Affiliation:
1. Peking University
2. Sun Yat-sen University
3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
4. Peng Cheng Laboratory (PCL)
Abstract
Visible light communication (VLC) has emerged as a promising technology
for future sixth-generation (6 G) communications. Estimating and
predicting the impairments, such as turbulence and free space signal
scattering, can help to construct flexible and adaptive VLC networks.
However, the monitoring of impairments of VLC is still in its infancy.
In this Letter, we experimentally demonstrate a
deep-neural-network-based signal-to-noise ratio (SNR) estimation
scheme for VLC networks. A vision transformer (ViT) is first utilized
and compared with the conventional scheme based on a convolutional
neural network (CNN). Experimental results show that the ViT-based
scheme exhibits robust performance in SNR estimation for VLC networks
compared to the CNN-based scheme. Specifically, the ViT-based scheme
can achieve accuracies of 76%, 63.33%, 45.33%, and 37.67% for
2-quadrature amplitude modulation (2QAM), 4QAM, 8QAM, and 16QAM,
respectively, against 65%, 57.67%, 41.67%, and 34.33% for the
CNN-based scheme. Additionally, data augmentation has been employed
for achieving enhanced SNR estimation accuracies of 95%, 79.67%,
58.33%, and 50.33% for 2QAM, 4QAM, 8QAM, and 16QAM, respectively. The
effect of the SNR step size of a contour stellar image dataset on the
SNR estimation accuracy is also studied.
Funder
Shenzhen Municipal Science and Technology
Innovation Council
Tsinghua Shenzhen International Graduate
School
Subject
Atomic and Molecular Physics, and Optics