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Wireless Localization Based on Deep Learning: State of Art and Challenges
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2020-10-19 , DOI: 10.1155/2020/5214920
Yun-Xia Ye 1, 2 , An-Nan Lu 1, 2 , Ming-Yi You 1, 2 , Kai Huang 1, 2 , Bin Jiang 1, 2
Affiliation  

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.

中文翻译:

基于深度学习的无线本地化:现状和挑战

位置估计的问题在无线通信领域中一直被广泛讨论。近年来,深度学习技术正在迅速发展并吸引了众多应用。深度学习的高维建模能力使它有可能解决许多非理想情况下的定位问题,这些情况是经典模型难以处理的。因此,在过去的十年中,基于深度学习的无线定位吸引了广泛的研究。综述了基于深度学习的无线定位技术的研究与应用。总结了典型的深度学习模型,重点是它们的输入,输出和本地化方法。还提到了有助于增强本地化能力的技术细节。最后,
更新日期:2020-10-19
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