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1 Ergebnisse
1
Enabling Accurate Positioning in NLOS Scenarios by Hybrid M..:
, In:
2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
,
Zhao, Longhai
;
Xiong, Qi
;
Yang, Yunchuan
... - p. 1-5 , 2022
Link:
https://doi.org/10.1109/VTC2022-Fall57202.2022.1001299
RT T1 <cTitle>2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)</cTitle>: T1 <Title>Enabling Accurate Positioning in NLOS Scenarios by Hybrid Machine Learning with Denoising and Inpainting</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10012991&Exemplar=1&LAN=DE A1 Zhao, Longhai A1 Xiong, Qi A1 Yang, Yunchuan A1 Li, Pengru A1 Yu, Bin A1 Sun, Feifei A1 Sun, Chengjun A1 Xue, Peng YR 2022 SN 2577-2465 K1 Training K1 Vehicular and wireless technologies K1 Noise reduction K1 Supervised learning K1 Machine learning K1 Learning (artificial intelligence) K1 Noise measurement K1 Positioning K1 NLOS K1 Diffusion generative models SP 1 OP 5 LK http://dx.doi.org/https://doi.org/10.1109/VTC2022-Fall57202.2022.10012991 DO https://doi.org/10.1109/VTC2022-Fall57202.2022.10012991 SF ELIB - SuUB Bremen
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