基于贝叶斯生成对抗网络模拟器的端到端学习策略,用于OAM模式的概率塑造,OAM模式的划分,IM/DD传输的多重化
Optics express
|December 2, 2023
概括
本研究介绍了贝叶斯生成对抗网络 (BGAN) 模拟器,用于增强光纤通信容量,使用轨道角动量 (OAM) 模式分割复杂化 (MDM) 和概率塑造 (PS). 新方法提高了OAM-MDM系统中的建模精度和数据传输效率.
科学领域:
- 光学通信是指光学通信.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 轨道角动量 (OAM) 模式分割复杂化 (MDM) 提供了更大的传输能力.
- 概率造型 (PS) 优化了在香农极限附近的容量.
- 由于模式合和非线性损伤,现有的PS不适合OAM-MDM强度调制直接检测 (IM-DD).
研究的目的:
- 为OAM-MDM IM/DD系统提出贝叶斯生成对抗网络 (BGAN) 模拟器.
- 开发一个端到端 (E2E) 学习策略,以在OAM-MDM中获得最佳的PS.
- 为了解决OAM-MDM传输中传统方法的局限性.
主要方法:
- 实现了一个BGAN模拟器,使用权重和偏差作为概率分布,以匹配OAM-MDM非线性模型.
- 利用E2E学习策略来找到最佳的PS概率分布.
- 在5公里的环芯光纤上使用200Gbit/s的OAM-MDM CAP-32信号进行了实验.
主要成果:
- 与传统CGAN相比,BGAN模拟器表现出更高的建模精度 (29.3%和26.3%的两个OAM模式的改进).
- 通过E2E学习策略,实现了比MB分发和CGAN.更高的泛化相互信息 (GMI).
- 实验结果证实了BGAN模拟器和E2E策略的有效性.
结论:
- 建议使用BGAN模拟器的E2E学习策略对OAM-MDM IM/DD系统有效.
- 这种方法提高了传输能力,克服了现有的PS技术的局限性.
- 对于未来的高容量光纤通信,BGAN模拟器显示出显著的前景.
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