Xu Qi, Gao Ran, Chang Huan, Li Zhipei, Wang Fei, Cui Yi, Liu Jie, Guo Dong, Pan Xiaolong, Zhu Lei, Zhang Qi, Tian Qinghua, Huang Xin, Yan Jinghao, Jiang Lin, Xin Xiangjun
Opt Express. 2023 Nov 20;31(24):40508-40524. doi: 10.1364/OE.502563.
Orbital angular momentum (OAM) mode division multiplexing (MDM) has emerged as a new multiplexing technology that can significantly increase transmission capacity. In addition, probabilistic shaping (PS) is a well-established technique that can increase the transmission capacity of an optical fiber to close to the Shannon limit. However, both the mode coupling and the nonlinear impairment lead to a considerable gap between the OAM-MDM channel and the conventional additive white Gaussian noise (AWGN) channel, meaning that existing PS technology is not suitable for an OAM-MDM intensity-modulation direct-detection (IM-DD) system. In this paper, we propose a Bayesian generative adversarial network (BGAN) emulator based on an end-to-end (E2E) learning strategy with probabilistic shaping (PS) for an OAM-MDM IM/DD transmission with two modes. The weights and biases of the BGAN emulator are treated as a probability distribution, which can be accurately matched to the stochastic nonlinear model of OAM-MDM. Furthermore, a BGAN emulator based on an E2E learning strategy is proposed to find the optimal probability distribution of PS for an OAM-MDM IM/DD system. An experiment was conducted on a 200 Gbit/s two OAM modes carrierless amplitude phase-32(CAP-32) signal over a 5 km ring-core fiber transmission, and the results showed that the proposed BGAN emulator outperformed a conventional CGAN emulator, with improvements in modelling accuracy of 29.3% and 26.3% for the two OAM modes, respectively. Moreover, the generalized mutual information (GMI) of the proposed E2E learning strategy outperformed the conventional MB distribution and the CGAN emulator by 0.31 and 0.33 bits/symbol and 0.16 and 0.2 bits/symbol for the two OAM modes, respectively. Our experimental results demonstrate that the proposed E2E learning strategy with the BGAN emulator is a promising candidate for OAM-MDM IM/DD optic fiber communication.
轨道角动量(OAM)模式分割复用(MDM)已成为一种能够显著提高传输容量的新型复用技术。此外,概率整形(PS)是一种成熟的技术,它可以将光纤的传输容量提高到接近香农极限。然而,模式耦合和非线性损伤导致OAM-MDM信道与传统加性高斯白噪声(AWGN)信道之间存在相当大的差距,这意味着现有的PS技术不适用于OAM-MDM强度调制直接检测(IM-DD)系统。在本文中,我们提出了一种基于端到端(E2E)学习策略和概率整形(PS)的贝叶斯生成对抗网络(BGAN)模拟器,用于具有两种模式的OAM-MDM IM/DD传输。BGAN模拟器的权重和偏差被视为概率分布,它可以精确匹配OAM-MDM的随机非线性模型。此外,还提出了一种基于E2E学习策略的BGAN模拟器,以找到OAM-MDM IM/DD系统的PS最优概率分布。在5公里环形芯光纤传输上对200 Gbit/s的两个OAM模式无载波幅度相位-32(CAP-32)信号进行了实验,结果表明,所提出的BGAN模拟器优于传统的CGAN模拟器,两种OAM模式的建模精度分别提高了29.3%和26.3%。此外,所提出的E2E学习策略的广义互信息(GMI)在两种OAM模式下分别比传统的MB分布和CGAN模拟器高出0.31和0.33比特/符号以及0.16和0.2比特/符号。我们的实验结果表明,所提出的带有BGAN模拟器的E2E学习策略是OAM-MDM IM/DD光纤通信的一个有前途的候选方案。