Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the co...

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Vydáno v:IEEE access Ročník 7; s. 23579 - 23594
Hlavní autoři: Jiang, Rongkun, Wang, Xuetian, Cao, Shan, Zhao, Jiafei, Li, Xiaoran
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acoustic noise
Algorithms
Artificial neural networks
Back propagation networks
Biological neural networks
Bit error rate
Channel estimation
Channels
Communications systems
Deep neural networks
Mean square errors
Modulation
Multipath channels
Neural networks
OFDM
OFDM systems
Orthogonal Frequency Division Multiplexing
Training
underwater acoustic communication
Underwater acoustics
Underwater communication
ISSN:2169-3536, 2169-3536
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Shrnutí:Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2899990