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symbol detection Based on Temporal Convolutional Network in Optical Communications

China Communications 2022年第1期19卷 284-292页

作者： Yingzhe Luo Jianhao Hu Key Laboratory of Science and Technology on Communication University of Electronic and Science Technology of ChinaChengdu 611731China

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 *** paper uses the DL method to design a symbol detection algorithm in receiver for optical communication *** proposed DL based method is implemented by a non-causal temporal convolutional network(ncTCN),which is a convolutional neural network and appropriate for sequence ***,we adopt three methods to realize the training process for multiple signal-to-noise ratios of the AWGN ***,we apply two nonlinear activation functions for the noise robustness to the proposed *** losing generality,we apply the ncTCN-based receiver to the 16-ary quadrature amplitude modulation optical communication system in the simulation *** to the experiment results,the proposed method can obtain some bit error rate performance gain compared to some conventional receivers.

关键词： deep learning optical communicaitons quadrature amplitude modulation symbol detection

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

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数字通信与网络:英文版 2023年第4期9卷 920-930页

作者： Yingzhe Luo Jianhao Hu National Key Laboratory of Science and Technology on Communications University of Electronic and Science Technology of ChinaChengdu611731China

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 *** DL-based methods have been applied to such systems to improve bit-error *** 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 *** 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 ***,we adopt three training approaches for different signal-to-noise *** also apply a parametric rectified linear unit to enhance the noise robustness of the proposed *** 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.

关键词： Deep learning Optical communications symbol detection Temporal convolutional network

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Deep Learning-Based symbol detection for Time-Varying Nonstationary Channels

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China Communications 2022年第3期19卷 158-171页

作者： Xuantao Lyu Wei Feng Ning Ge Xianbin Wang Research Center of Networks and Communications Peng Cheng LaboratoryShenzhen 518055China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering The University of Western OntarioLondonON N6A 5B9Canada

The highly dynamic channel(HDC)in an extremely dynamic environment mainly has fast timevarying nonstationary *** this article,we focus on the most difficult HDC case,where the channel coherence time is less than the symbol *** this end,we propose a symbol detector based on a long short-term memory(LSTM)neural *** the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better *** addition,using the basic expansion model(BEM)as the preprocessing unit significantly reduces the number of neural network ***,the simulation part uses the highly dynamic plasma sheath channel(HDPSC)data measured from shock tube *** results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

关键词： highly dynamic channel deep neural network long short-term memory basis expansion model symbol detection

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