Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.

Symbol Detection Based on Temporal Convolutional Network in Optical Communications

Deep learning(DL)is one of the fastest developing areas in artificial intelligence,it has been recently gained studies and application in computer vision,automatic driving,automatic speech recognition,and communication.This paper uses the DL method to design a symbol detection algorithm in receiver for optical communication systems.The proposed DL based method is implemented by a non-causal temporal convolutional network(ncTCN),which is a convolutional neural network and appropriate for sequence processing.Meanwhile,we adopt three methods to realize the training process for multiple signal-to-noise ratios of the AWGN channel.Furthermore,we apply two nonlinear activation functions for the noise robustness to the proposed ncTCN.Without losing generality,we apply the ncTCN-based receiver to the 16-ary quadrature amplitude modulation optical communication system in the simulation experiment.According to the experiment results,the proposed method can obtain some bit error rate performance gain compared to some conventional receivers.

RS-LDPC概率联合译码算法

现有的译码方案中,由于复杂度问题,LDPC码多使用部分并行译码架构,难以满足高速、超高速应用;RS码多采用硬判决译码算法.因此,对于RS-LDPC级联码译码在复杂度和性能上有进一步优化的空间.本文基于概率计算的思想提出了RS-LDPC概率联合迭代译码算法.该算法继承了概率LDPC译码器的低复杂度优势和概率RS译码算法可采用硬译码器逼近软译码性能的特点,同时通过联合迭代译码架构改善了译码性能.为此,我们设计了一种基于LDPC概率值的测试向量产生方法和一种新颖的应用于概率LDPC译码器的变量节点结构的附加外信息生成机制.仿真结果表明,在误比特率为10-6处,我们提出的算法与使用浮点BP和BM硬判决译码的RS-LDPC级联译码方案相比,可以获得0.3 d B的增益;其实现复杂度为一个概率LDPC译码器和多个RS硬译码器的复杂度;译码时延由概率LDPC译码器时延决定.因此,本文提出的联合译码算法具有很好的应用价值.