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    我们开发了一种多任务学习 (MTL) 神经网络均衡器,用于远程光学传输. 这种方法提高了与单任务模型相比的概括性和性能,并降低了复杂性.

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    科学领域:

    • 光学通信工程 光学通信工程
    • 机器学习在电信中的应用
    • 用于光网络的信号处理.

    背景情况:

    • 长途密集波长分割多重复合 (DWDM) 光学系统面临的信号损害需要先进的平衡.
    • 传统的后平衡器在高容量光学传输中与一般化和复杂性作斗争.
    • 基于神经网络 (NN) 的均衡器提供了潜力,但需要强大的概括.

    研究的目的:

    • 为连贯检测DWDM系统开发基于神经网络 (NN) 的高效后平衡器.
    • 通过使用多任务学习 (MTL) 增强 NN 均衡器的概括能力.
    • 与现有方法相比,评估MTL等效器的性能和复杂性.

    主要方法:

    • 实施一个多任务学习 (MTL) 框架,用于共享神经网络 (NN) 均衡器.
    • 使用远程DWDM系统的实验数据对MTL等分器模型进行培训和验证.
    • 与单一任务的NN均衡器对应物和以前的方法进行比较分析.

    主要成果:

    • 与单任务NNs相比,开发的MTL等效器模型表现出卓越的性能和通用性.
    • 实验数据验证了MTL在提高等效器性能方面的有效性.
    • 使用MTL可以显著降低等效器的复杂性,而不会影响性能.

    结论:

    • 多任务学习 (MTL) 提供了一种有效的策略,用于在远程DWDM光学系统中增强基于NN的后平衡器.
    • 与单一任务方法相比,MTL提供了更好的性能和概括性.
    • 拟议的MTL均衡器在性能和计算复杂性之间实现了有利的平衡.