| Autor: |
Fangxing Yuan, Jie Wang, Weixiang Yu, Jiuchun Ren |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Photonics Journal, Vol 13, Iss 2, Pp 1-11 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1943-0655 |
| DOI: |
10.1109/JPHOT.2021.3071008 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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