A Novel Machine Learning Algorithm With Mathematical Modeling for Channel Estimation in VLC Systems

In visible light communications (VLC), signal-dependent shot noise (SDSN) significantly impacts the accuracy of channel estimation. This work introduces a simple yet novel machine learning-based estimator to overcome this challenge. A mathematical framework for the proposed estimator is developed, enabling the computation of its mean square error (MSE). Results show that, in the presence of SDSN, the proposed estimator outperforms the traditional least squares estimator (LS). In the ideal scenario without SDSN, the proposed algorithm remains slightly more efficient and accurate.