Wavefront shaping for multi-user line-of-sight and non-line-of-sight visible light communication

Non-line-of-sight optical wireless communication system enabled by wavefront shaping for multi-user indoor access

In this Letter, we experimentally investigate a non-line-of-sight (NLOS) optical wireless communication (OWC) system that utilizes wavefront shaping techniques to realize simultaneous data transmission for multiple users. Wavefront shaping techniques are employed to address the issue of low intensity of diffusely reflected light at the receiver in NLOS scenarios for indoor high-speed access. To achieve communication path planning and tracing for two different users in free-space optical communication, the pixels of the spatial light modulator (SLM) are divided into two halves to separately manipulate the wavefront of two independent data carriers centered at different wavelengths. The maximum received optical power can be effectively improved by more than 15 dB with the wavefront shaping technique. To avoid power enhancement of non-target wavelength, the wavelength difference of two different users is experimentally studied. The difference in power enhancement ratio (DPER) is increased with the wavelength difference, and 14.95 dB DPER is obtained with a 10 nm wavelength difference. Under the aforementioned wavelength planning strategy, successful transmission and reception of 2 × 160 Gbit/s 16-QAM signals for two users with coherent detection is achieved using wavelengths of 1550 and 1560 nm in an indoor access scenario.

Feature decoupled knowledge distillation enabled lightweight image transmission through multimode fibers

Multimode fibers (MMF) show tremendous potential in transmitting high-capacity spatial information. However, the quality of multimode transmission is quite sensitive to inherent scattering characteristics of MMF and almost inevitable external perturbations. Previous research has shown that deep learning may break through this limitation, while deep neural networks are intricately designed with huge computational complexity. In this study, we propose a novel feature decoupled knowledge distillation (KD) framework for lightweight image transmission through MMF. In this framework, the frequency-principle-inspired feature decoupled module significantly improves image transmission quality and the lightweight student model can reach the performance of the sophisticated teacher model through KD. This work represents the first effort, to the best of our knowledge, that successfully applies a KD-based framework for image transmission through scattering media. Experimental results demonstrate that even with up to 93.4% reduction in model computational complexity, we can still achieve averaged Structure Similarity Index Measure (SSIM) of 0.76, 0.85, and 0.90 in Fashion-MNIST, EMNIST, and MNIST images respectively, which are very close to the performance of cumbersome teacher models. This work dramatically reduces the complexity of high-fidelity image transmission through MMF and holds broad prospects for applications in resource-constrained environments and hardware implementations.