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

Digital Communications and Networks - Tập 9 - Trang 920-930 - 2023
Yingzhe Luo1, Jianhao Hu1
1National Key Laboratory of Science and Technology on Communications, University of Electronic and Science Technology of China, Chengdu, 611731, China

Tài liệu tham khảo

Xia, 2010, Greening the optical backbone network: a traffic engineering approach, 1 Smith, 2014, Optical network unit with redundant reverse powering from customer premises equipment with alarm fault discrimination indicative for power fault condition, uS Patent, 8, 192 P. Herve, S. Ovadia, Optical technologies for enterprise networks., Intel Technol. J. 8 (2), 2004. Ovadia, 2009, Method and architecture for optical networking between server and storage area networks, uS Patent, 7, 582 Palais, 1988 Savory, 2008, Digital filters for coherent optical receivers, Opt Express, 16, 804, 10.1364/OE.16.000804 Mahgerefteh, 2009, Power source for a dispersion compensation fiber optic system, uS Patent, 7, 902 Singer, 2008, Electronic dispersion compensation, IEEE Signal Process. Mag., 25, 110, 10.1109/MSP.2008.929230 Bülow, 2008, Electronic dispersion compensation, J. Lightwave Technol., 26, 158, 10.1109/JLT.2007.913066 Alfiad, 2009, Ffe, dfe and mlse equalizers in phase modulated transmission systems, 193 Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Network., 2, 359, 10.1016/0893-6080(89)90020-8 Qin, 2019, Deep learning in physical layer communications, IEEE Wireless Commun., 26, 93, 10.1109/MWC.2019.1800601 O'Shea, 2017, An introduction to deep learning for the physical layer, IEEE Transact. Cognit. Commun. Network., 3, 563, 10.1109/TCCN.2017.2758370 Karanov, 2018, End-to-end deep learning of optical fiber communications, J. Lightwave Technol., 36, 4843, 10.1109/JLT.2018.2865109 Karanov, 2019, Deep learning for communication over dispersive nonlinear channels: performance and comparison with classical digital signal processing, 192 Lee, 2019, Deep learning framework for wireless systems: applications to optical wireless communications, IEEE Commun. Mag., 57, 35, 10.1109/MCOM.2019.1800584 Jones, 2018, Deep learning of geometric constellation shaping including fiber nonlinearities, 1 Owaki, 2016, Equalization of optical nonlinear waveform distortion using neural-network based digital signal processing, 1 Houtsma, 2019, 92 and 50 gbps tdm-pon using neural network enabled receiver equalization specialized for pon Ye, 2017, Demonstration of 50gbps im/dd pam4 pon over 10ghz class optics using neural network based nonlinear equalization, 1 Estaran, 2016, Artificial neural networks for linear and non-linear impairment mitigation in high-baudrate im/dd systems, 1 Rios-Müller, 2017, Experimental estimation of optical nonlinear memory channel conditional distribution using deep neural networks Ip, 2008, Compensation of dispersion and nonlinear impairments using digital backpropagation, J. Lightwave Technol., 26, 3416, 10.1109/JLT.2008.927791 Farsad, 2018, Neural network detection of data sequences in communication systems, IEEE Trans. Signal Process., 66, 5663, 10.1109/TSP.2018.2868322 Lemaire, 2019, Temporal convolutional networks for speech and music detection in radio broadcast, 229 S. Bai, J. Z. Kolter, V. Koltun, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, arXiv preprint arXiv:1803.01271. Xu, 2018 Binh, 2014 He, 2015, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, 1026 LeCun, 2012, Efficient backprop, 9 S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint arXiv:1502.03167. Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Yoshioka, 2016, Noise robust speech recognition using recent developments in neural networks for computer vision, 5730 Sivadas, 2015, Investigation of parametric rectified linear units for noise robust speech recognition He, 2016, Deep residual learning for image recognition, 770 He, 2016, Identity mappings in deep residual networks, 630 Bishop, 1995 Ripley, 2007 Venables, 2013 X. Zhang, Y. Gao, Y. Yu, W. Li, Music Artist Classification with Wavenet Classifier for Raw Waveform Audio Data, arXiv preprint arXiv:2004.04371. D. P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980. Huang, 2015, An analysis of convolutional neural networks for speech recognition, 4989 Szegedy, 2015, Going deeper with convolutions, 1 K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409.1556. Qian, 2016, 70 Tan, 2018, Adaptive very deep convolutional residual network for noise robust speech recognition, IEEE/ACM Transact. Audio, Speech, and Lang. Proces., 26, 1393, 10.1109/TASLP.2018.2825432 D. Pearce, J. Picone, Aurora working group: Dsr front end lvcsr evaluation au/384/02, Inst. for Signal & Inform. Process., Mississippi State Univ., Tech. Rep. Vincent, 2017, An analysis of environment, microphone and data simulation mismatches in robust speech recognition, Comput. Speech Lang, 46, 535, 10.1016/j.csl.2016.11.005 B. Xu, N. Wang, T. Chen, M. Li, Empirical Evaluation of Rectified Activations in Convolutional Network, arXiv preprint arXiv:1505.00853. Zhu, 2019, A deep learning method based on convolution neural network for blind demodulation of mixed signals with different modulation types, 91 Xia, 2019, Transfer learning assisted deep neural network for osnr estimation, Opt Express, 27, 19398, 10.1364/OE.27.019398 Abadi, 2016, Tensorflow: a system for large-scale machine learning, 265