Identification of Clusters in UWB Channel Modeling

Woon, Ong Hwee; Krishnan, S. P. T.

doi:10.1109/vtcf.2006.41

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…It has been widely recognized that clusters are groups of MPCs exhibiting the similar larger-scale properties, such as ToAs, angle of arrivals (AoAs) and decaying amplitudes [11] [17][20] [22]. In other word, the MPCs showing very similar large-scale fading properties (or reflected from the same large object) should be grouped into one cluster, while those exhibiting quite different amplitudes or ToAs characteristics should be put into another cluster.…”

Section: Ant Intelligence Inspired Cluster Identificationmentioning

confidence: 99%

“…Thus, despite the significant theoretical contribution, this method can be hardly applied to channel modeling or the practical design, in which a channel response is also supposed to be impractically sparse [36]. In fact, as highlighted by refs [17] and [20], amplitude discontinuities usually involve useful information to judge a cluster [37]. In this scheme, we manage to make full use of the decaying property of amplitudes, yet still consider ToAs as the indispensable information for successful cluster identifications.…”

Section: Parameters Configurationmentioning

confidence: 99%

“…Ref. [17] firstly studied the automatic cluster identification issue by setting up a quantitative criterion of cluster, which may unfortunately produce fake clusters due to serious small-scale fading or specula reflections. Next, by assuming that different clusters with an exponential power delay profile (PDP) may introduce remarkable amplitude discontinuity, Ref.…”

Section: Introductionmentioning

confidence: 99%

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Efficient and Robust Cluster Identification for Ultra-wideband Propagations Inspired byBiological Ant Colony Clustering

Zhao

Zhang

et al. 2014

IEEE Trans. Commun.

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Cluster identification of ultra-wideband propagations is of great significance to the parameters extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e. the decaying amplitude and the time of arrivals (ToAs), are combined organically and explored fully in the suggested cluster identification algorithm. Each resolvable trajectory component is firstly projected onto a two dimensional (2-D) amplitude-time plane, and further modeled as a virtual ant-agent which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can accurately and efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameters configuration is analyzed and a group of numerical performance metrics are derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses (CIRs) can be extracted automatically.

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Section: Ant Intelligence Inspired Cluster Identificationmentioning

confidence: 99%

Section: Parameters Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient and Robust Cluster Identification for Ultra-wideband Propagations Inspired byBiological Ant Colony Clustering

Zhao

Zhang

et al. 2014

IEEE Trans. Commun.

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“…Past efforts have developed a lot of algorithms on cluster identification, but most of them cannot get satisfactory results [10,11]. In some case, researchers focused on setting up some various criteria for cluster, but they ignored that rays in the same cluster decay exponentially which is inherent to SalehValenuela model [10].…”

Section: Introductionmentioning

confidence: 99%

“…In some case, researchers focused on setting up some various criteria for cluster, but they ignored that rays in the same cluster decay exponentially which is inherent to SalehValenuela model [10]. Other researchers only used the exponential decay of the power delay profile (PDP), but ignored the importance of the power threshold of the rays which should be decay by the ray delay [12].…”

Section: Introductionmentioning

confidence: 99%

Novel Cluster Identification Algorithm for Ultra-Wide Band Channel

Zhong

Zhu

Kong

et al. 2010

2010 International Conference on Computational Intelligence and Software Engineering

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Saleh-Valenuela model was modified by both IEEE802.15.3a and IEEE802.15.4a task group, and became the standard channel model for UWB indoor environment. Cluster must be identified before any Saleh-Valenuela model parameters can be extracted. Past efforts have developed a lot of algorithms on cluster identification, but most of them ignored either the exponentially decay of power delay profile (PDP) or the power threshold of the cluster starting ray. Here we propose a new approach which uses both of these essential factors. Simulation results on IEEE 802.15.3a committee channel model shows that our algorithm can get the right position of the cluster starting point even when the clusters are overlapped.

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