2022
DOI: 10.3390/s22228992
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LSTM Attention Neural-Network-Based Signal Detection for Hybrid Modulated Faster-Than-Nyquist Optical Wireless Communications

Abstract: In order to improve the accuracy of signal recovery after transmitting over atmospheric turbulence channel, a deep-learning-based signal detection method is proposed for a faster-than-Nyquist (FTN) hybrid modulated optical wireless communication (OWC) system. It takes advantage of the long short-term memory (LSTM) network in the recurrent neural network (RNN) to alleviate the interdependence problem of adjacent symbols. Moreover, an LSTM attention decoder is constructed by employing the attention mechanism, wh… Show more

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Cited by 4 publications
(1 citation statement)
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“…The attention mechanism in LSTM allows the model to focus on the most important inputs at each time step when making predictions. This can improve the model’s performance and stability compared to traditional LSTM models [ 41 ].…”
Section: Methodsmentioning
confidence: 99%
“…The attention mechanism in LSTM allows the model to focus on the most important inputs at each time step when making predictions. This can improve the model’s performance and stability compared to traditional LSTM models [ 41 ].…”
Section: Methodsmentioning
confidence: 99%