Symmetry of constellation diagram-based intelligent SNR estimation for visible light communications

Abstract

Visible light communication (VLC) technology with rich spectrum resources is thought of as an essential component in the future ubiquitous communication networks. Accurately monitoring its transmission impairments is important for improving the stability of high-speed communication networks. Existing research on intelligently monitoring the signal-to-noise ratio (SNR) performance of VLC focuses primarily on the application of neural networks but neglects the physical nature of communication systems. In this work, we propose an intelligent SNR estimation scheme for VLC systems, which is based on the symmetry of constellation diagrams with classical deep learning frameworks. In order to increase the accuracy of the SNR estimation scheme, we introduce two data augmentation methods (DA): point normalization and quadrant normalization. The results of extensive simulations demonstrate that the proposed point normalization method is capable of improving accuracy by about 5, 10, 14, and 26%, respectively, for 16-, 64-, 256-, and 1024-quadrature amplitude modulation compared with the same network frameworks without DA. The effect of accuracy improvement can be further superimposed with traditional DA methods. Additionally, the extensive number of constellation points (e.g., 32, 64, 128, 256, 512, 1024, and 2048) on the accuracy of SNR estimation is also investigated.