概括
本研究引入了一种新的深度学习方法,使用双剩余特征解网络 (DRFDnet) 来改进高速通信系统. 在DRFDnet有效地减轻信号损害的强度调制/直接检测轨道角动量模式分离多重复合 (IM/DD OAM-MDM) 系统.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 强度调制/直接检测 (IM/DD) 轨道角动量 (OAM) 模式分割复杂化 (MDM) 为下一代被动光学网络 (PON) 提供高容量.
- 现有的IM/DD OAM-MDM系统面临挑战,包括模式合,非线性损害和量子化噪声,特别是低分辨率的数字对模拟转换器 (DAC).
研究的目的:
- 提出和验证一个新的端到端 (E2E) 学习方案,以减轻IM/DD OAM-MDM系统中的信号损害.
- 开发一个DRFDnet模拟器,能够准确地模拟OAM-MDM系统中的复杂非线性.
- 通过使用DRFDnet实现联合概率塑造 (PS) 和噪声塑造 (NS) 以提高信号补偿.
主要方法:
- 开发一个双剩余特征脱网络 (DRFDnet) 模拟器,单独模拟线性和非线性信号损害.
- 实施基于DRFDnet的E2E学习方案,集成联合的概率塑造 (PS) 和噪音塑造 (NS).
- 在使用IM/DD OAM-MDM传输的200 Gbit/s PON系统上进行实验验证.
主要成果:
- 拟议的基于DRFDnet的联合PS和NS计划有效地减轻IM/DD OAM-MDM系统中的非线性扭曲.
- 该方案在硬决策前向错误纠正 (HD-FEC) 值下,相对于基于CGAN和传统的PS/NS联合方案,实现了1.2dBm和2.5dBm的接收器灵敏度改进.
- DRFDnet模拟器准确地模拟了OAM-MDM系统的复杂非线性行为.
结论:
- 基于DRFDnet的E2E学习方案与联合PS和NS是提高IM/DD OAM-MDM通信系统性能的一个有希望的解决方案.
- 这种方法有效地弥补信号损害,优于现有方法.
- 基于DRFDnet模拟器的E2E学习方案为未来的高速PON部署提供了可行的候选人.
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