Work

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

Year: 2025

Type: article

Abstract: 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.

Source: ICT Express

DOI: 10.1016/j.icte.2025.11.006

Authors: Rafat Bin Mofidul, Al Imran, Md. Shahriar Nazim, Yeong Min Jang

Institution: Kookmin University

Language: English

Topic: Optical Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: National Research Foundation of Korea, Ministry of Science and ICT, South Korea