@inproceedings{6a2f95578abe4cf095ae22815ee89463,
title = "One-Bit DOA Estimation Based on Deep Neural Network",
abstract = "This paper established a deep neural network model for DOA estimation of narrowband signals. First, one-bit quantization is considered into implementation for only retaining the symbol information of training data, as it offers low cost and low complexity in actual communication system. Then we investigate the performance of the neural network trained with quantized data and traditional MUSIC algorithm. Finally, simulations are conducted for correctness and validation. The results illustrate that the proposed method can realize meshless DOA estimation and has higher estimation accuracy in the case of low signal-to-noise ratio.",
keywords = "DOA estimation, Deep neural network, One-bit quantization",
author = "Chen Wang and Suhang Li and Yongkui Ma",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020 ; Conference date: 04-07-2020 Through 05-07-2020",
year = "2021",
doi = "10.1007/978-981-15-8411-4\_77",
language = "英语",
isbn = "9789811584107",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "572--580",
editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Xiaoxia Li and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems",
address = "德国",
}