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Wireless Localization Based on Deep Learning: State of Art and Challenges
Joint Authors
Jiang, Bin
Ye, Yun-Xia
Lu, An-Nan
You, Ming-Yi
Huang, Kai
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The problem of position estimation has always been widely discussed in the field of wireless communication.
In recent years, deep learning technology is rapidly developing and attracting numerous applications.
The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models.
Consequently, wireless localization based on deep learning has attracted extensive research during the last decade.
The research and applications on wireless localization technology based on deep learning are reviewed in this paper.
Typical deep learning models are summarized with emphasis on their inputs, outputs, and localization methods.
Technical details helpful for enhancing localization ability are also mentioned.
Finally, some problems worth further research are discussed.
American Psychological Association (APA)
Ye, Yun-Xia& Lu, An-Nan& You, Ming-Yi& Huang, Kai& Jiang, Bin. 2020. Wireless Localization Based on Deep Learning: State of Art and Challenges. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1195758
Modern Language Association (MLA)
Ye, Yun-Xia…[et al.]. Wireless Localization Based on Deep Learning: State of Art and Challenges. Mathematical Problems in Engineering No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1195758
American Medical Association (AMA)
Ye, Yun-Xia& Lu, An-Nan& You, Ming-Yi& Huang, Kai& Jiang, Bin. Wireless Localization Based on Deep Learning: State of Art and Challenges. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1195758
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195758