Transfer Learning–Based Artificial Neural Networks Post-Equalizers for Underwater Visible Light Communication

Abstract

In this article, we demonstrate two transfer learning–based dual-branch multilayer perceptron post-equalizers (TL-DBMLPs) in carrierless amplitude and phase (CAP) modulation-based underwater visible light communication (UVLC) system. The transfer learning algorithm could reduce the dependence of artificial neural networks (ANN)–based post-equalizer on big data and extended training cycles. Compared with DBMLP, the TL-DBMLP is more robust to the jitter of the bias current (Ibias) of light-emitting diode (LED), which indicates that TL-DBMLP does not require further training in Ibias varying UVLC system. In terms of voltage peak-to-peak (Vpp) varying VLC system, DBMLP requires a training set with a size of more than 105 and 50 training epochs, which quantitatively prove the effectiveness of DBMLP in reducing reliance on large amount of training epochs. On the counterpart, the TL-DBMLP only requires a training set with a size of less than 2×104 and 10 training epochs, which quantitatively prove the effectiveness of DBMLP in reducing reliance on big data. Finally, we experimentally demonstrate that transfer learning can effectively reduce ANN dependence on extensive size training data and large amount of training epochs, whether in VLC systems with varying Ibias and varying Vpp.