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Asia communications and Photonics Conference and Exhibition (ACP)

作者： Tonghui Ji Qi Wu Zhaopeng Xu Honglin Ji Yu Yang Lulu Liu Shangcheng Wang Zhongliang Sun Junpeng Liang Linsheng Fan Jianwei Tang Jinlong Wei Juhao Li Weisheng Hu 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 a fast and accurate estimation method of MDL-induced SNR penalty with non-uniform noise loading in SDM link. The proposed approach substantially saves the time cost while maintaining an estimation error wit... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： optical losses Costs Accuracy Simulation Loading Propagation losses optical fiber communication Numerical models Signal to noise ratio Photonics

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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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Asia communications and Photonics Conference and Exhibition (ACP)

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: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Q-factor Equalizers Symbols Artificial neural networks optical computing optical fiber networks Multitasking Real-time systems Complexity theory Photonics

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Improving Aircraft Engine Remaining Useful Life Prediction Scheme Based on Handcrafted Features and Neural network Integration 5

Improving Aircraft Engine Remaining Useful Life Prediction S...

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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Bi, Dequan Bi, Meihua Qian, Lien Teng, Xuyang Hu, Miao School of Communication Engineering Hangzhou Dianzi University Zhe Jiang province Hangzhou310018 China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication System and Networks Department of Electronic Engineering Shanghai200240 China China Elect Technol Grp Corp Hangzhou China

ISBN: (纸本)9798331530334

The prediction of Remaining Useful Life (RUL) serves as a key indicator in the prognostics and health management of industrial equipment, holding significant implications for enhancing the availability of industrial equipment and reducing lifecycle costs. The common scheme based on the neural network especially for the Long Short-Term Memory (LSTM) networks can achieve satisfactory results in RUL prediction, while they are incapable of actively selecting key features and efficiently extracting degradation information embedded in the data. In this paper, we propose a new hybrid model based on LSTM networks. Here, the Convolutional Neural networks (CNN) is employed to extract key features, then a Bidirectional Long Short-Term Memory (BiLSTM) is utilized to capture long and short-term dependencies within the features. Besides, the attention mechanism is introduced to be applied to assign different weights to select important parts. In this way, by integrating the achieved statistical features, the accuracy of aircraft engine RUL prediction can be effectively enhanced. And, by experiment, our method is verified by carrying out on the C-MAPSS dataset. The corresponding results reveal that our approach is superior to the other reported neural network-based scheme, and it can be considered as one of the most promising schemes using for the RUL prediction of aircraft engine. © 2024 IEEE.

关键词： Convolutional neural networks

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Low Latency Fiber communication system Equalizer Based on Photonic Reservoir Computing

Low Latency Fiber Communication System Equalizer Based on Ph...

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

作者： Zuo, Xiaoyan Pei, Li Bai, Bing Bai, Bowen Wang, Jianshuai Sui, Juan Institute of Lightwave Technology Beijing Jiaotong University Key Laboratory of All-Optical Networks and Modern Communication Networks of Ministry of Education Beijing China School of Electronics Peking University State Key Laboratory of Advanced Optical Communications System and Networks Beijing China Technology Co. Ltd Beijing China

ISBN: (纸本)9798350312614

We demonstrate a low latency fiber channel distortion equalizer that implements mainly of the equalization calculations in the optical domain, based on an integrated photonic reservoir computing chip. We discussed the equalization effect of the equalizer under different dispersion noise and optical signal-to-noise ratio (OSNR) conditions. The bit error rate (BER) of the C-band with 25Gb/s on-off key (OOK) distorted signal in 25km standard single-mode fiber (SSMF) has been reduced by 3 orders of magnitude. By optimizing weights, this equalizer can adapt to link impairments. It can effectively equalize the distorted signals at data rates of 10-40 Gb/s over 25km in SSMF transmission. Furthermore, it can efficiently equalize the impaired signals after 5-40km SSMF transmission at a signal rate of 25Gb/s. © 2023 IEEE.

关键词： equalizer low latency photonic reservoir computing silicon photonics

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Statistical Characteristics of Distributed Modal Crosstalk for Weakly-Coupled FMF Transmission

Statistical Characteristics of Distributed Modal Crosstalk f...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Qiao, Gang Zuo, Mingqing Zhang, Jiarui Long, Chengbin He, Yongqi Chen, Zhangyuan Li, Juhao State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing100871 China Department of Fundamental Network Technology China Mobile Research Institute Beijing100053 China Peng Cheng Laboratory Shenzhen518055 China

We experimentally show that noise induced by the distributed modal crosstalk in weakly-coupled FMFs is well approximated by Gaussian distribution. Its impact on weakly-coupled MDM transmission systems is experimentall... 详细信息

ISBN: (纸本)9781957171258

关键词： Crosstalk

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50-km Weakly-coupled Transmission with L-band 4-LP-mode FM-EDFA

50-km Weakly-coupled Transmission with L-band 4-LP-mode FM-E...

