Channel estimation-based time-frequency neural network for post-equalization in underwater visible light communication

This Letter proposes a post-equalizer for underwater visible light communication(UVLC) systems that combines channel estimation and joint time-frequency analysis, named channel-estimation-based bandpass variable-order time-frequency network(CBV-TFNet). By utilizing a bandpass variable-order loss function with communication prior knowledge, CBVTFNet enhances communication performance and training stability. It enables lightweight implementation and faster convergence through a channel estimation-based mask. The superior performance of the proposed equalization method over Volterra and deep neural network(DNN)-based methods has been studied experimentally. Using bit-power loading discrete multitone (DMT) modulation, the proposed method achieves a transmission bitrate of 4.956 Gbps through a 1.2 m underwater channel utilizing only 38.15% of real multiplication calculations compared to the DNN equalizer and achieving a bitrate gain of440 Mbps and a significantly larger dynamic range over the LMS-Volterra equalizer. Results highlight CBV-TFNet's potential for future post-equalization in UVLC systems.

MISO visible light communication system utilizing hybrid post-equalizer aided pre-convergence of STBC decoding

In this Letter, we propose a modified hybrid linear and nonlinear post-equalizer to aid pre-convergence of space–time block coding（STBC） decoding in the formulated multiple-input-single-output（MISO） visible light communication（VLC） model. Meanwhile, we present a channel estimation algorithm, as the existing method is suboptimal. Experiments demonstrate that a data rate of 1 Gbit/s is easily achieved in 1.3 m indoor free space transmission with the bit error rate（BER） limited to 3.8 × 10^-3. Correspondingly, the Q factor can be improved to 3.13 d B compared to the pure linear equalizer case.