Paper Info
Title
Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel.
Abstract
In recent years, deep learning (DL) techniques have shown great potential in wireless communications. Unlike DL-based receivers for time-invariant or slow time-varying channels, we propose a new DL-based receiver for single carrier communication in time-varying underwater acoustic (UWA) channels. Without the off-line training, the proposed receiver alternately works with online training and test modes for accommodating the time variability of UWA channels. Simulation results show a better detection performance achieved by the proposed DL-based receiver and with a considerable reduction in training overhead compared to the traditional channel-estimate (CE)-based decision feedback equalizer (DFE) in simulation scenarios with a measured sound speed profile. The proposed receiver has also been tested by using the data recorded in an experiment in the South China Sea at a communication range of 8 km. The performance of the receiver is evaluated for various training overheads and noise levels. Experimental results demonstrate that the proposed DL-based receiver can achieve error-free transmission for all 288 burst packets with lower training overhead compared to the traditional receiver with a CE-based DFE.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2906424
IEEE ACCESS
Keywords
Field
DocType
Channel equalization,deep learning,deep neural network,DFE,machine learning,single carrier communication,underwater acoustic network
Sound speed profile,Equalizer,Wireless,Computer science,Network packet,Computer network,Communication channel,Electronic engineering,Artificial intelligence,Deep learning,Underwater
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Youwen Zhang110.69
Junxuan Li210.35
Yuriy V. Zakharov319429.29
Jianghui Li453.80
Yingsong Li512034.72
Chuan Lin610.35
Xiang Li7566.55