Channel Estimation for Visible Light Communications Using Neural Networks
This addresses the challenge of designing reliable VLC systems for communication technology, but it appears incremental as it applies neural networks to a known bottleneck in a specific domain.
The paper tackled the problem of channel estimation in visible light communications (VLC) by proposing a neural network-based methodology, with results showing that neural networks can be effectively trained to predict channel taps under different environmental conditions.
Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work, a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to train neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions.