Deep-learning-assisted communication capacity enhancement by non-orthogonal state recognition of structured light

Abstract

In light of pending capacity crunch in information era, orbital-angular-momenta-carrying vortex beams are gaining traction thanks to enlarged transmission capability. However, high-order beams are confronted with fundamental limits of nontrivial divergence or distortion, which consequently intensifies research on new optical states like low-order fractional vortex beams. Here, we experimentally demonstrate an alternative mean to increase the capacity by simultaneously utilizing multiple non-orthogonal states of structured light, challenging a prevailing view of using orthogonal states as information carriers. Specifically, six categories of beams are jointly recognized with accuracy of >99% by harnessing an adapted deep neural network, thus providing the targeted wide bandwidth. We then manifest the efficiency by sending/receiving a grayscale image in 256-ary mode encoding and shift keying schemes, respectively. Moreover, the well-trained model is able to realize high fidelity recognition (accuracy >0.8) onto structured beams under unknown turbulence and restricted receiver aperture size. To gain insights of the framework, we further interpret the network by revealing the contributions of intensity signals from different positions. This work holds potential in intelligence-assisted large-capacity and secure communications, meeting ever growing demand of daily information bandwidth.