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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： Huang, Shuailuo Zhu, Baolong Qiao, Gang Gao, Yuyang Zuo, Mingqing Shen, Shikui Wang, Zelin He, Yongqi Chen, Zhangyuan Li, Juhao Chen, Zhangyuan Li, Juhao China Unicom Research Institute Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Fundamental Network Technology China Mobile Research Institute Beijing China Pengcheng National Laboratory Shenzhen China

ISBN: (纸本)9798350379266

关键词： Pulse amplitude modulation

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OFDR system with broadened frequency sweep range assisted by dual electro-optic frequency comb 27

OFDR system with broadened frequency sweep range assisted by...

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27th International Conference on optical Fiber Sensors, OFS 2022

作者： Zhu, Yidai Fan, Xinyu Zhang, Zhaopeng Yuan, Zhengchao Xu, Bingxin He, Zuyuan State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai200240 China Peng Cheng Laboratory Guangdong Shenzhen518055 China

We propose an OFDR system with the assistance of dual electro-optic frequency comb (EOFC). Rayleigh backscattering signals collected by multiple comb lines are synthesized to broaden the frequency sweep range for the ... 详细信息

ISBN: (纸本)9781957171142

关键词： Effective refractive index Frequency combs optical amplification optical amplifiers Spatial resolution Swept sources

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1-Pb/s CPRI-Equivalent Rate Coherent DA-RoF Fronthaul with 1024-QAM Scalable in Capacity, Reach, and Linewidth Using Residual Carrier-Based Phase Tracking

1-Pb/s CPRI-Equivalent Rate Coherent DA-RoF Fronthaul with 1...

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Asia communications and Photonics Conference and Exhibition (ACP)

作者： Yixiao Zhu Xiansong Fang Chenbo Zhang Jingjing Lin Yicheng Xu Weiwei Hu Zhangyuan Chen Qunbi Zhuge Fan Zhang Xiaopeng Xie Weisheng Hu State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China State Key Laboratory o_f Advanced Optical Communication System and Networks Peking University Beijing China

We demonstrate 1.0-Pb/s CPRI-equivalent rate fronthaul with 1024-QAM by using digital-analog radio-over-fiber and coherent detection based on modulator bias-induced residual carrier for phase tracking. The reach is extended to 20-km SSMF with 4096-QAM and 80-km with 256-QAM, and laser linewidth can be relaxed to 3MHz.

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To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning 29

To What Extent Do Natural Language Understanding Datasets Co...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yitian Tian, Jidong Chen, Wenqing Fan, Caoyun He, Hao Jin, Yaohui MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University China

Reasoning and knowledge-related skills are considered as two fundamental skills for natural language understanding (NLU) tasks such as machine reading comprehension (MRC) and natural language inference (NLI). However, it is not clear to what extent an NLU task defined on a dataset correlates to a specific NLU skill. On the one hand, evaluating the correlation requires an understanding of the significance of the NLU skill in a dataset. Significance judges whether a dataset includes sufficient material to help the model master this skill. On the other hand, it is also necessary to evaluate the dependence of the task on the NLU skill. Dependence is a measure of how much the task defined on a dataset depends on the skill. In this paper, we propose a systematic method to diagnose the correlations between an NLU dataset and a specific skill, and then take a fundamental reasoning skill, logical reasoning, as an example for analysis. The method adopts a qualitative indicator to indicate the significance while adopting a quantitative indicator to measure the dependence. We perform diagnosis on 8 MRC datasets (including two types) and 3 NLI datasets and acquire intuitively reasonable results. We then perform the analysis to further understand the results and the proposed indicators. Based on the analysis, although the diagnostic method has some limitations, it is still an effective method to perform a basic diagnosis of the correlation between the dataset and logical reasoning skill, which also can be generalized to other NLU skills. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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Sensing-communication-Computation Integration for Federated Edge Learning with Controllable Model Dropout

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IEEE Internet of Things Journal 2025年

作者： Jiao, Xiang Zhu, Guangxu Jiang, Wei Chen, Li Luo, Wu Wen, Dingzhu State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The CAS Key Laboratory of Wireless Optical Communication University of Science and Technology of China Hefei China Network Intelligence Center School of Information Science and Technology ShanghaiTech University Shanghai China

FEderated Edge Learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: sensing, computation, and communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into Integrated Sensing-Computation-communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a sub-model through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables such as batch size, sensing power, dropout rate, and communication power. This non-convex problem is decomposed into two sub-problems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes. © 2014 IEEE.

关键词： Federated learning

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