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2021 Applied Optics and Photonics China: Optoelectronics and Nanophotonics, AOPC 2021

作者： Wang, Yimeng Shu, Haowen Han, Xiuyou School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian116024 China State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510649996

Integrated optical temperature sensor nowadays has attracted extensive interests thanks to its merits of high accuracy, electromagnetic immunity, and compact size compared with the traditional solutions. So far, most of the state-of-art integrated optical temperature sensors work by spectrum detection of a resonant cavity, which has certain limitations in terms of response time and cost, and it is difficult to achieve high level integration of the overall system. To solve the above problems, in this paper we use silicon-based integrated micro-ring array technology for high-precision real-time temperature sensing. By constructing a monotonic relationship between the output light intensity of the micro-ring array with the variation of the temperature, rapid and high-precision temperature measurement could be realized. Depending on different practical scenarios, the system can be customized to adapt specific temperature measurement range and resolution. Through the optimized design, the system accuracy could be better than 60mK covering temperature range of 297K∼415K, with response time better than 20μs. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Temperature measurement

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Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access networks: An Overview

Deep Reinforcement Learning for Sleep Control in 5G and Beyo...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Nessrine Trabelsi Rihab Maaloul Lamia Chaari Fourati Wael Jaafar Laboratory of Signals systeMs aRtificial intelligence and neTworkS – Digital Research Center of Sfax Sfax Tunisia Higher Institute of Computer Science of Mahdia (ISIMa) University of Monastir Tunisia Higher Institute of Computer Science and Multimedia in Sfax (ISIMS) University of Sfax Tunisia Department of Software and IT Engineering École de technologies supérieure (ÉTS) Montreal Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The advent of 5G and beyond networks is envisioned to support lower latency, higher data rates, and wider connectivity than previous cellular network generations. However, given the denser deployment of base stations (BSs) to accommodate such improvements, this results inevitably in a significant and unsustainable increase in the network’s energy consumption. Sleep Control (SC), which allows switching off some BS hardware components during light-traffic time, is considered a viable solution for greener and more energy-efficient Radio Access networks (RAN). However, the optimization of SC is a highly challenging large-scale network combinatorial problem that depends on dynamic wireless channel conditions and varying traffic demands with stringent Quality-of-Service (QoS) requirements. Driven by the benefits and efficiency of Deep Reinforcement Learning (DRL), which has been successfully applied to multiple wireless network optimization problems, this paper investigates DRL approaches addressing sleep control in 5G and beyond RAN. To this end, we propose a taxonomy to classify the related literature. Then, we provide an overview of the different components of the Markov Decision Process (MDP) modeling the sequential decision-making of sleep control and the applied DRL algorithms. Finally, we highlight the main challenges in existing works and suggest novel strategies to address them.

关键词： 5G mobile communication Wireless networks Taxonomy Decision making Quality of service Switches Deep reinforcement learning

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LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

arXiv

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arXiv 2024年

作者： Tian, Jiwei Shen, Chao Wang, Buhong Xia, Xiaofang Zhang, Meng Lin, Chenhao Li, Qian Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Air Traffic Control and Navigation College Air Force Engineering University Xi’an China Information and Navigation College Air Force Engineering University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

—Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids. © 2024, CC BY.

关键词： Location

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Data Collection and Trajectory Planning for Energy-Constrained UAV Swarm: A Q-Learning based Approach

Data Collection and Trajectory Planning for Energy-Constrain...

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Chinese Control Conference (CCC)

作者： Yuan Gao Heng Zhang Jianwei Sun Xin Wang Ming Yang Xueli Wang Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China School of Computer Engineering Jiangsu Ocean University Lianyungang P. R. China School of Electronic Engineering Jiangsu Ocean University Lianyungang P. R. China

This paper studies the energy-constrained path planning of multiple unmanned aerial vehicles (UAVs) based on the data collection of ground sensors in large-scale farms to achieve crop data monitoring. To reduce the energy consumption of the UAV swarm and improve the task completion rate, when all nodes are connected, the relay UAV is dynamically allocated according to the location information of the UAV swarm. The relay UAV collects all data and flies to the base station for data transmission. According to this scenario, a multi-UAV trajectory optimization problem including task time, energy consumption and node access constraints is established. Then, an optimization algorithm for UAV swarm trajectory design is proposed. This algorithm is based on Q-learning and takes the changes in the distance between the UAV and the node, the UAV's flight angle, and the safe distance as the reward function. The Q table is updated according to the awards to guide the flight route of the UAV. The analysis shows that the proposed algorithm can effectively improve the completion rate of data collection tasks and reduce the overall energy consumption of the UAV swarm.

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Data-Driven Optimal Control of Traffic Signals for Urban Road networks

Data-Driven Optimal Control of Traffic Signals for Urban Roa...

