Future high-speed mobile communication systems require low latency and high capacity networks.Coherent wavelength division multiplexing(WDM)passive optical network(PON)scheme is expected to play a vital role in these ...Future high-speed mobile communication systems require low latency and high capacity networks.Coherent wavelength division multiplexing(WDM)passive optical network(PON)scheme is expected to play a vital role in these systems.In this paper,coherent WDM-PON scheme based on dual-polarization 16-quadrature amplitude modulation(DP-16 QAM)transceiver has been investigated.The aim of this scheme is to build a 2 Tbit/s(125 Gbit/s/λ×16 wavelengths)network that will be used in the construction of the transport architecture of fifth generation(5 G)and beyond 5 G(B5 G)cellular networks either in mobile front haul(MFH)or mobile back haul(MBH).The results indicate that the proposed scheme is very adequate for both 5 G and B5 G cellular networks requirements.展开更多
Coherent optics are emerging as promising solutions for future passive optical networks.However,upstream burst-mode coherent detection faces challenges due to the need for fast digital signal processing and its suscep...Coherent optics are emerging as promising solutions for future passive optical networks.However,upstream burst-mode coherent detection faces challenges due to the need for fast digital signal processing and its susceptibility to laser wavelength drift.To address these issues,we propose an algorithm capable of rapid channel equalization and frequency offset estimation(FOE).The feasibility of the proposed scheme is experimentally verified through 128-Gbit/s 16QAM signal transmission systems.Consequently,integrating a fine FOE tap into the adaptive equalization allows for rapid convergence and accurate frequency offset estimates within~±0.5 times the symbol rate while maintaining low complexity.展开更多
The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
Optical neural networks are emerging as a competitive alternative to their electronic counterparts,offering distinct advantages in bandwidth and energy efficiency.Despite these benefits,scaling up on-chip optical neur...Optical neural networks are emerging as a competitive alternative to their electronic counterparts,offering distinct advantages in bandwidth and energy efficiency.Despite these benefits,scaling up on-chip optical neural networks for end-to-end inference is facing significant challenges.First,network depth is constrained by the weak cascadability of optical nonlinear activation functions.Second,the input size is constrained by the scale of the optical matrix.Herein,we propose a scaling up strategy called partially coherent deep optical neural networks(PDONNs).By leveraging an on-chip nonlinear activation function based on opto-electro-opto conversion,PDONN enables network depth expansion with positive net gain.Additionally,convolutional layers achieve rapid dimensionality reduction,thereby allowing for an increase in the accommodated input size.The use of a partially coherent optical source significantly reduces reliance on narrow-linewidth laser diodes and coherent detection.Owing to their broader spectral characteristics and simpler implementation,such sources are more accessible and compatible with scalable integration.Benefiting from these innovations,we designed and fabricated a monolithically integrated optical neural network with the largest input size and the deepest network depth,comprising an input layer with a size of 64,two convolutional layers,and two fully connected layers.We successfully demonstrate end-to-end two-class classification of fashion images and four-class classification of handwritten digits with accuracies of 96%and 94%,respectively,using an in-situ training method.Notably,performance is well maintained with partially coherent illumination.This proposed architecture represents a critical step toward realizing energy-efficient,scalable,and widely accessible optical computing.展开更多
Spiral waves have been observed in the biological experiments on rat cortex perfused with drugs which can block inhibitory synapse and switch neuron excitability from type II to type I. To simulate the spiral waves ob...Spiral waves have been observed in the biological experiments on rat cortex perfused with drugs which can block inhibitory synapse and switch neuron excitability from type II to type I. To simulate the spiral waves observed in the experiment, the spatiotemporal patterns are investigated in a network composed of neurons with type I and II excitabilities and excitatory coupling. Spiral waves emerge when the percentage(p) of neurons with type I excitability in the network is at middle levels, which is dependent on the coupling strength. Compared with other spatial patterns which appear at different p values, spiral waves exhibit optimal spatial correlation at a certain spatial frequency, implying the occurrence of spatial coherence resonance-like phenomenon. Some dynamical characteristics of the network such as mean firing frequency and synchronous degree can be well interpreted with distinct properties between type I excitability and type II excitability. The results not only identify dynamics of spiral waves in neuronal networks composed of neurons with different excitabilities, but also are helpful to understanding the emergence of spiral waves observed in the biological experiment.展开更多
We study the consensus of a family of recursive trees with novel features that include the initial states controlled by a parameter.The consensus problem in a linear system with additive noises is characterized as net...We study the consensus of a family of recursive trees with novel features that include the initial states controlled by a parameter.The consensus problem in a linear system with additive noises is characterized as network coherence,which is defined by a Laplacian spectrum.Based on the structures of our recursive treelike model,we obtain the recursive relationships for Laplacian eigenvalues in two successive steps and further derive the exact solutions of first-and second-order coherences,which are calculated by the sum and square sum of the reciprocal of all nonzero Laplacian eigenvalues.For a large network size N,the scalings of the first-and second-order coherences are lnN and N,respectively.The smaller the number of initial nodes,the better the consensus bears.Finally,we numerically investigate the relationship between network coherence and Laplacian energy,showing that the firstand second-order coherences increase with the increase of Laplacian energy at approximately exponential and linear rates,respectively.展开更多
基金the Alexander von Humboldt Foundation for their support。
文摘Future high-speed mobile communication systems require low latency and high capacity networks.Coherent wavelength division multiplexing(WDM)passive optical network(PON)scheme is expected to play a vital role in these systems.In this paper,coherent WDM-PON scheme based on dual-polarization 16-quadrature amplitude modulation(DP-16 QAM)transceiver has been investigated.The aim of this scheme is to build a 2 Tbit/s(125 Gbit/s/λ×16 wavelengths)network that will be used in the construction of the transport architecture of fifth generation(5 G)and beyond 5 G(B5 G)cellular networks either in mobile front haul(MFH)or mobile back haul(MBH).The results indicate that the proposed scheme is very adequate for both 5 G and B5 G cellular networks requirements.
