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Power evolution prediction of bidirectional Raman amplified WDM system based on a transformer

Optics Express 2025年第8期33卷 18369-18377页

作者： Feng, Zhongshuai Su, Xingrui Li, Wei Hu, Kaijing Yu, Jiekui Huang, Liyan Hu, Qianggao Jiang, Yi Luo, Ming Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Accelink Technologies Co. Ltd. Wuhan430205 China State Key Laboratory of Optical Communication Technologies and Networks Wuhan430200 China

This study presents a transformer-based power prediction model for Raman-amplified wavelength division multiplexing (WDM) systems. Leveraging the transformer’s self-attention mechanism, the model effectively captures global channel interactions, achieving a maximum prediction error of only 0.122 dB in a 200 km bidirectional Raman system with 47 channels and 12 pumps. The differential evolution (DE) technique was employed to optimize the hyperparameters of the models, further enhancing their performance. The comparative analysis was conducted to evaluate the performance of the two models under their respective optimal configurations. The model demonstrates robust generalization under varying pump powers and surpasses convolutional neural network (CNN)-based approaches in accuracy and interpretability. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Convolutional neural networks

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Chirp-free hundred-picosecond pulse fiber laser based on carbon nanotubes mode-locker

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Optics Express 2025年第7期33卷 14850-14857页

作者： Li, Weixi Jiang, Kailin Dai, Lilong Huang, Qianqian Zou, Meng Luo, Siyu Chen, Siyu Song, Yingxiong Liu, Yunqi Yan, Zhijun Mou, Chengbo Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access System Huazhong University of Science and Technology Wuhan430074 China

We demonstrate a narrow bandwidth passively erbium-doped mode-locked fiber laser based on single-wall carbon nanotubes (SWCNTs). The center wavelength is 1546.61 nm. The strong filtering effect and low saturable absorber (SA) modulation depth provide advantages for a long pulse duration of 323 ps. An overall spectral bandwidth of 1.3 GHz (0.01 nm) is measured by the optical heterodyne method, representing the narrowest spectral width based on SWCNT under a single filter structure. The obtained spectral width is very close to the transform-limited bandwidth of 8 pm, indicating that the pulse is chirp-free. Meanwhile, the generation of beat signals reveals the strong coherence between pulses. The repetition frequency of the laser is 7.8 MHz, and the number of longitudinal mode locks is only 166, which is the minimum number of longitudinal mode-locking in passively mode-locked laser based on material SA. At the same time, numerical simulations further support our experimental results and reveal the pulse evolution, providing evidence that SA and filter are the key factors in the generation of chirp-free long pulse. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Saturable absorbers

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Beamforming Design for RIS-Aided ISCC in Internet of Vehicles Systems

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IEEE Internet of Things Journal 2025年第9期12卷 12153-12165页

作者： Yang, Ruihang Wang, Dezhi Zhu, Shiyin Zhu, Chen Bao, Jianrong Yang, Zhaohui Huang, Chongwen Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Science and Technology of China Hefei230026 China New H3C Technologies Company Ltd. Hangzhou310052 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China

With the development of communication technology, the Internet of Vehicles (IoV) is becoming increasingly important, enabling vehicle-to-everything communication for real-time information exchange and processing, thereby significantly enhancing traffic efficiency and safety. In this article, we consider a joint beamforming design problem in IoV, where the objective is to minimize transmission power, computation rate, and communication rate within the integrated sensing, communication, and computation (ISCC) framework. Moreover, reconfigurable intelligent surfaces (RISs) can provide additional spatial degrees of freedom to enhance the performance of ISCC systems in IoV within limited spectrum, energy resources, and complex interference management. To address the joint beamforming design problem, we present a cooperative beamforming algorithm called weight performance optimization (WPO), which explores three single-objective optimization problems in sensing, computation, and communication within the IoV context, using alternating optimization (AO) to simplify and solve these foundational elements of the WPO framework within limited resources and vehicle mobility, enhancing resource distribution while maintaining a balance between power efficiency and system performance. Numerical results demonstrate the efficiency and potential advantages of our proposed algorithms. Specifically, the results show that the sensing error of the WPO algorithm is reduced by up to 92.2% compared to existing popular algorithms, while the computation rate and communication rate are increased by more than 29.5% and 23.9%, respectively. © 2014 IEEE.

关键词： Array signal processing Sensors Interference Heuristic algorithms Reconfigurable intelligent surfaces Real-time systems Optimization Resource management Reliability Complexity theory

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Statistical CSI based beamforming for reconfigurable intelligent surface aided MISO systems with channel correlation

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China communications 2025年第5期22卷 14-27页

作者： Li Haochen Pan Zhiwen Wang Bin Liu Nan You Xiaohu National Mobile Communications Research Laboratory (Southeast University) Nanjing 210096 China Purple Mountain Laboratories Nanjing 211100 China Science and Technology on Communication Networks Laboratory Hebei 050081 China

