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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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Make Power Allocation More Adaptive in Ultra Dense networks: Priority-Driven Deep Reinforcement Learning via Noise-Perturbations

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IEEE Transactions on Wireless communications 2025年第5期24卷 4010-4023页

作者： Sun, Xiaochuan Han, Jinpeng Li, Yingqi Zhu, Kaiyu Zhang, Haijun Hebei Key Laboratory of Industrial Intelligent Perception Tangshan063210 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China

Efficient and stable resource management in ultra-dense networks is essential for interference reduction and quality of service guarantee for large-scale users. Although reinforcement learning is currently the most talked-about method for achieving efficient resource allocation, it still faces significant challenges when dealing with the key adaptive issues, such as strong interference, large-scale users dynamic demands, and differentiated user priority requirements. In the case, this paper proposes a pervasive power allocation method in the framework of deep reinforcement learning. Concretely, we develop a noisy deep Q-network for guaranteeing stable exploration of average rate and accelerating the model convergence, where the parameterized noise is incorporated into the connection weights of neural networks. On the other hand, considering the varying priority levels of different users, we devise a prioritized experience replay mechanism aimed at enhancing the service quality for users with high importance. Simulation results demonstrate that our proposal can surpass the state-of-the-art methods in terms of average rate, convergence, stability, and model complexity, achieving more adaptive power allocation. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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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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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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Compact Shared Aperture Metasurface with Power Splitting for Simultaneous Wireless Information and Power Transfer

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IEEE Internet of Things Journal 2025年第10期12卷 14386-14397页

作者： Yu, Fan Yang, Xue-Xia Dong, Shiwei Tan, Guannan Gao, Steven Shanghai University School of Communication and InformationEngineering Shanghai200444 China Shanghai University Key Laboratory of Specialty Fiber Optics AndOptical Access Networks Shanghai200444 China China Academy of Space Technology National Key Laboratory of Science and Technology on Space Microwave Xi'an710100 China Huizhou Speed Wireless Technology Company Huizhou516000 China ChineseUniversity of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong

Simultaneous wireless information and power transfer (SWIPT) with a power-splitting (PS) scheme has been extensively studied for Internet of Things (IoT) applications. However, the PS scheme usually employs a power divider or hybrid coupler for splitting the radio frequency (RF) signal, which makes the receiver structure complex and introduces extra losses. To address this challenge, we propose a novel PS scheme for SWIPT using a shared aperture metasurface (SAM). The SAM includes a rectifying metasurface on the first substrate layer and a communication metasurface antenna (meta-antenna) on the second one. The rectifying metasurface can harvest most RF signals as energy and transmit a small portion for communication, whose ratio can be adjusted by the etched gap width between two substrate layers. A prototype with 8.5:1 ratio of energy to information is designed and fabricated. The measured results show that the rectifying metasurface has a radio frequency (RF) to direct current (DC) conversion efficiency over 50% at any polarization direction within a wide reception range of 45° oblique incidence. A maximum data rate of 350 Mbps for 128-QAM is measured when bit error rate (BER) is lower than 10-6 and signal-to-noise ratio (SNR) is lower than -30 dB. An SWIPT system demonstration employing the proposed SAM for environmental monitoring sensor nodes is presented. When sensors receive the RF signal from the transmitter, they can convert it to DC power for self-powering and send the environmental data back to the transmitter. © 2014 IEEE.

关键词： Inductive power transmission

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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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Measuring weak microwave signals via current-biased Josephson junctions: Approaching the quantum limit of energy detection

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Physical Review B 2025年第2期111卷 024501-024501页

作者： Y. Q. Chai S. N. Wang P. H. OuYang 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

Single-photon detection is an energy quantum limit detection (EQLD) of extremely weak electromagnetic waves. Given that the sensitivity of the conventional electromagnetic induction microwave receiver working at room temperature is very limited, as a result of the significantly strong thermal noise, here we analyze the possibility of approaching the EQLD of the weak microwave signal by using a current-biased Josephson junction (CBJJ) detector. By numerically simulating the dynamics for the phase particle of the CBJJ, we propose an approach to describe the discriminability between the probabilistically escaped events of the phase particle with and without the microwave current driving, by measuring the minimum dKC index. Our predictions indicate that the experimentally demonstrated CBJJ detector possesses the ability to resolve about a dozen photons. Furthermore, with the minimum detectable signal strength and the saturation threshold, we discuss how to determine the dynamic range of the proposed CBJJ detector. The feasibility of using the proposed CBJJ detector to achieve the EQLD in a microwave band is also discussed.

关键词： Josephson junction devices

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