基金supported by the National Key Research and Development Program of China(No.2021YFB2900800)the Science and Technology Commission of Shanghai Municipality(Nos.22511100902 and 22511100502)the 111 Project(No.D20031)。
文摘Coherent optics are emerging as promising solutions for future passive optical networks.However,upstream burst-mode coherent detection faces challenges due to the need for fast digital signal processing and its susceptibility to laser wavelength drift.To address these issues,we propose an algorithm capable of rapid channel equalization and frequency offset estimation(FOE).The feasibility of the proposed scheme is experimentally verified through 128-Gbit/s 16QAM signal transmission systems.Consequently,integrating a fine FOE tap into the adaptive equalization allows for rapid convergence and accurate frequency offset estimates within~±0.5 times the symbol rate while maintaining low complexity.
文摘The BER performance of the coherent time-spreading OCDMA network is analyzed by considering the MAI and beat noises as well as the other additive noises. The influence and solution for the beat noise issue are discussed.
基金supported by the Fundamental Research Funds for the Central Universities.
文摘Optical neural networks are emerging as a competitive alternative to their electronic counterparts,offering distinct advantages in bandwidth and energy efficiency.Despite these benefits,scaling up on-chip optical neural networks for end-to-end inference is facing significant challenges.First,network depth is constrained by the weak cascadability of optical nonlinear activation functions.Second,the input size is constrained by the scale of the optical matrix.Herein,we propose a scaling up strategy called partially coherent deep optical neural networks(PDONNs).By leveraging an on-chip nonlinear activation function based on opto-electro-opto conversion,PDONN enables network depth expansion with positive net gain.Additionally,convolutional layers achieve rapid dimensionality reduction,thereby allowing for an increase in the accommodated input size.The use of a partially coherent optical source significantly reduces reliance on narrow-linewidth laser diodes and coherent detection.Owing to their broader spectral characteristics and simpler implementation,such sources are more accessible and compatible with scalable integration.Benefiting from these innovations,we designed and fabricated a monolithically integrated optical neural network with the largest input size and the deepest network depth,comprising an input layer with a size of 64,two convolutional layers,and two fully connected layers.We successfully demonstrate end-to-end two-class classification of fashion images and four-class classification of handwritten digits with accuracies of 96%and 94%,respectively,using an in-situ training method.Notably,performance is well maintained with partially coherent illumination.This proposed architecture represents a critical step toward realizing energy-efficient,scalable,and widely accessible optical computing.
基金supported by the National Natural Science Foundation of China(Grant Nos.11372224&11572225)
文摘Spiral waves have been observed in the biological experiments on rat cortex perfused with drugs which can block inhibitory synapse and switch neuron excitability from type II to type I. To simulate the spiral waves observed in the experiment, the spatiotemporal patterns are investigated in a network composed of neurons with type I and II excitabilities and excitatory coupling. Spiral waves emerge when the percentage(p) of neurons with type I excitability in the network is at middle levels, which is dependent on the coupling strength. Compared with other spatial patterns which appear at different p values, spiral waves exhibit optimal spatial correlation at a certain spatial frequency, implying the occurrence of spatial coherence resonance-like phenomenon. Some dynamical characteristics of the network such as mean firing frequency and synchronous degree can be well interpreted with distinct properties between type I excitability and type II excitability. The results not only identify dynamics of spiral waves in neuronal networks composed of neurons with different excitabilities, but also are helpful to understanding the emergence of spiral waves observed in the biological experiment.
基金Project supported by the National Natural Science Foundation of China(No.61673144)the Program of China Scholarship Council(No.201808330592)。
文摘We study the consensus of a family of recursive trees with novel features that include the initial states controlled by a parameter.The consensus problem in a linear system with additive noises is characterized as network coherence,which is defined by a Laplacian spectrum.Based on the structures of our recursive treelike model,we obtain the recursive relationships for Laplacian eigenvalues in two successive steps and further derive the exact solutions of first-and second-order coherences,which are calculated by the sum and square sum of the reciprocal of all nonzero Laplacian eigenvalues.For a large network size N,the scalings of the first-and second-order coherences are lnN and N,respectively.The smaller the number of initial nodes,the better the consensus bears.Finally,we numerically investigate the relationship between network coherence and Laplacian energy,showing that the firstand second-order coherences increase with the increase of Laplacian energy at approximately exponential and linear rates,respectively.
