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A Novel Target Recognition Based Radio Channel Clustering Algorithm

  • Chen Huang
  • , Ruisi He*
  • , Zhangdui Zhong
  • , Bo Ai
  • , Gongpu Wang
  • , Zhimeng Zhong
  • , Claude Oestges
  • , Katsuyuki Haneda
  • *Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

6 Sitaatiot (Scopus)

Abstrakti

In this paper, a novel target recognition based clustering algorithm is proposed for time-varying channels. Power angle spectrum (PAS) is extracted from measurement data by using Bartlett beamformer. Then the clusters in the PAS are separated from the background by using the proposed algorithm, where the amplitude distribution of the elements in the PAS is considered. Moreover, morphology operations are applied to further divide the clusters which are connected to each other. It is found that, the dominating clusters in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments can be well recognized by the proposed algorithm with low computation cost. By using the proposed algorithm, the dynamic changes of the clusters in real-Time channel measurement can be clearly observed, without using any high-resolution parameter estimation.

AlkuperäiskieliEnglanti
Otsikko2018 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018
KustantajaIEEE
ISBN (elektroninen)978-1-5386-6119-2
ISBN (painettu)978-1-5386-6120-8
DOI - pysyväislinkit
TilaJulkaistu - 30 marrask. 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Wireless Communications and Signal Processing - Hangzhou, Kiina
Kesto: 18 lokak. 201820 lokak. 2018
Konferenssinumero: 10

Julkaisusarja

NimiInternational Conference on Wireless Communications and Signal Processing
KustantajaIEEE
ISSN (painettu)2325-3746
ISSN (elektroninen)2472-7628

Conference

ConferenceInternational Conference on Wireless Communications and Signal Processing
LyhennettäWCSP
Maa/AlueKiina
KaupunkiHangzhou
Ajanjakso18/10/201820/10/2018

Rahoitus

VI. ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China under Grant 61771037, 61501020, and 61471360, the National Key R&D Program of China under Grant 2018YFF0212103, 2016YFB1200102-04, and 2016YFE0200900, the Beijing Natural Science Foundation (4182047), the State Key Laboratory of Rail Traffic Control and Safety under Grant RCS2018ZT014, the Fundamental Research Funds for the Central Universities under Grant 2018JB-M301, the Teaching Reform Project under Grant 134601522.

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