短距光通信的软解映射：深度神经网络与 Volterra 级数的比较
Soft-Demapping for Short Reach Optical Communication: A Comparison of Deep Neural Networks and Volterra Series
AI总结：
本文针对短距光通信中 Volterra 非线性均衡器复杂度过高的问题，提出按比特软 DNN 均衡器（SDNNE），实验表明其可在同等性能下降复杂度 65%，或在同等复杂度下获得 0.35 dB OSNR 增益。
摘要：
在光纤通信中，光学和电子元件会引入非线性，因此需要有效补偿以实现最高数据速率。特别是在短距通信中，元件是非线性的主要来源。Volterra 级数是一种常用对策，用于在接收端均衡非线性元件损伤及其记忆效应。然而，Volterra 均衡器架构通常非常复杂。本文研究软深度神经网络（DNN）架构，将其作为非线性均衡和软判决解映射的替代方案。在相干 92 GBd 双偏振 64QAM 背靠背测量中，通过实验评估了性能与复杂度。在 15% 开销前向纠错（FEC）门限下，将所提出的按比特软 DNN 均衡器（SDNNE）与五阶 Volterra 均衡器进行比较。在性能相当时，计算复杂度降低 65%；在复杂度相当时，光信噪比（OSNR）性能提升 0.35 dB。
英文摘要：
In optical fiber communication, optical and electrical components introduce nonlinearities, which require effective compensation to attain highest data rates. In particular, in short reach communication, components are the dominant source of nonlinearities. Volterra series are a popular countermeasure for receiver-side equalization of nonlinear component impairments and their memory effects. However, Volterra equalizer architectures are generally very complex. This article investigates soft deep neural network (DNN) architectures as an alternative for nonlinear equalization and soft-decision demapping. On coherent 92 GBd dual polarization 64QAM back-to-back measurements performance and complexity is experimentally evaluated. The proposed bit-wise soft DNN equalizer (SDNNE) is compared to a 5th order Volterra equalizer at a 15 % overhead forward error correction (FEC) limit. At equal performance, the computational complexity is reduced by 65 %. At equal complexity, the performance is improved by 0.35 dB gain in optical signal-to-noise-ratio (OSNR).
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。
