基于机器学习的可见光通信信号处理研究现状

随着无线通信领域的发展,具有诸多优点的可见光通信(VLC)已经发展成为了一种具有广阔前景的通信手段。然而,可见光通信中的各种非线性效应会给其信号处理带来诸多的困难,并恶化系统的性能。机器学习在解决非线性问题方面具有很大的优势和潜力,结合机器学习算法的可见光通信技术必然具有巨大的研究价值。已有研究表明,传统的机器学习算法如K-means、DBSCAN以及支持向量机(SVM)等在预均衡、后均衡、抗系统抖动,以及相位纠正等方面均有很好的表现。而深度神经网络(DNN)则因为其强大的非线性拟合能力能够更进一步提升VLC系统的性能。对以上几种方法进行了分析和介绍,并对其在可见光通信信号处理领域的应用进行了分析与总结,希望可以为机器学习解决可见光通信方面的各种非线性问题提供参考。

Research Status of Machine Learning Based Signal Processing in Visible Light Communication

With the development of wireless communication,visible light communication(VLC)has become very promising technology owing to its many advantages.However,the nonlinear effect of VLC introduces many challenges for signal processing and deteriorates system performance.As machine learning has many advantages and significant potential for solving nonlinearity issues,the VLC that utilizes machine learning algorithms is bound to have tremendous research value.Existing research shows that traditional machine learning algorithms,such as Kmeans,DBSCAN,and support vector machine,perform well in pre-equalization,post-equalization,anti-system jitter,and phase correction.A deep neural network can further improve the performance of the VLC system because of its strong nonlinear fitting ability.In this article,we analyze the aforementioned methods and introduce their application to the signal processing in VLC.We hope this paper provides a reference for solving the nonlinearity problems related to machine learning in VLC.