Traditional soft or hard decisions ignoring the correlation between symbols removing linear and nonlinear damage directly may lead to lack of information and result in system performance degradation. In order to reduce the performance degradation of the system caused by the lack of information, we propose a method that use the Gaussian mixed model to consider the influence of the correlation between consecutive symbols for underwater visible light communication system. The experimental results show that the performance of the system is greatly improved after the correlation between consecutive symbols is considered. The more consecutive symbols there are, the greater the performance will be. When the forward error correction (FEC) threshold is met, the Q factor of Gaussian mixture models(GMM) that join three adjacent symbols is 1.19 dB greater than without the GMM clustering algorithm. The highest data rate that can be achieved exceeds 1.5 Gbps with GMM.

中文翻译：

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高斯混合模型在PAM8水下可见光系统通信中解决符号间干扰的应用

传统的软或硬决策忽略符号之间的相关性直接去除线性和非线性损伤可能会导致信息缺乏并导致系统性能下降。为了减少信息缺失导致系统性能下降，我们提出了一种使用高斯混合模型来考虑水下可见光通信系统连续符号间相关性影响的方法。实验结果表明，考虑到连续符号之间的相关性后，系统的性能得到了很大的提高。连续的符号越多，性能就越好。当满足前向纠错 (FEC) 阈值时，加入三个相邻符号的高斯混合模型 (GMM) 的 Q 因子为 1。比不使用 GMM 聚类算法时高 19 dB。使用 GMM 可实现的最高数据速率超过 1.5 Gbps。