Hybrid CNN-Swin Transformer denoising network for channel estimation in Optical IRS-assisted indoor MIMO VLC system

Optical Intelligent Reflective Surface (OIRS)-assisted MIMO VLC offers high-capacity communication but struggles with accurate channel estimation due to multipath and noise. We propose a hybrid CNN–Swin Transformer denoising network (HCSTNet), integrating a CNN-based noise estimator with a Swin Transformer bottleneck to learn local features and global spatial dependencies jointly. In a realistic indoor MIMO VLC setup with LOS, NLOS, and OIRS links, HCSTNet achieves an NMSE of 1 0 − 3 – 1 0 − 4 and outperforms existing models in terms of PSNR while also reducing the parameter count and inference time. These results demonstrate the efficiency and robustness of HCSTNet for practical VLC applications.