SCI-D 2 NN: An Optimization Framework for OAM-Multiplexed FSO Communications
Year: 2026
Type: article
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 (D2NN) can be used as an all-optical front end to mitigate turbulence-induced distortions before detection. However, existing D2NN compensation schemes are not specifically optimized for communication detection. In this paper, we propose a supervised contrastive inspired D2NN (SCI-D2NN) 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-D2NN 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-D2NN achieves more than a 3-dB improvement in bit error rate (BER) over the conventional D2NN 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.
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W7223083151