The integration of CNNs with Swin Transformers creates a hybrid model capable of capturing both local spatial features and global contextual information, enabling highly accurate channel estimation and signal reconstruction for indoor VLC systems.
Enhanced MIMO Performance for High-Speed Optical Links
By leveraging the hybrid architecture, indoor MIMO VLC systems achieve significantly improved data throughput, reduced interference, and greater reliability, even in dense multi-device environments.
Robustness Against Noise and Environmental Variations
The deep learning–driven design enhances resistance to real-world distortions such as ambient light noise, reflection-induced fading, and device movement, ensuring stable and consistent communication indoors.
Energy-Efficient Communication for Smart Indoor Spaces
The optimized model architecture ensures low computational overhead while maintaining high performance, making it ideal for integration into smart homes, IoT networks, and energy-conscious indoor infrastructures.
Paving the Way for Next-Generation Optical Wireless Systems
This breakthrough hybrid framework lays the foundation for future VLC advancements, offering the potential for ultra-fast, secure, and interference-free indoor communication systems that surpass traditional RF technologies.
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