 2008

MILCOM 2008 - 2008 IEEE Military Communications Conference

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Multiple-input multiple-output (MIMO)systems operating at frequencies in the upper VHF and lower UHF region is attractive for peer-to-peer communication applications where robustness is of high importance, e.g., in tactile networks and emergency response systems. When designing and evaluating such systems, knowledge of realistic propagation conditions is required. This paper presents results from an uran MIMO measurement campaign at 300 MHz. Measurements are performed along 25 receiver routes and for three fixed transmitter locations, using antenna arrays mounted on two cars. Channel characteristics and ergodic capacity for the 7X7 MIMO channels are extracted from the measured data. A path-loss model is derived for the measured scenario, and the distributions of the large-scale fading, the Ricean K-factor, the delay spread, and the ergodic capacity are studied in deatail. The correlation distance for the different channel parameters is also examined. Furthermore, the analysis reveals that several of the channel parameters are correlated, and also have a strong correlation with the capacity. MILCOM 2008This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-Multiple-input multiple-output (MIMO) systems operating at frequencies in the upper VHF and lower UHF region is attractive for peer-to-peer communication applications where robustness is of high importance, e.g., in tactical networks and emergency response systems. When designing and evaluating such systems, knowledge of realistic propagation conditions is required. This paper presents results from an urban MIMO measurement campaign at 300 MHz. Measurements are performed along 25 receiver routes and for three fixed transmitter locations, using antenna arrays mounted on two cars. Channel characteristics and ergodic capacity for the 7×7 MIMO channels are extracted from the measured data. A path-loss model is derived for the measured scenario, and the distributions of the large-scale fading, the Ricean K-factor, the delay spread, and the ergodic capacity are studied in detail. The correlation distance for the different channel parameters is also examined. Furthermore, the analysis reveals that several of the channel parameters are correlated, and also have a strong correlation with the capacity.

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Section: Delay Spreadsupporting

confidence: 93%

Urban peer-to-peer MIMO channel measurements and analysis at 300 MHz

Eriksson

Linder

Wiklundh

et al. 2008

MILCOM 2008 - 2008 IEEE Military Communications Conference

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show abstract

“…Several multipath clustering algorithms have already been proposed, which are either manual (visual inspection of data) or automatic, and some are the combination of both. Visual inspection has been widely used in the past [3]- [4], but because of its limitations most especially in clustering high-dimensional data, there was the development of the different automatic clustering algorithms for better channel modeling. However, finding an efficient and accurate clustering algorithm is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

Teologo¹

2020

IJETER

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In radio communications, channel modeling has a very significant impact. Most of the system's performance relies on the behavior and characteristics of a radio channel. Due to its mobility features, radio channels are time-variant that change over time, making channel characterizations to be dynamic and very challenging. To ensure maximum data rate and reliable communication, accurate channel models are necessary, which requires a correct grouping of wireless multipaths. Accurate clustering of wireless multipath components (MPCs) is essential for cluster-based channel modeling resulting in a more reliable wireless channel system. Currently, determining the best clustering technique is still a challenge as there is no standard way of evaluating and comparing the performance of the various clustering algorithms. This work presents the comparative study on the accuracy performance of the four clustering algorithms namely K-Power Means (KPM), Ant Colony Optimization (ACO), Kernel Power Density-based Estimation (KPD), and Gaussian Mixture Model (GMM) in grouping the wireless MPCs using datasets generated from COST 2100 channel model (C2CM) which represent different Indoor and Semi-urban channel scenarios. Using the Jaccard index, an evaluation metric that compares the calculated data with the reference data, the accuracy of each algorithm is determined as well as their corresponding computational duration. A comparison of their performance is presented, and results show that KPM outperforms other clustering techniques in all channel scenarios of Indoor and Semi-urban environments, making it a right candidate for further improvements to develop a more accurate and computationally efficient clustering technique for wireless propagation multipaths.

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“…Either manual or automatic clustering techniques have been used to group MPCs. In the past, the manual method has been widely used [1], [2], [3], [4], [5], [6] through visual inspection but has posed limitations when high-dimensional data is involved. For this reason, automatic clustering has replaced the manual method.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Wise Jaccard Accuracy of KPower Means on Multipath Datasets

Teologo¹,

Materum²

2019

IJETER

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This paper presents the accuracy performance of the KPower Means (KPM) algorithm in clustering wireless multipaths using the generated datasets from COST2100 channel model (C2CM). KPM is one of the popular techniques used to cluster wireless multipath components (MPCs) and has been a basis of other complex multipath clustering approaches. KPM is implemented in Matlab using eight different channel scenarios obtained from C2CM representing various indoor and semi-urban environments at 2.85 MHz and 5.3 GHz bands, respectively. Results show that KPM performs well in an indoor environment than in a semi-urban due to the presence of numerous scatterers in a semi-urban environment yielding more multipaths. Jaccard similarity index is used to validate the accuracy performance of the KPM.

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Modeling Outdoor Macrocellular Clusters Based on 1.9-GHz Experimental Data

Cited by 14 publications

References 10 publications

Urban peer-to-peer MIMO channel measurements and analysis at 300 MHz

Urban peer-to-peer MIMO channel measurements and analysis at 300 MHz

Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

Cluster-Wise Jaccard Accuracy of KPower Means on Multipath Datasets

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