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

Xiaokang Ye, Xuesong Cai, Xuefeng Yin, José Rodríguez-Piñeiro, Li Tian, Jianwu Dou

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23 Citationer (Scopus)

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 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.
OriginalsprogUdefineret/Ukendt
Titel2017 IEEE Globecom Workshops (GC Wkshps)
ForlagIEEE
Publikationsdato2017
ISBN (Trykt)978-1-5386-3921-4
ISBN (Elektronisk)978-1-5386-3920-7
DOI
StatusUdgivet - 2017
Udgivet eksterntJa
Begivenhed2017 IEEE Globecom Workshops (GC Wkshps): 6th International Workshop on Emerging Technologies for 5G and Beyond Wireless and Mobile Networks (ET5GB) - , Singapore
Varighed: 8 dec. 20178 dec. 2017

Konference

Konference2017 IEEE Globecom Workshops (GC Wkshps)
Land/OmrådeSingapore
Periode08/12/201708/12/2017

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