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Learnable digital signal processing:a new benchmark of linearity compensation for optical fiber communications

Light(Science & Applications) 2024年第9期13卷 1931-1943页

作者： Zekun Niu Hang Yang Lyu Li Minghui Shi Guozhi Xu Weisheng Hu Lilin Yi State Key Lab of Advanced Optical Communication Systems and Networks School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240PR China

The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing(DSP)schemes that are highly cost-effective with both high performance and low *** benchmarks for nonlinear compensation methods,however,traditional DSP designed with block-by-block modules for linear compensations,could exhibit residual linear effects after compensation,limiting the nonlinear compensation *** we propose a high-efficient design thought for DSP based on the learnable perspectivity,called learnable DSP(LDSP).LDSP reuses the traditional DSP modules,regarding the whole DSP as a deep learning framework and optimizing the DSP parameters adaptively based on backpropagation algorithm from a global *** method not only establishes new standards in linear DSP performance but also serves as a critical benchmark for nonlinear DSP *** comparison to traditional DSP with hyperparameter optimization,a notable enhancement of approximately 1.21 dB in the Q factor for 400 Gb/s signal after 1600 km fiber transmission is experimentally demonstrated by combining LDSP and perturbation-based nonlinear compensation *** from the learnable model,LDSP can learn the best configuration adaptively with low complexity,reducing dependence on initial *** proposed approach implements a symbol-rate DSP with a small bit error rate(BER)cost in exchange for a 48%complexity reduction compared to the conventional 2 samples/symbol *** believe that LDSP represents a new and highly efficient paradigm for DSP design,which is poised to attract considerable attention across various domains of optical communications.

关键词： fiber signal optical

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Performance Improvement of Adaptive N-Square PS-QAM Based on LUT for Flexible Coherent PON

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Journal of Lightwave Technology 2025年第12期43卷 5569-5578页

作者： Lin, Gengming Zhu, Yixiao Yan, Zijun Cheng, Ziyu Zhuge, Qunbi Hu, Weisheng Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication Systems and Networks School of Electronic Information and Electrical Engineering Department of Electronic Engineering Shanghai200240 China

Flexible coherent passive optical network (PON) is recognized as an advanced capacity optimization strategy for beyond 100G PON due to its flexibility and adaptability, which are pivotal for the evolution of high-capacity optical access networks. In this study, we propose an adaptive N-square QAM scheme compatible with probabilistic shaping (PS), enabling a more continuous variation of shaping entropy compared to standard 2M-QAM shaping scheme and improved frequency offset estimation (FOE) accuracy in blind 4th-power Fast-Fourier-transform (FFT) algorithm. In experimental demonstrations, dynamic-range and data-rate product (DRDRP) of uniform N2-QAM surpasses that of uniform 2M-QAM by 86 dB·Gbps. The DRDRP of PS N2-QAM also outperforms that of PS 2N-QAM by around 41 dB·Gbps. The development of PON optimization scheme for multi-carrier transmission is achieved through the generation of a look-up table (LUT) based on channel signal-to-noise ratio (SNR) conditions. The feasibility of the LUT is demonstrated under different super-Gaussian filter parameter settings at the back-to-back (B2B) scenario. After 20-km standard single-mode fiber (SSMF) transmission, the normalized generalized mutual information (NGMI) is greater than 0.985 across four subcarriers with varying received optical power (ROP) from -16 to -11 dBm. © 2025 IEEE.

关键词： Single mode fibers

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Design of Large-Scale OXC for the Next-Generation ROADM 16

Design of Large-Scale OXC for the Next-Generation ROADM

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Ye, Tong Luo, Jiayi Li, Herui Yao, Yibei Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication Systems and Networks Shanghai China

Using multiple MEMSes sandwiched between two columns of WSSes, this paper designs a low-loss, nonblocking, and scalable OXC that is suitable for the future SDM optical network with a smaller number of wavelengths per ... 详细信息

ISBN: (纸本)9798350372076

关键词： optical fiber communication

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Multi-Task Learning: Towards Low-Complexity Neural network Nonlinearity Compensation in Coherent optical systems

Multi-Task Learning: Towards Low-Complexity Neural Network N...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and optical communications, IPOC 2024

作者： Ji, Tonghui Wu, Qi Xu, Zhaopeng Ji, Honglin Yang, Yu Liu, Lulu Wang, Shangcheng Sun, Zhongliang Liang, Junpeng Fan, Linsheng Tang, Jianwei Wei, Jinlong Li, Juhao Hu, Weisheng Pengcheng Laboratory Shenzhen China State Key Lab of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China State Key Laboratory of Advanced Optical Communication Systems & Networks Peking University Beijing China

We propose multi-task learning based NN equalization for nonlinearity compensation in coherent optical system. Compared with conventional NN equalization, 30% complexity reduction is achieved in an 800-Gb/s PDM-16QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Equalizers

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Fast and Accurate Estimation of MDL-Induced SNR Penalty in SDM Link with Non-uniform Noise Loading

Fast and Accurate Estimation of MDL-Induced SNR Penalty in S...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and optical communications, IPOC 2024

We propose a fast and accurate estimation method of MDL-induced SNR penalty with non-uniform noise loading in SDM *** proposed approach substantially saves the time cost while maintaining an estimation error within 0.... 详细信息

ISBN: (纸本)9798350379266

关键词： Signal to noise ratio

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Ontology-Semantic Alignment On Contrastive Video-Language Model for Multimodel Video Retrieval Task 7

Ontology-Semantic Alignment On Contrastive Video-Language Mo...

