Underwater visible light communication (UVLC) has the advantages of high speed, low latency, and high confidentiality. However, the signal transmission is susceptible to light-emitting diode (LED) modulation bandwidth, non-linear effects of LEDs, and underwater channels. Neural networks, capable of addressing complex nonlinear problems, are increasingly applied to signal equalization in visible light communication. It is found that multilayer perceptron (MLP) is capable of extracting spectral features and nonlinear relationships, and gated recurrent unit (GRU) is capable of handling timing correlation and channel fading problems. Therefore, we propose a GRU-MLP model as a post-equalizer for the UVLC system and experiment using orthogonal frequency division multiplexing modulated signals on a 60-cm underwater experimental platform. The results show that the GRU-MLP equalizer can extend the system transmission bandwidth by 47% higher than the bidirectional gated recurrent unit (BIGRU) and long short-term memory (LSTM) equalizer when only limited by the LED bandwidth; under the influence of the underwater optical channel, the performance of GRU-MLP is similar to that of BIGRU and LSTM. The bit error rate of the GRU-MLP algorithm is significantly lower than other algorithms under the combined effect of two factors. In summary, GRU-MLP demonstrates superior equalization performance in bandwidth-constrained complex channel environments.
We provide an experimental study of the channel characteristics in an underwater wireless optical communication (UWOC) system given by a 35-m transmission distance in various water types. A UWOC system using a low-power 488-nm laser diode is established to comprehensively evaluate the influence of the link distance, water turbidity, receiver parameters, and link misalignment on the communication link power. The results show that the link misalignment-induced power loss is significant and the effect of receiver parameters on power is limited. However, a higher turbidity or longer signal transmission distance can help to reduce the link misalignment effect and manifest the receiver parameters effect on the received power. In particular, in turbid waters with an attenuation coefficient of 0.471 m − 1, the change of the signal reception power curve is small over a certain degree of the link misalignment in the 35-m physical distance, and the reasonable configuration of receiver parameters effectively improves the signal reception quality in the UWOC system. These results can provide theoretical guidance for optimizing the UWOC system.
In order to solve the problem of quality degradation of underwater image due to absorption and scattering of water body, this paper proposes a method of underwater image enhancement based on the combination of computational imaging and deep learning. The method has achieved good results in removing image blur and scattering noise. It can effectively enhance the target images in turbid water, which will allow underwater image applications to have a wider range of areas.
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