The Extraction and Tracking Trajectory of Wireless Channel Tap Clusters Based on Machine Learning

doi:10.13190/j.jbupt.2018-298

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (4): 126-132.doi: 10.13190/j.jbupt.2018-298

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The Extraction and Tracking Trajectory of Wireless Channel Tap Clusters Based on Machine Learning

ZHANG Jia-chi^1,2, LIU Liu¹, ZHOU Tao¹, WANG Kai¹, PIAO Zhe-yan²

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
2. School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China

Received:2018-11-28 Online:2019-08-28 Published:2019-08-26

Abstract

Abstract: A new method for extraction and tracking trajectory of dynamic wireless channel tap clusters is proposed. First, the channel impulse response (CIR) denoising is achieved by back propagation (BP) neural network in time delay-amplitude dimension. Then effective taps are clustered by k-means clustering algorithm. Next, density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied to remove the abnormal peak taps for every cluster. Finally, the trajectory of cluster peak taps is obtained by polynomial fitting. The simulation result shows that trajectory obtained by proposed method is approximate to geometric calculation result. Moreover, the analysis result of high speed railway measured data is consistent with the actual observations.

Key words: wireless channel, neural network, density-based clustering, tap clusters, tracking trajectory

CLC Number:

TN929.53

ZHANG Jia-chi, LIU Liu, ZHOU Tao, WANG Kai, PIAO Zhe-yan. The Extraction and Tracking Trajectory of Wireless Channel Tap Clusters Based on Machine Learning[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(4): 126-132.

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The Extraction and Tracking Trajectory of Wireless Channel Tap Clusters Based on Machine Learning

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