Free-space optical systems are emerging for high data rate communication and transfer of information in indoor and outdoor settings. However, free-space optical communication becomes challenging when an occlusion blocks the light path. Here, we demonstrate, for the first time, a direct communication scheme, passing optical information around a fully opaque, arbitrarily shaped obstacle that partially or entirely occludes the transmitter's field-of-view. In this scheme, an electronic neural network encoder and a diffractive optical network decoder are jointly trained using deep learning to transfer the optical information or message of interest around the opaque occlusion of an arbitrary shape. The diffractive decoder comprises successive spatially-engineered passive surfaces that process optical information through light-matter interactions. Following its training, the encoder-decoder pair can communicate any arbitrary optical information around opaque occlusions, where information decoding occurs at the speed of light propagation. For occlusions that change their size and/or shape as a function of time, the encoder neural network can be retrained to successfully communicate with the existing diffractive decoder, without changing the physical layer(s) already deployed. We also validate this framework experimentally in the terahertz spectrum using a 3D-printed diffractive decoder to communicate around a fully opaque occlusion. Scalable for operation in any wavelength regime, this scheme could be particularly useful in emerging high data-rate free-space communication systems.
翻译:自由空间光学系统正在出现用于高数据率的通信和室内外信息传输。然而,当遮挡物阻挡光线时,自由空间光学通信变得具有挑战性。在这里,我们首次演示了一种直接通信方案,它能够通过完全不透明的任意形状的障碍物,传递所需信息的光学信息。在此方案中,使用深度学习联合训练了电子神经网络编码器和衍射光学网络解码器以传输光学信息或感兴趣的信息。 衍射解码器包括连续的空间设计被动表面,通过光物相互作用处理光学信息。训练后,编码器-解码器对可以在任意不透明的遮挡物周围通信任何任意光学信息,其中解码信息的发生速率与光传播速度相同。 对于随时间变换大小和/或形状的遮挡物,编码器神经网络可以被重新训练,以成功地与现有衍射解码器进行通信,而不改变已经部署的物理层。我们还在太赫兹光谱中使用3D打印的衍射解码器验证了这个框架的实验。该方案可扩展到任何波长范围的操作,特别是对于新兴的高数据率自由空间通信系统来说会很有用。