增强基于人工神经网络的平衡性能,用于短距离光纤通信
Mahmoud M T Maghrabi1,2, Hariharan Swaminathan1, Shiva Kumar1
1Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种人工神经网络 (ANN) 均衡器,以减少短距离光纤系统的扭曲. 与传统方法相比,新型ANN等分器提供了更高的性能和更低的计算成本.
科学领域:
- 光学通信是指光学通信.
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 短距离光纤通信系统面临信号扭曲.
- 强度调制和直接检测 (IMDD) 系统被广泛使用,但容易受到损害.
- 现有的等式化方法,如MLSE可以是计算密集的.
研究的目的:
- 提出一种高效且易于实施的基于人工神经网络 (ANN) 的等分器.
- 为了减轻短距离IMDD光纤系统中的扭曲.
- 为了提高补偿性能,并减少等分器的计算复杂性.
主要方法:
- 开发一个单层人工神经网络 (ANN) 均衡器.
- 实施一项高级培训计划,以提高薪酬绩效.
- 对10和28Gbaud短距离光纤通信系统进行测试和验证.
主要成果:
- 拟议的ANN等分器显著改善了补偿性能.
- 该ANN等分器展示了优越的比特错误率 (BER) 性能.
- 与MLSE相比,计算成本和存储内存要求降低.
结论:
- 基于ANN的等分器是短距离光纤系统的高效和强大的解决方案.
- 高级培训计划提高了ANN等效器的有效性.
- 拟议的均衡器在性能和复杂性之间提供了比MLSE更好的权衡.
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