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.
Voice over Internet Protocol (VoIP) is a technology that transports voice data packets across packet switched networks using the Internet Protocol (IP).However, the current Internet, which was originally designed for data communications, provides best-effort service only, and does not guarantee to transmission quality. As known, packet delay dramatically degrades the quality of VoIP calls. It is widely accepted the network was self-similar. The degree of self-similarity can be expressed by the Hurst parameter H (0.5
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