Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
نویسندگان
چکیده
Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe fading. The receiver has to recover distorted OTFS signal with inter-symbol interference (ISI) inter-carrier (ICI). conventional UWA receivers perform channel estimation explicitly equalization detect transmitted symbols, requires prior knowledge of system. This paper proposes deep learning-based detection method for in neural network can received symbols after sufficient training. particular, it cascades convolutional (CNN) skip connections (SC) bidirectional long short-term memory (BiLSTM) recovery. proposed extracts feature information from sequences trains detection. numerical results demonstrate SC-CNN-BiLSTM-based performs lower bit error rate (BER) 2D-CNN, FC-DNN, methods.
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ژورنال
عنوان ژورنال: Journal of Marine Science and Engineering
سال: 2022
ISSN: ['2077-1312']
DOI: https://doi.org/10.3390/jmse10121920