Reconfigurable intelligent surface (RIS) is a promising candidate technology of the upcoming Sixth Generation (6G) communication system for its ability to provide unprecedented spectral and energy efficiency increment through passive beamforming. However, it is challenging to obtain instantaneous channel state information (I-CSI) for RIS, which obliges us to use statistical channel state information (S-CSI) to achieve passive beamforming. In this paper, RIS-aided multiple-input single-output (MISO) multi-user downlink communication system with correlated channels is investigated. Then, we formulate the problem of joint beamforming design at the AP and RIS to maximize the sum ergodic spectral efficiency (ESE) of all users to improve the network capacity. Since it is too hard to compute sum ESE, an ESE approximation is adopted to reformulate the problem into a more tractable form. Then, we present two joint beamforming algorithms, namely the singular value decomposition-gradient descent (SVD-GD) algorithm and the fractional programming-gradient descent (FP-GD) algorithm. Simulation results show the effectiveness of our proposed algorithms and validate that 2-bits quantizer is enough for RIS phase shifts implementation.

关键词： Array signal processing Reconfigurable intelligent surfaces Reflection MISO Vectors Correlation Downlink Covariance matrices Spectral efficiency Approximation algorithms

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Dynamic fabrication method of SNAP microresonators

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Optics Letters 2025年第9期50卷 3042-3045页

作者： Wang, Zijie Crespo-Ballesteros, Manuel Collins, Christopher Yang, Yong Zhang, Qi Zhang, Xiaobei Sumetsky, Michael Aston Institute of Photonics Technology Aston University Aston Triangle BirminghamB47ET United Kingdom Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China

Surface nanoscale axial photonics (SNAP) technology has demonstrated the record subangstrom fabrication precision of optical microresonators and resonant photonic circuits at the optical fiber surface. However, fabrication errors arising from fluctuations of temperature, inscription parameters, alignment inconsistencies, and other factors did not allow researchers to achieve the subangstrom precision without sophisticated post-processing. Here, we show that the key fabrication method of SNAP structures—CO2 laser beam optical fiber annealing—suffers from significant fiber displacements, which may introduce a few percent fabrication errors. To suppress the effects of misalignment, we develop a dynamic fabrication method employing a translating beam exposure and demonstrate its excellent precision. An effective fiber radius variation of ∼10 nm is introduced with an error of ∼0.1 Å. We suggest that the remaining fabrication errors can be attributed to laser power fluctuations. © 2025 Optica Publishing Group. All rights,

关键词： Optical fiber fabrication

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TCN-based end-to-end optimization for IM-DD optical communication system 4

TCN-based end-to-end optimization for IM-DD optical communic...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhang, Tianqian Liu, Wu Zhong, Zhiyi Li, Han Luo, Ming Liu, Shouyin National Key Laboratory of Optical Communication Technologies and Networks China Information and Communication Technologies Group Corporation Wuhan430200 China College of Physical Science and Technology Central China Normal University Wuhan430070 China

ISBN: (纸本)9781510688834

In recent years, machine learning has greatly impacted optical communication systems, with end-to-end learning becoming a key focus. Originating from wireless communications, this approach treats the transmitter, channel, and receiver as a single neural network, forming an auto-encoder for global optimization. In this study, we propose a Temporal Convolutional Network (TCN)-based end-to-end framework for intensity-modulation and direct-detection (IM-DD) optical communication systems, targeting the efficient transmission of 32 Gbaud PAM4 signals. The TCN captures the temporal dependencies of the optical channel, while a one-dimensional convolutional neural network (1D-CNN) optimizes signal encoding at the transmitter. At the receiver, a residual CNN (Res-CNN) mitigates noise and distortion. Our approach improves receiver sensitivity by 1.4 dB and 0.88 dB compared to the Volterra-DFE and feed-forward equalization (FFE) at the receiver coupled with 1D-CNN transmitter, demonstrating its potential for future robust optical communications. © 2025 SPIE.

关键词： Convolutional neural networks

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HG2former: HSV-Gamma Guided Transformers for Efficient Underwater Image Enhancement

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IEEE Journal of Oceanic Engineering 2025年第2期50卷 866-878页

作者： Qing, Yuhao Shen, Liquan Fang, Zhijun Wang, Yueying Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai200051 China

Due to optical phenomena, such as the absorption and scattering of light in underwater environments, underwater images often suffer from degradation in color, contrast, clarity, and noise. Existing deep learning-based methods for underwater image enhancement typically learn a direct mapping from low-quality to high-quality underwater images, without fully considering the mapping of local luminance, chrominance, and contrast features. In this article, we propose a transformer model guided hue, saturation, value (HSV) and gamma correction for underwater image enhancement. The HG2former combines the HSV color model and gamma correction techniques to isolate the three fundamental characteristics of color, providing rich, differentiated enhancement for both color and contrast in underwater images. In addition, nonlinear gamma correction adaptively adjusts the brightness and contrast of images, addressing issues of visibility reduction and color distortion in underwater imaging. Furthermore, we introduce a meticulously designed encoder–decoder structure, along with an improved multihead self-attention module, to capture the spatial distribution patterns of underwater images while modeling both local and long-range dependencies. Extensive experimental results on multiple data sets demonstrate that the proposed HG2former outperforms other state-of-the-art methods. © 1976-2012 IEEE.

