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逐步应用于室内外场景中的高数据率通信与信息传输。然而，当遮挡物阻断光路时，自由空间光学通信将面临挑战。本研究首次提出一种直接通信方案，可使光学信息绕过完全遮挡视场的任意形状不透明障碍物传递。在该方案中，电子神经网络编码器与衍射光学网络解码器通过深度学习联合训练，实现目标光学信息或报文绕过任意形状不透明遮挡物的传输。衍射解码器由一系列经过空间设计的无源表面构成，通过光-物质相互作用处理光学信息。训练完成后，编码器-解码器对能够以光速传播的速度解码信息，实现任意光学信息绕过不透明遮挡物的通信。针对尺寸或形状随时间变化的动态遮挡物，仅需重新训练编码器神经网络即可与现有衍射解码器协同通信，无需修改已部署的物理层。我们通过三维打印衍射解码器在太赫兹波段开展实验验证，成功实现了绕过完全遮挡物的通信。该方案可扩展至任意波长范围，对新兴的高数据率自由空间通信系统具有重要应用价值。