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Air-to-Ground Big-Data-Assisted Channel Modeling Based on Passive Sounding in LTE Networks

Ye, Xiaokang ; Cai, Xuesong LU ; Yin, Xuefeng ; Rodriguez-Pineiro, Jose ; Tian, Li and Dou, Jianwu (2018) 2017 IEEE Global Telecommunications Conference, GC 2017 p.1-6
Abstract

In this paper, a novel approach of channel modeling based on big data analysis is proposed that is applied to extract air-to-ground channel models from down-link signals collected by using an Unmanned Aerial Vehicle (UAV) in operating Long-Term-Evolution (LTE) networks. In this approach, the most "sensitive" channel parameter to the UAV height is chosen based on a feature selection algorithm from a parameter set consisting of nine channel parameters calculated from channel impulse responses. In the case considered here, the K-factor is found to be the most height-sensitive parameter. The behavior of the mean of K-factor is modeled as a piece-wise function against height which demonstrates a break point that is determined by assessing... (More)

In this paper, a novel approach of channel modeling based on big data analysis is proposed that is applied to extract air-to-ground channel models from down-link signals collected by using an Unmanned Aerial Vehicle (UAV) in operating Long-Term-Evolution (LTE) networks. In this approach, the most "sensitive" channel parameter to the UAV height is chosen based on a feature selection algorithm from a parameter set consisting of nine channel parameters calculated from channel impulse responses. In the case considered here, the K-factor is found to be the most height-sensitive parameter. The behavior of the mean of K-factor is modeled as a piece-wise function against height which demonstrates a break point that is determined by assessing the contribution of height-dependent samples to the overall entropy. The residuals of subtracting the mean K-factor are statistically modeled. The results illustrate that the proposed big-data-assisted approach is applicable to provide accurate description of channel statistics versus the variables of interests.

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Please use this url to cite or link to this publication:
author
; ; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2017 IEEE Globecom Workshops, GC Wkshps 2017 - Proceedings
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2017 IEEE Global Telecommunications Conference, GC 2017
conference location
Singapore, Singapore
conference dates
2017-12-04 - 2017-12-08
external identifiers
  • scopus:85050480308
ISBN
9781538639207
9781538639214
DOI
10.1109/GLOCOMW.2017.8269204
language
English
LU publication?
no
additional info
Funding Information: This work was jointly supported by National Natural Science Foundation of China (NSFC) (Grant No. 61471268), the Key Project “5G Ka frequency bands and higher and lower frequency band cooperative trail system research and development” under Grant 2016ZX03001015 of China Ministry of Industry and Information Technology, and the HongKong, Macao and Taiwan Science & Technology Cooperation Program of China under Grant 2014DFT10290. Corresponding author: Xuefeng Yin. Publisher Copyright: © 2017 IEEE.
id
5bb3be0f-20fa-4958-822c-64d2bdc2f62e
date added to LUP
2021-11-22 22:48:13
date last changed
2024-04-20 16:14:51
@inproceedings{5bb3be0f-20fa-4958-822c-64d2bdc2f62e,
  abstract     = {{<p>In this paper, a novel approach of channel modeling based on big data analysis is proposed that is applied to extract air-to-ground channel models from down-link signals collected by using an Unmanned Aerial Vehicle (UAV) in operating Long-Term-Evolution (LTE) networks. In this approach, the most "sensitive" channel parameter to the UAV height is chosen based on a feature selection algorithm from a parameter set consisting of nine channel parameters calculated from channel impulse responses. In the case considered here, the K-factor is found to be the most height-sensitive parameter. The behavior of the mean of K-factor is modeled as a piece-wise function against height which demonstrates a break point that is determined by assessing the contribution of height-dependent samples to the overall entropy. The residuals of subtracting the mean K-factor are statistically modeled. The results illustrate that the proposed big-data-assisted approach is applicable to provide accurate description of channel statistics versus the variables of interests.</p>}},
  author       = {{Ye, Xiaokang and Cai, Xuesong and Yin, Xuefeng and Rodriguez-Pineiro, Jose and Tian, Li and Dou, Jianwu}},
  booktitle    = {{2017 IEEE Globecom Workshops, GC Wkshps 2017 - Proceedings}},
  isbn         = {{9781538639207}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{1--6}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Air-to-Ground Big-Data-Assisted Channel Modeling Based on Passive Sounding in LTE Networks}},
  url          = {{http://dx.doi.org/10.1109/GLOCOMW.2017.8269204}},
  doi          = {{10.1109/GLOCOMW.2017.8269204}},
  year         = {{2018}},
}