关键词： Color

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Mode Pilot-Tone (MPT)-based High-precision Crosstalk Monitoring for Mode-Division Multiplexing Systems

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Journal of Lightwave technology 2025年第11期43卷 5225-5234页

作者： Zhao, Tianfeng Wen, Feng Feng, Bianxia Wei, Jinlong Liang, Junpeng Tan, Mingming Wu, Baojian Xu, Bo Qiu, Kun University of Electronic Science and Technology of China Key Lab of Optical Fiber Sensing and Communication Networks Ministry of Education Chengdu611731 China Pengcheng Laboratory Shenzhen518000 China Aston University Aston Institute of Photonics Technologies BirminghamB4 7ET United Kingdom

Efficient and precise monitoring on mode-coupling-induced crosstalk (XT) is crucial for the operation of mode-division multiplexing (MDM) systems, particularly for optimizing digital signal processing (DSP) parameter configurations and maintaining stable transmission performance. In this paper, we propose a mode pilot-tone (MPT) monitoring scheme and investigate its performance for XT (MPT-XT) monitoring in a three-mode MDM transmission system. By deploying a photodiode (PD) with a 1 GHz bandwidth for each mode to extract the MPT responses, the complete XT information from 21 GBaud 16-QAM signals is obtained by the proposed scheme. In the experiment, the root-mean-square-error (RMSE) for XT-prediction achieves 0.11 dB, with a coefficient of determination R2 of 0.9992 across the OSNR range from 15dB to 25dB. By applying the transfer learning-based crosstalk neural network (TL-XT-NN) scheme, which transfers the NN model trained exclusively with simulation data to experimental XT monitoring scenarios, the training overhead required by the NN is reduced more than 48.3%. Moreover, we further evaluate the monitoring performance of the proposed scheme on the multiplexing density, the chromatic dispersion (CD), the differential mode group delay (DMGD), and the mode differential loss (MDL). Simulation results confirm its exceptional and consistent performance across all conditions. Finally, we investigate the impact of MPT modulation on the compensation behavior of multi-input multi-output (MIMO) equalizer and propose an inverse mapping label erasure (IMLE) scheme to mitigate the resulting signal degradation. Through the MPT erasure procedure, the error vector magnitude (EVM) penalty of transmitted signals caused by MPT operation is reduced from 2.92% to 0.19%. © 1983-2012 IEEE.

关键词： Chromatic dispersion

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Trustworthy deep learning for encrypted traffic classification

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Soft Computing 2025年第2期29卷 645-662页

作者： Li, Zheng Liu, Yanbei Zhang, Changqing Shan, Wanjin Zhang, Haifeng Zhu, Xiaoming School of Electronic and Information Engineering Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems Tianjin300387 China College of Intelligence and Computing Tianjin University Tianjin300072 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

Network traffic classification refers to the identification of collected network traffic data of various applications, which is widely used in research fields such as network resource allocation, traffic scheduling and intrusion detection systems. With the widespread application of encryption technology in the network, encrypted traffic classification has become a hot research topic. At present, most existing methods only focus on the accuracy of network traffic classification. Yet, few work studies the reliability of the classification model, which plays an important role in network regulation and network security. In this paper, we propose a novel traffic classification method based on trustworthy deep learning model, which can effectively improve the reliability of encrypted traffic classification models by correcting the confidence of model output. Specifically, we firstly perform data preprocessing on the original network traffic, and then adopt a ConvNet for feature learning and a ClassifyNet for traffic classification in the initial stage. At the same time, we utilize a trustworthy confidence criterion to design a ConfidNet trained according to the probability of the true class. The ConfidNet can provide a reliable confidence measure for the prediction of the classification model. Finally, we demonstrate the effectiveness of our framework through comprehensive experiments on two benchmark datasets ISCX VPN-nonVPN and USTC-TFC2016, and show that our method can improve the reliability of the classification model and has a good ability to identify misclassified samples compared with state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Network intrusion

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Controllable on-chip generation and detection of path-entangled microwave photons

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Physical Review A 2025年第1期111卷 013712-013712页

作者： Y. F. Li D. He S. R. He L. F. Wei Information Quantum Technology Laboratory International Cooperation Research Center of China Communication and Sensor Networks for Modern Transportation School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China

Path-entangled optical photons have played an important role in the multinode quantum network and quantum sensing, although most of them were usually generated probabilistically with linear optical platforms. Here, by using the spontaneous radiations of a pair of entangled solid-state qubits coupled to a common one-dimensional waveguide, we alternatively propose an on-chip approach to deterministically generate the path-entangled microwave single-photon state. With the trapped electrons on liquid helium, we specifically demonstrate its feasibility. In addition to the calculation of the von Neumann entropy for the entanglement witness, we show that the generated path-entanglement photons can be experimentally tested by simply measuring the distinguishability of the spontaneously emitted spectra of the photons being detected by the left and right single-photon detectors. The generalization of the proposal to the other solid-state experimental platforms is also discussed.

关键词： Quantum entanglement

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