Air-to-Ground Big-Data-Assisted Channel Modeling Based on Passive Sounding in LTE Networks
(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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- author
- Ye, Xiaokang ; Cai, Xuesong LU ; Yin, Xuefeng ; Rodriguez-Pineiro, Jose ; Tian, Li and Dou, Jianwu
- publishing date
- 2018-01-24
- 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}}, }