arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.08790eess.SPcs.ITcs.LGmath.IT
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深度学习在短距离光纤通信数字信号处理中的实验研究

Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

University College London(伦敦大学学院);Karlsruhe Institute of Technology(卡尔斯鲁厄理工学院);Nokia Bell Labs(诺基亚贝尔实验室)

Boris Karanov, Mathieu Chagnon, Vahid Aref, Filipe Ferreira, Domanic Lavery, Polina Bayvel, Laurent Schmalen

摘要:

我们研究的方法,实验性能增强的自动编码器的基础上,一个循环神经网络(RNN)的通信分散非线性信道。特别是,我们的重点是最近提出的滑动窗口双向RNN(SBRNN)光纤自动编码器。我们表明,调整处理窗口中的序列估计算法在接收机提高了达到简单的系统训练的信道模型和应用“原样”的传输链路。此外,收集的实验数据被用来优化接收器神经网络参数,允许传输42 Gb/s的误比特率(BER)低于6.7%的硬判决前向纠错阈值在距离70公里,以及84 Gb/s在20公里。将基于实验数据优化的数字信号处理(DSP)扩展到脉冲幅度调制,接收机采用前馈或递归神经网络以及经典的非线性Volterra均衡进行滑动窗口序列估计。我们的研究结果表明,对于固定的算法内存,基于深度学习的DSP实现了改善的BER性能,从而增加了系统的覆盖范围。

英文摘要:

We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In particular, our focus is on the recently proposed sliding window bidirectional RNN (SBRNN) optical fiber autoencoder. We show that adjusting the processing window in the sequence estimation algorithm at the receiver improves the reach of simple systems trained on a channel model and applied "as is" to the transmission link. Moreover, the collected experimental data was used to optimize the receiver neural network parameters, allowing to transmit 42 Gb/s with bit-error rate (BER) below the 6.7% hard-decision forward error correction threshold at distances up to 70km as well as 84 Gb/s at 20 km. The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization. Our results show that, for fixed algorithm memory, the DSP based on deep learning achieves an improved BER performance, allowing to increase the reach of the system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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