期刊
IEEE ACCESS
卷 7, 期 -, 页码 38420-38430出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2906424
关键词
Channel equalization; deep learning; deep neural network; DFE; machine learning; single carrier communication; underwater acoustic network
资金
- National Natural Science Foundation of China [51609052, 61471138, 61531012, 50909029]
- China Scholarship Council Funding
- Program of International Science and Technology Cooperation [2013DFR20050]
- Defense Industrial Technology Development Program [B2420132004]
- Acoustic Science and Technology Laboratory in 2014
- U.K. Engineering and Physical Sciences Research Council [EP/P017975/1, EP/R003297/1]
- Fund of Acoustics Science and Technology Laboratory
- EPSRC [EP/R003297/1, EP/P017975/1] Funding Source: UKRI
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.
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