概括
一个新的轻量级神经网络检测方案显著提高了接收器的灵敏度,并削减了光通信系统中的计算复杂性. 这种优化的检测可以提高带宽有限的强度调制和直接检测系统的性能.
科学领域:
- 光学通信是指光学通信.
- 数字信号处理 数字信号处理
- 机器学习在通信中的应用
背景情况:
- 带宽有限的强度调制和直接检测 (IM/DD) 系统在接收器数字信号处理 (DSP) 中面临挑战.
- 传统的优化检测方案经常表现出高的计算复杂性.
- 高效的接收器设计对于下一代高速光学网络至关重要.
研究的目的:
- 为IM/DD系统提出和评估一个轻量级和高效的优化检测方案.
- 为了提高接收器性能,利用轻量级增强神经网络 (LSE-NN).
- 为了减少接收器DSP的计算复杂性.
主要方法:
- 开发了一个基于LSE-NN的新型检测方案.
- 集成的输入预处理与2-点尺度增强低通过.
- 使用基于神经网络的查找表 (NN-LUT) 和基于神经网络的后期日志最大值 (MAP) 解码器 (NN-MAP).
- 在2公里的122Gbps光学IM/DD系统中实施和测试了该方案,该系统具有4级脉冲振幅调制 (PAM-4) 超过2公里.
主要成果:
- 在LSE-NN-MAP方案中,接收器灵敏度提高了2.4dB.
- 与传统方法相比,计算复杂性减少了98.44%以上.
- 该系统成功达到7%的预期错误校正 (FEC) 门.
结论:
- 拟议的LSE-NN-MAP方案为IM/DD系统中优化检测提供了一种轻量级和高效的解决方案.
- 这证明了轻量级神经网络架构的首次应用,用于IM/DD系统中优化检测.
- 结果突出了基于神经网络的DSP在提高光通信性能方面的潜力.
相关概念视频
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01:18Reducing Line Loss
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