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Convolution-Enhanced LSTM Neural Network Post-Equalizer used in Probabilistic Shaped Underwater VLC System

We proposed a convolution-enhanced long shortterm memory (CE-LSTM) neural network as the equalizer to overcome severe linear and nonlinear distortion in underwater visible light communication (UVLC) system with probabilistic shaping technologies. By comparing the proposed equalizer with the traditional Volterra-series based equalizer and normal LSTM-based equalizer, we demonstrated the efficiency and performance advantages of using a CE-LSTM based equalizer. To the best of our knowledge, this is the first practice to combine convolution network with LSTM in equalization scheme of UVLC and the highest achievable information rate (AIR) of 3.53 Gbit/s is achieved for single LED-based UVLC with PS.