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.

Deep Learning-Based Symbol Detection for Time-Varying Nonstationary Channels

The highly dynamic channel(HDC)in an extremely dynamic environment mainly has fast timevarying nonstationary characteristics.In this article,we focus on the most difficult HDC case,where the channel coherence time is less than the symbol period.To this end,we propose a symbol detector based on a long short-term memory(LSTM)neural network.Taking 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 performance.In addition,using the basic expansion model(BEM)as the preprocessing unit significantly reduces the number of neural network parameters.Finally,the simulation part uses the highly dynamic plasma sheath channel(HDPSC)data measured from shock tube experiments.The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

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.