SCI-D$^2$NN: An Optimization Framework for OAM-Multiplexed FSO Communications

Abstract

Orbital angular momentum (OAM) multiplexing can increase the capacity of free-space optical (FSO) communications, but its detection performance is strongly affected by impairments such as atmospheric turbulence, transmitter pointing errors, and photodetection noise. The diffractive deep neural network (D$^2$NN) can be used as an all-optical front end to mitigate turbulence-induced distortions before detection. However, existing D$^2$NN compensation schemes are not specifically optimized for communication detection. In this paper, we propose a supervised contrastive inspired D$^2$NN (SCI-D$^2$NN) framework for improving the detection performance of OAM-multiplexed FSO communications under these impairments. The proposed framework introduces two training branches: a projection branch that maps the optical field to low-dimensional decision domain samples, and a label branch that provides supervised labels to impose a separation constraint among decision domain samples. In addition, we characterize complex-amplitude crosstalk to obtain the receiver observation vector and formulate two detection schemes, namely single-port profile-likelihood detection and joint maximum-likelihood (ML) detection. We further design two SCI-D$^2$NN training losses called Bhattacharyya distance (BD) based loss and the ML based loss to improve decision domain separability and mitigate detection-performance degradation. Numerical results show that SCI-D$^2$NN achieves more than a 3-dB improvement in bit error rate (BER) over the conventional D$^2$NN baseline in most transmit-power regions. The BD based loss gives the lowest BER under different system parameters and provides more than a 10-dB BER improvement over the baseline in the high transmit power region.