A Post-Equalizer Based on Dual Self-Attention Network in UVLC System

• Main Authors: Fangxing Yuan, Jie Wang, Weixiang Yu, Jiuchun Ren
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Photonics Journal
• Subjects: Underwater visible light communication; dual self-attention network; post equalizer; time series analysis; deep learning
• Online Access: https://ieeexplore.ieee.org/document/9395215/
• ISSN: 1943-0655

The post-equalizer in the Underwater Visible Light Communication (UVLC) system can overcome the nonlinear distortion existing in the system. The existing nonlinear post-equalizer based on deep learning still has problems such as the number of data nodes has a great influence on the effect, the equalization effect decreases significantly when the data rate becomes higher and too complex a model leads to slow training time. In this paper, we propose a Dual Self-Attention Network (DSANet) as a post equalizer in the CAP modulated UVLC system. Experiments show that the DSANet-based post equalizer can achieve good equalization performance at different data rates; it shows strong robustness when the number of data nodes changes; its training speed is close to that of the plainest nonlinear post-equalizer.