A Power-Angle-Spectrum Based Clustering and Tracking Algorithm for Time-Varying Radio Channels

Huang, Chen; He, Ruisi; Zhang, Zhong; Ai, Bo; Geng, Yangli-ao; Zhong, Zhimeng; Li, Qingyong; Haneda, Katsuyuki; Oestges, Claude

doi:10.1109/tvt.2018.2878049

Cited by 27 publications

(10 citation statements)

References 39 publications

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“…Figs. 7 show the comparison among theoretical values (mean delay and Doppler calculated from the simulation) and tracked values respectively from the proposed PSBST, the PASCT [24] and the conventional KPMCT [8] from snap- shot 680 to 780 when three clusters coexist. As regards the KPMCT, we use the combination of Calinski-Harabasz index (CH) and Davies-Bouldin index (DB) [35] to automatically determine the number of clusters and then iteratively run the KPowerMeans algorithm with the definite number of clusters five times to guarantee the clustering convergence.…”

Section: B Evaluation Resultsmentioning

confidence: 99%

“…It can rapidly detect clusters compared with conventional HRPE based algorithms [18]- [23]. Moreover, it can completely identify clusters avoiding the cluster omission which happens in PASCT [24].…”

Section: Fine-grained Segmentationmentioning

confidence: 99%

“…• We conduct the synthetic channel simulation to investigate performance of the tracker. The accuracy and computational time of PSBST are compared with those of PASCT [24] and the conventional KPowerMeans based cluster tracking algorithm (KPMCT) [8].…”

Section: Fine-grained Segmentationmentioning

confidence: 99%

“…The proposed PSBST belongs to the category of FCLT and it is also divided into two steps, i.e., clustering and tracking as described in [8], [18] and [24]. The flowchart of PSBST is shown in Fig.…”

Section: Proposed Sequential Trackermentioning

confidence: 99%

“…In order to avoid these troublesome details, a new kind of algorithms represented by the power-angle-spectrum based clustering and tracking algorithm (PASCT) [24] recently emerges which identifies clusters via image processing of power spectrum and tracks clusters using image feature matching. In fact, this kind of algorithms is the application of computer vision in channel modeling.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Power Spectrum-Based Sequential Tracker for Time-Variant Radio Propagation Channel

Yin

Lee

2020

IEEE Access

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Cluster tracking is a mainstream approach for the study of time-variant channel characteristics. In the paper, we propose a power spectrum based sequential tracker (PSBST) to compensate for the disadvantages of existing cluster tracking algorithms. The proposed tracker identifies clusters via simple three-stage power spectrum processing. Furthermore, fuzzy c-means (FCM) algorithm is incorporated to separate clusters considering the overlapped clusters which may appear in the power spectrum. In terms of tracking, we implement Kalman filter to sequentially predict candidate ranges of tracked clusters in consecutive snapshots and simultaneously a novel gradient-based histogram of power (GBHOP) method is devised to determine the evolution of clusters. We also investigate the performance of the tracker by synthetic channel simulation. It demonstrates high accuracy and less computational time compared with the results derived from the existing cluster tracking algorithms. Besides, we verify applicability of the tracker by analyzing the field measurement conducted in vehicle-to-everything (V2X) scenario, and preliminary statistical characteristics for intra-cluster and inter-cluster parameters can be readily obtained in the sequel.INDEX TERMS Time-variant radio channel, cluster tracking, power spectrum, fuzzy clustering, cluster detection, V2X.

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Section: B Evaluation Resultsmentioning

confidence: 99%

Section: Fine-grained Segmentationmentioning

confidence: 99%

Section: Fine-grained Segmentationmentioning

confidence: 99%

Section: Proposed Sequential Trackermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Power Spectrum-Based Sequential Tracker for Time-Variant Radio Propagation Channel

Yin

Lee

2020

IEEE Access

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Device‐to‐Device Channels

He¹,

Yang²,

Ai³

et al. 2019

Wiley 5G Ref

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Device‐to‐device (D2D) communications enable devices in a communication system to exchange data directly. Through D2D communication link, terminals communicate through multiplexing system resources, which can reduce the pressure on the core network of the communication systems and greatly improve the spectral efficiency and throughput. Therefore, D2D technology is considered as one of the key technologies of 5G systems. Investigation of channel characteristics is an important prerequisite for subsequent D2D communication research. This article reviews the measurements and models for D2D channels. The challenges that D2D channel measurement and modeling may face in the future are also addressed.

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