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.
Originalsprog | Udefineret/Ukendt |
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Titel | 2017 IEEE Globecom Workshops (GC Wkshps) |
Forlag | IEEE |
Publikationsdato | 2017 |
ISBN (Trykt) | 978-1-5386-3921-4 |
ISBN (Elektronisk) | 978-1-5386-3920-7 |
DOI | |
Status | Udgivet - 2017 |
Udgivet eksternt | Ja |
Begivenhed | 2017 IEEE Globecom Workshops (GC Wkshps): 6th International Workshop on Emerging Technologies for 5G and Beyond Wireless and Mobile Networks (ET5GB) - , Singapore Varighed: 8 dec. 2017 → 8 dec. 2017 |
Konference
Konference | 2017 IEEE Globecom Workshops (GC Wkshps) |
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Land/Område | Singapore |
Periode | 08/12/2017 → 08/12/2017 |