Low Complexity Component Nonlinear Distortions Mitigation Scheme for Probabilistically Shaped 64-QAM Signals

Complexity Reduction over Bi-RNN-Based Nonlinearity Mitigation in Dual-Pol Fiber-Optic Communications via a CRNN-Based Approach

Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computational complexity which may impede their real-time functioning. In this paper, by addressing the sources of complexity in these methods, we propose a more efficient network architecture, where a convolutional neural network encoder and a uni…

面向非线性干扰噪声抑制的球形整形中的多维能量限制

Multi-dimensional Energy Limitation in Sphere Shaping for Nonlinear Interference Noise Mitigation

本文提出四维能量限制的枚举球形整形方法，用于降低速率损失并抑制非线性干扰，在单跨段和WDM仿真中比传统ESS提升0.19 bit/4D-symbol，但增益在多跨段系统中扩展性有限。

A Low Complexity Near-Maximum Likelihood MIMO Receiver with Low Resolution Analog-to-Digital Converters

Based on a new equivalent model of quantizer with noisy input recently presented in [23], we propose a new low complexity receiver that takes into account the nonlinear distortion (NLD) generated by Analog to Digital converter (ADC) with insufficient resolution. The strength of new model is that it presents the NLD as a function of only the desired part of input signal (without noise). Therefore it can easily be used in a variety of NLD mitigation techniques. Here, as an illustration of this, we use a pseudo-ML approach to detect the original QAM modulation based on the equivalent transfer fun…

Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems

Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance optical fiber communication. Digital back-propagation (DBP) is a primary methodology to mitigate both linear and nonlinear impairments by solving the inverse-propagating nonlinear Schrödinger equation (NLSE), which requires detailed link information. Recently, the paradigms based on neural network (NN) were proposed to mitigate nonlinear transmission impairments in optical communication systems. However, almost all neural network-based equalization schemes yield high computation complexity, which pr…

基于双向循环神经网络相干WDM系统中的多通道非线性均衡

Multichannel Nonlinear Equalization in Coherent WDM Systems based on Bi-directional Recurrent Neural Networks

针对WDM系统中Kerr非线性限制，提出基于双向循环神经网络（bi-VRNN）的多通道均衡算法，实验证明其较单通道RNN和DBP在提升OSNR的同时大幅降低复杂度。