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7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Zhou, Yifan Ding, Yizhou Dong, Yuwu He, Hao Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication System and Network Shanghai China

ISBN: (纸本)9798400708688

Contrastive Learning-based models have shown impressive performance in text-image retrieval tasks. However, when applied in video retrieval, traditional contrastive learning strategies have faced challenges in achieving satisfactory results due to redundancy of video contents. We discern several potential reasons: (1)Current methodologies sometimes overlook the significant information imbalance between videos and query text, specifically neglecting the in-depth textual representation of the content within the videos. (2) Current video matching methodologies typically focus on cross-model alignment at general entity similarity level, without specific consideration for how entity pair preferences and similarity properties affect the task at hand. (3) Previous vectorized retrieval based on video content features have been somewhat flawed. They primarily focused on aligning overall features without having an video content tags feature for meaningful feature discrimination. Considering the shortcomings identified in the mentioned three aspects, we propose an ontology semantic labels augments retrieval model and introduce a method to integrate video ontology semantic labels into the contrastive learning framework. In particular, we have developed ontology semantic descriptions about entities encompassing both human figures and textual elements within the videos. Subsequently, we conducted training and testing on the CMIVQA dataset to assess the performance of our approach. The experimental results show that employing fine-grained ontology labels as sample pairs for contrastive learning leads to an increased level of precision in video retrieval tasks. © 2023 ACM.

关键词： Ontology

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LDSeq: Latent Diffusion Models for Sequence to Sequence Text Generation 7

LDSeq: Latent Diffusion Models for Sequence to Sequence Text...

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7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Ding, Yizhou Tian, Jidong Mei, Shanxing Zhou, Yifan Dong, Yuwu He, Hao Hu, Weisheng State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400708688

Diffusion models have demonstrated remarkable success in generating continuous data, such as images and audios. Previous studies on text generation employing continuous diffusion models have revealed the potential of the diffusion framework. However, challenges like embedding collapse persist, limiting the overall generation performance. In this paper we introduce LDSeq, a latent diffusion framework employing a two-stage training procedure for sequence-to-sequence text generation. In the proposed framework, we first train a Variational Auto-Encoder (VAE) on downstream datasets to compress the target text of samples into a continuous latent space, and then we train a conditional latent diffusion model in the fixed continuous latent space, where the latent vectors are iteratively sampled conditioned on the input source text. The disjoint training stages prevent the collapse of diffusion space. Experimental results on paraphrase generation and text summarization datasets show that LDSeq achieves comparable or superior performance in comparison to AR and NAR baselines while requiring lower training cost. Furthermore, We discuss some potential future directions for enhancing diffusion models in the text generation domain. © 2023 ACM.

关键词： Diffusion

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Net 8×250 Gbit/s/λ PAM6/8 optical Interconnect Over High-Density Eight-Core Fiber and Low-Crosstalk Laser Direct Writing FI/FO Devices

Net 8×250 Gbit/s/λ PAM6/8 Optical Interconnect Over High-D...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Yang, Yu Xu, Zhaopeng Ji, Honglin Qiao, Gang Liu, Lulu Wang, Shangcheng Cheng, Ruiting Yu, Jinyi Yang, Chuanchuan He, Zhixue He, Yongqi Chen, Zhangyuan Hu, Weisheng Li, Juhao Peng Cheng Laboratory Shenzhen China Peking University State Key Lab of Advanced Optical Communication Systems and Networks Beijing China Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication Systems and Networks Shanghai China

ISBN: (纸本)9798350312614

We experimentally demonstrate a net 8×250 Gbit/s/λ PAM6 and PAM8 transmission over 200-m standard-125μm-cladding high-density eight-core fiber and low-crosstalk femtosecond laser direct writing fan-in/fan-out devices. The proposed prototype system is promising for high-speed optical interconnection applications. © 2023 IEEE.

关键词： Fan-in/fan-out High-speed optical interconnects MCF SDM

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High Efficiency Fiber-Chip Edge Coupler for Near-Ultraviolet (NUV) Waveband

High Efficiency Fiber-Chip Edge Coupler for Near-Ultraviolet...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Du, Yuhan Ji, Xingchen Xie, Weiqiang Tian, Meng He, Yu Su, Yikai State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

We design and experimentally demonstrate an efficient fiber-chip edge coupler for NUV using symmetric double-tip taper and MMI-based coupler. We measure the coupling loss down to 2.85 dB at 407 nm on an alumina-on-ins... 详细信息

ISBN: (纸本)9781957171395

关键词： System-on-chip

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High Efficiency Fiber-Chip Edge Coupler for Near-Ultraviolet (NUV) Waveband

High Efficiency Fiber-Chip Edge Coupler for Near-Ultraviolet...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Ultraviolet devices

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