线性浅层神经网络加速发射机色散眼闭合四次方（TDECQ）评估

IF 7.6 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Science China Information Sciences Pub Date : 2024-03-21 DOI:10.1007/s11432-023-3947-8

Junjiang Xiang, Zejun Chen, Yijun Cheng, Hailin Yang, Xuancheng Huo, Meng Xiang, Gai Zhou, Yuwen Qin, Songnian Fu

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引用次数: 0

摘要

我们展示了一种基于 L-SNN 的数据驱动 TDECQ 评估方案。与现有的基于 DL 的方案相比，所提出的 L-SNN 计算复杂度最低，只有 210 次乘法运算。经实验验证，在 1.5-4.0 dB 的 TDECQ 范围内，L-SNN 方案对 25 和 50 Gbaud PAM-4 光信号的 MAE 分别为 0.13 和 0.15 dB，达到了 IEEE 标准推荐的 0.25 dB 精度阈值。

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Linear shallow neural network to accelerate transmitter dispersion eye closure quaternary (TDECQ) assessment

We have demonstrated a data-driven TDECQ assessment scheme based on L-SNN. In comparison with existing DL-based schemes, the proposed L-SNN can achieve the lowest computation complexity with only 210 multiplications. The MAE of the L-SNN scheme for 25 and 50 Gbaud PAM-4 optical signals is experimentally verified to be 0.13 and 0.15 dB, respectively, over the TDECQ range of 1.5–4.0 dB, which has reached the accuracy threshold of 0.25 dB recommended by the IEEE standard.

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来源期刊

Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

12.60

自引率

5.70%

发文量

224

审稿时长

8.3 months

期刊介绍： Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.