Spatially Consistent Air-to-Ground Channel Modeling via Generative Neural Networks-Reference-Cited by-同舟云学术

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Spatially Consistent Air-to-Ground Channel Modeling via Generative Neural Networks

Published:2024-04 Issue:4 Volume:13 Page:1158-1162
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Giuliani Amedeo¹^ORCID,Nikbakht Rasoul¹^ORCID,Geraci Giovanni²^ORCID,Kang Seongjoon³^ORCID,Lozano Angel⁴^ORCID,Rangan Sundeep³^ORCID

Affiliation:

1. Centre Tecnològic de Telecomunicacions de Catalunya, Castelldefels, Spain

2. Telefónica, Madrid, Spain

3. NYU Tandon School of Engineering, Brooklyn, NY, USA

4. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

Funder

Spanish Research Agency

European Union NextGenerationEU through the UNICO 5G I+D SORUS Project

UPF-Fractus Chair

Institució Catalana de Recerca i Estudis Avançats

NSF

National Telecommunications and Information Administration

Industrial Affiliates of NYU WIRELESS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962382/10496347/10423834.pdf?arnumber=10423834

Reference22 articles.

1. What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G

2. A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence

3. UAV Communications for 5G and Beyond

4. Optimizing cellular networks for UAV corridors via quantization theory;Karimi-Bidhendi;arXiv:2308.01440,2023

5. The Essential Guide to Realizing 5G-Connected UAVs with Massive MIMO

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