A Machine Learning Label-Free Method for Underwater Acoustic OFDM Channel Estimations

被引：0
|
作者：
Zhang, Yonglin [1 ,2 ]
Wang, Haibin [1 ]
Tai, Yupeng [1 ]
Li, Chao [1 ]
Meriaudeau, Fabrice [2 ]
机构：
[1] Chinese Acad Sci, Inst Acoust, Univ Chinese Acad Sci, Beijing, Peoples R China
[2] Univ Bourgogne Franche Comte, Lab ImViA, Dijon, France
来源：
WUWNET'21: THE 15TH ACM INTERNATIONAL CONFERENCE ON UNDERWATER NETWORKS & SYSTEMS | 2021年
关键词：
neural networks; label-free; underwater acoustic channel estimation; OFDM; MIMO-OFDM;
D O I：
10.1145/3491315.3491326
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, a machine learning label-free scheme is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel estimation, which avoids the necessity of the real UWA channel label as in the traditional training process. To this end, a label-free loss function is developed, based on which the training process requires only the received pilot symbols without true channel information. The experiments indicate that, with sufficient training, the proposed label-free network can perform a near-optimal channel estimation.
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页数：5
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