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IEEE Conference on Decision and Control

作者： Tong Liu Hong Wang Zhong-Ping Jiang Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper studies the issue of data-driven optimal control design for traffic signals of oversaturated urban road networks. The signal control system based on the store and forward model is generally uncontrollable for which the controllable decomposition is needed. Instead of identifying the unknown parameters like saturation rates and turning ratios, a finite number of measured trajectories can be used to parametrize the system and help directly construct a transformation matrix for Kalman controllable decomposition through the fundamental lemma of J. C. Willems. On top of that, an infinite-horizon linear quadratic regulator (LQR) problem is formulated considering the constraints of green times for traffic signals. The problem can be solved through a two-phase data-driven learning process, where one solves an infinite-horizon unconstrained LQR problem and the other solves a finite-horizon constrained LQR problem. The simulation result shows the theoretical analysis is effective and the proposed data-driven controller can yield desired performance for reducing traffic congestion.

关键词： Regulators Roads Simulation Process control Optimal control Turning Trajectory

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Synesthesia of Machines (SoM)-Enhanced Wideband Multi-User CSI Learning With LiDAR Sensing

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Haotian Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Photonics and Communications School of Electronics Beijing100871 China Guangzhou511400 China The Chinese University of Hon g Kong Guangdong Provincial Key Laboratory of Future Networks of Intelligence Shenzhen China Internet of Things Thrust Intelligent Transportation Thrust Guangzhou510000 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Light detection and ranging (LiDAR) has been utilized for optimizing wireless communications due to its ability to detect the environment. This paper explores the use of LiDAR in channel estimation for wideband multi-user multiple-input-multiple-output orthogonal frequency division multiplexing systems and introduces a LiDAR-Enhanced Channel State Information (CSI) Learning Network (LE-CLN). By utilizing user positioning information, LE-CLN first calculates user-localized over-complete angular measurements. It then investigates the correlation between LiDAR and CSI, transforming raw LiDAR data into a low-complexity format embedded with signal propagation characteristics. LE-CLN also adapts the use of LiDAR based on channel conditions through attention mechanisms. Thanks to the unique wireless features offered by LiDAR, LE-CLN achieves higher estimation accuracy and spectrum efficiency compared to benchmarks, particularly in latency-sensitive applications where pilot transmissions are expected to be reduced. © 1967-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Thing

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IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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A Novel High-Gain Magneto-Electric Dipole Antenna Fed by ISGW 13

A Novel High-Gain Magneto-Electric Dipole Antenna Fed by ISG...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Zhou, Hang Shen, Dongya Yuan, Hong Zhang, Xiupu Zhou, Liping Yunnan University Yunnan Provincial Engineering Laboratory Of Cloud Wireless Access Heterogeneous Networks Kunming650500 China Concordia University IPhotonics Labs Department Of Electrical And Computer Engineering MontrealH3G1M8 Canada

ISBN: (纸本)9781665434379

A high-gain magneto-electric (ME) dipole antenna fed by Integrated Substrate Gap Waveguide (ISGW) for use in 5G communication systems is proposed. The ME dipole antenna is mainly composed of two pairs of printed dipoles grounded through metallized vias and a constructed current loop through ISGW, and one dipole is used as the electric dipole and the other for the magnetic dipole. The impedance bandwidth of this antenna ranges from 23.63 to 29.61 GHz (22.5%), and the average gain is higher than 9.56 dB. In addition, a stable gain and symmetrical radiation pattern are obtained. © 2021 IEEE.

关键词： Directional patterns (antenna)

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Intelligent Energy-Efficient Resource Allocation for Multi-UAV-Assisted Mobile Edge Computing networks

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China Communications 2025年第4期22卷 339-355页

作者： Hu Han Shen Le Zhou Fuhui Wang Qun Zhu Hongbo The China Information Consulting and Designing Institute CO. Ltd.Nanjing 210000China The Jiangsu Key Laboratory of Wireless Communications(Nanjing University of Posts and Telecommunications) Nanjing 210003China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education(Nanjing University of Posts and Telecommunications)Nanjing 210003China The College of Artificial Intelligence(Nanjing University of Aeronautics and Astronautics) Nanjing 210000China The Department of Computer Science(San Francisco State University) San FranciscoCAUSA

The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency ***,the multi-UAVassisted MEC network remains largely *** this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground *** considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is *** address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,*** results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.

关键词： dynamic trajectory optimization intelligent resource allocation unmanned aerial vehicle uav assisted uav assisted mec energy efficiency smart applications mobile edge computing mec deep reinforcement learning

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Kerr effect in ultra-compact hybrid plasmonic metal-insulator-metal nano-focusing structure

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Chinese Optics Letters 2022年第3期20卷 106-110页

作者： Kejian Zhu Pengfei Sun F|engfei Xu Xingpeng Liu Tangyou Sun Haiou Li Zhiping Zhou State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologyGuilin 541004China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China

Nano-focusing structures based on hybrid plasmonic waveguides are likely to play a key role in strong nonlinear optical *** the insertion loss is considerable,a significant nonlinear phase shift may be achieved by decreasing the nano-focusing device footprint and careful parameter ***,we study the Kerr effect in hybrid plasmonic waveguides by analyzing the mode effective area,energy velocity,and insertion ***,by utilizing plasmonics to manipulate the effective index and mode similarity,the TM mode is reflected and absorbed,while the TE mode passes through with relatively low propagation *** providing a deep understanding of hybrid plasmonic waveguides for nonlinear applications,we indicate pathways for their future optimization.

关键词： silicon photonics plasmonics nonlinear optics nano-focusing

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