@inproceedings{88154d290ec94f91ad9da36e48ee3fe1,
title = "Joint Channel Estimation and Localization for Cooperative Millimeter Wave Systems",
abstract = "Localization is one of the most interesting topics related to the promising millimeter wave (mmWave) technology. In this paper, we investigate joint channel estimation and localization for a cooperative mmWave system with several receivers. Due to the strong line-of-sight path common to mmWave channels, one can localize the position of the user by exploiting the signal's angle-of-Arrival (AoA). Leveraging a variational Bayesian approach, we obtain soft information about the AoA for each receiver. We then use the soft AoA information and geometrical constraints to localize the position of the user and further improve the channel estimation performance. Numerical results show that the proposed algorithm has centimeter-level localization accuracy for an outdoor scene. In addition, the proposed algorithm provides 1-3 dB of gain for channel estimation by exploiting the correlation among the receiver channels depending on the availability of prior information about the path loss model.",
keywords = "Channel estimation, localization, mmWave system, variational Bayesian inference",
author = "Xi Yang and Wen, \{Chao Kai\} and Shi Jin and Swindlehurst, \{A. Lee\} and Jing Zhang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020 ; Conference date: 26-05-2020 Through 29-05-2020",
year = "2020",
month = may,
doi = "10.1109/SPAWC48557.2020.9154215",
language = "英语",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020",
address = "美国",
}