Nonlinear Compensation Based on Bidirectional Temporal Convolutional Network in Underwater Visible Light Communication System

In this work, we proposed a Bidirectional Temporal Convolutional network (BiTCN) model to process signal post-equalization in underwater visible light communication channel (UVLC) for the first time. This strategy is more resistant to nonlinearities compared to traditional equalizers like least mean square (LMS) equalizer and avoids the inefficiency issue caused by the sequential processing requirement of Bidirectional Gate Recurrent Unit (BiGRU) networks, through this approach, we successfully conduct a transmission of a 64quadrature amplitude modulation-carrierless amplitude (64QAM-CAP) signal in a 1.2m underwater experiment using a blue laser operating at a wavelength of 450 nm. Remarkably, our system achieves a high data rate of 16.2 Gbps, all while maintaining the bit error rate (BER) below the 7% hard decision forward error correction (HD-FEC) threshold of 3.8E-3. Under optimal Peak-to-Peak Voltages (VPP) and bias current conditions, compared to the post equalization methods of LMS+Volterra and BiGRU, our proposed method achieved speed improvements of 550 and 210 Mbps, respectively.