@inproceedings{d430aaedb2d54b6dac1fe79276c379c4,
title = "Learning-based tracking of AoAs and AoDs in mmWave networks",
abstract = "This paper considers a millimeter-wave communication system and proposes an efficient channel estimation scheme with a minimum number of pilots. We model the dynamics of the channel{\textquoteright}s second-order statistics by a Markov process and develop a learning framework to obtain these dynamics from an unlabeled set of measured angles of arrival and departure. We then find the optimal precoding and combining vectors for pilot signals. Using these vectors, the transmitter and receiver will sequentially estimate the corresponding angles of departure and arrival, and then refine the pilot precoding and combining vectors to minimize the error of estimating the channel gains.",
keywords = "Channel estimation, Machine learning, Markov decision process, Millimeter-wave, Tracking",
author = "Ghadikolaei, \{Hossein S.\} and Hadi Ghauch and Carlo Fischione",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems, mmNets 2018, , Co-located with MobiCom 2018 ; Conference date: 29-10-2018",
year = "2018",
month = oct,
day = "1",
doi = "10.1145/3264492.3264500",
language = "English",
series = "Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM",
publisher = "Association for Computing Machinery",
pages = "45--50",
booktitle = "mmNets 2018 - Proceedings of the 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems, Co-located with MobiCom 2018",
}