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Cited by 594

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

A Survey of 5G Channel Measurements and Models

Wang, Cheng-Xiang, Bian, Ji, Sun, Jian, Zhang, Wensheng, Zhang, Minggao·2018·IEEE Communications Surveys & Tutorials

Abstract

The fifth generation (5G) mobile communication systems will be in use around 2020. The aim of 5G systems to provide anywhere and anytime connectivity for anyone and anything. Several new technologies are being researched for 5G systems, such as massive multiple-input multiple-output communications, vehicle-to-vehicle communications, high-speed train communications, and millimeter wave communications. Each of these technologies introduces new propagation properties and sets specific requirements on 5G channel modeling. Considering the fact that channel models are indispensable for system design and performance evaluation, accurate and efficient channel models covering various 5G technologies and scenarios are urgently needed. This paper first summarizes the requirements of the 5G channel modeling, and then provides an extensive review the recent channel measurements and models. Finally, future research directions for channel measurements and modeling are provided.

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Metadata

Authors
Wang, C.-X., Bian, J., Sun, J., Zhang, W., & Zhang, M.
Venue
IEEE Communications Surveys & Tutorials
Published
2018
Volume
20
Issue
4
Pages
3142-3168
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
OpenAlex
W2900841156
Cited by
594
References
108
FWCI
24.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Survey of 5G Channel Measurements and Models. https://app.cochat.ai/papers/10.1109/comst.2018.2862141
The paper
Wang, C.-X., Bian, J., Sun, J., Zhang, W., & Zhang, M. (2018). A Survey of 5G Channel Measurements and Models. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2018.2862141

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.