A Novel Tracking-Based Multipath Component Clustering Algorithm

Huang, Chen; He, Ruisi; Zhang, Zhong; Geng, Yangli-ao; Li, Qingyong; Zhong, Zhimeng

doi:10.1109/lawp.2017.2740622

Cited by 23 publications

(17 citation statements)

References 16 publications

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“…• We implement a simple three-stage image processing in the framework of clustering. 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%

“…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%

“…First clustering last tracking (FCLT) and joint clustering-and-tracking are two common algorithm frameworks. The former identifies clusters in each snapshot using some clustering algorithm and then tracks clusters snapshot by snapshot based on their parameter characteristics, e.g., MCD [8] or total sum of distances [18]. Meanwhile, the latter framework first tracks the clusters or MPCs in the previous snapshot and then identifies clusters in the current snapshot based on the tracking results [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: 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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“…Authors in [25] proposed a novel clustering framework based on Kernel-Power-Density algorithm and took elevation angles into consideration. The Kuhn-Munkres algorithm was proposed to solve the tracking problem in [26]. The Kalman filter in [27] was used to track the clusters and to predict the cluster positions.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Bai

Wang

Huang

et al. 2018

Wireless Communications and Mobile Computing

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This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

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“…Reference [7] built an evolution model of the number of clusters from dynamic double-directional measurements in an indoor environment, and uses cluster-level parameters to describe the mobile channels. Reference [8] gives a Kuhn-Munkres-based method to model the evolution of clusters that is based on simulations of the 3rd Generation Partnership Project (3GPP) channel model. However, cluster-tracking statistical analysis to massive Multiple Input Multiple Output (MIMO) pedestrian mobile measurements, which can detect richer scatterers in environments, needs to be further studied.…”

Section: Introductionmentioning

confidence: 99%

Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations

Wang

Zhang

2019

Applied Sciences

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In wireless communication systems, channels evolve when user terminals move. To further understand channel variation, and especially the evolution of clusters in mobile channels, a set of experiments was designed. First, we performed pedestrian mobile measurements in an urban macro (UMa) scenario at 3.5 GHz, and the K-power means-Kalman filter (KPMKF) algorithm was used for clustering and tracking. By this process, the trajectory of different clusters could clearly be described during measurement. The birth and death rate of clusters per snapshot show that the change of one or two clusters in each snapshot takes more probabilities. In addition, the differences of the cluster lifetime between the clustering process with and without the Kalman filter (KF) algorithm are given to show the effect from the KF. Second, channel simulations were implemented based on the above observed results. The spatial-consistency feature was introduced to get closer to the measured channels, which is based on the primary module of International Mobile Telecommunications-2020 (IMT-2020) channel model. Comparisons among measurements and simulations with and without this feature show that adding this feature improves simulation accuracy. To explore a novel method to characterize clusters during linear movement, a gradient boosted decision-tree (GBDT) algorithm is introduced. It uses the above characteristics of clusters and channel impulse responses (CIRs) as the training and validating dataset. The root mean square error (RMSE) shows that this is promising.

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A Novel Tracking-Based Multipath Component Clustering Algorithm

Cited by 23 publications

References 16 publications

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

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

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations

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