Nonlinear Resilient Learning Method Based on Joint Time-Frequency Image Analysis in Underwater Visible Light Communication

Abstract

In this paper, we propose a novel nonlinear resilient learning post equalizer named TFDNet in UVLC system. Unlike the traditional deep neural network (DNN) based post equalizers which merely consider the time domain, the proposed TFDNet exploits time-frequency image analysis which considers the time and frequency domains simultaneously and transforms the signal into 2D time-frequency image, which is further learned by neural network. Experimental results demonstrate that TFDNet outperforms Volterra and DNN based methods for compensating nonlinear distortions through a 1.2 m underwater channel using 64 quadrature amplitude modulation-carrierless amplitude modulation (64QAM-CAP). Even under severe nonlinear distortions where Volterra and DNN cannot work, TFDNet retains valid bit error rate (BER) below the 7% forward error correction (FEC) limit of 3.8 × 10-3. The performance of TFDNet verifies the effectiveness of time-frequency image analysis which has been applied to tackle nonlinear distortions in UVLC system for the first time.

Keywords

• Underwater visible light communication
• post equalizer
• time-frequency image analysis
• nonlinear distortions
• neural network