A deep-learning approach for turbulence correction in free space optical communication with Laguerre–Gaussian modes

Abstract

Free space optical communication has become increasingly popular in the past decade due to its terahertz bandwidth, unlicensed spectrum, and enhanced security features. This technology has been attracting significant interest from the network community due to its broad applications in future military and civilian protocols. Recently, the use of orthogonal spatial states of the light field, including those with nonzero orbital angular momentum, has increased communication system capacities and bit-transfer rates per photon. However, atmospheric turbulence disrupts the orthogonal spatial modes over propagation. We present a novel deep learning architecture, cGULnet, to mitigate the effects of atmospheric turbulence on spatial multiplexing of Laguerre–Gaussian structured light modes. The model predicts the phase correction patterns required for an adaptive optical system. It is a U-shaped architecture trained adversarially to a patch discriminator similar to conditional generative adversarial neural-networks. The network is trained for exact phase recovery systems without approximating Zernike polynomials, enabling its use in highly phase-sensitive but high-speed spatial multiplexed communication systems. The proposed architecture can predict the successive three frames in a single shot using the previous ten frames. The method was evaluated for a wide range of beam waist sizes in a simulated Laguerre–Gaussian spatial mode multiplexed 1 km free space optical communication link. We have considered 20 dB optical signal-to-noise ratio, with turbulence conditions parameterized by the refractive index structure parameter of Cn2≤5×10−15m−23. It was found to reduce the bit error rate to the International Telecommunication Union recommended forward error correction limit of 3.8×10−3 under high turbulence conditions.