发表机构
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Convergent Communications and Networking Technologies in LEO Satellite Systems; School of Artificial Intelligence and Computer Science, North China University of Technology; Department of Physics, Capital Normal University; School of Optics and Photonics, Beijing Institute of Technology; angtze Delta Region Academy of Beijing Institute of Technology（北京邮电大学网络与交换技术国家重点实验室; 低轨卫星系统融合通信与网络技术北京市重点实验室; 华北理工大学人工智能与计算机学院; 首都师范大学物理系; 北京理工大学光电学院; 北京理工大学长三角研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对OAM复用FSO通信受多种损伤影响的问题，提出SCI-D$^2$NN优化框架，设计两种训练分支与损失，使BER较传统D$^2$NN实现显著提升。
AI 中文摘要
轨道角动量（OAM）复用可提升自由空间光（FSO）通信的容量，但其检测性能受大气湍流、发射端指向误差、光电检测噪声等损伤的强烈影响。衍射深度神经网络（D$^2$NN）可作为全光前端，用于在检测前缓解湍流引起的畸变。然而，现有D$^2$NN补偿方案未针对通信检测进行专门优化。本文提出一种受监督对比启发的D$^2$NN（SCI-D$^2$NN）框架，用于提升上述损伤下OAM复用FSO通信的检测性能。该框架引入两个训练分支：将光场映射到低维决策域样本的投影分支，以及提供监督标签以施加决策域样本间分离约束的标签分支。此外，本文表征复振幅串扰以获取接收端观测向量，并制定两种检测方案：单端口轮廓似然检测和联合最大似然（ML）检测。本文进一步设计两种SCI-D$^2$NN训练损失，即基于Bhattacharyya距离（BD）的损失和基于ML的损失，以提升决策域可分性并缓解检测性能下降。数值结果表明，在大多数发射功率区域，SCI-D$^2$NN的误码率（BER）较传统D$^2$NN基准实现超过3 dB的提升；基于BD的损失在不同系统参数下实现最低BER，且在高发射功率区域较基准提供超过10 dB的BER提升。
英文摘要
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.
Comments29 pages, 8 figures. This manuscript is currently under peer review
