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Characterization of State-Preparation Uncertainty in Quantum Key Distribution

Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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Deep Reinforcement Learning for Network Energy Saving in 6G and Beyond Networks

arXiv

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arXiv 2024年

作者： Tran, Dinh-Hieu Van Huynh, Nguyen Kaada, Soumeya Vo, Van Nhan Lagunas, Eva Chatzinotas, Symeon The University of Luxembourg Luxembourg The Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom The Department of computer science University of Rennes 1 Beaulieu Campus Rennes35000 France The Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam The Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving networks also needs to ensure the mobile users’ (MUs) QoS requirements such as throughput requirements (TR). This work considers a mobile cellular network including many ground base stations (GBSs), and some GBSs are intentionally turned off due to network energy saving (NES) or crash, so the MUs located in these outage GBSs are not served in time. Based on this observation, we propose the problem of maximizing the total achievable throughput in the network by optimizing the GBSs’ antenna tilt and adaptive transmission power with a given number of served MUs satisfied. Notice that, the MU is considered successfully served if its Reference Signal Received Power (RSRP) and throughput requirement are satisfied. The formulated optimization problem becomes difficult to solve with multiple binary variables and non-convex constraints along with random throughput requirements and random placement of MUs. We propose a Deep Q-learning-based algorithm to help the network learn the uncertainty and dynamics of the transmission environment. Extensive simulation results show that our proposed algorithm achieves much better performance than the benchmark schemes. © 2024, CC BY.

关键词： Energy saving

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Digital Twin-Empowered Network Planning for Multi-Tier Computing

arXiv

引用

arXiv 2022年

作者： Zhou, Conghao Gao, Jie Li, Mushu Shen, Xuemin Zhuang, Weihua The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The School of Information Technology Carleton University OttawaONK1S 5B6 Canada The Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada

In this paper, we design a resource management scheme to support stateful applications, which will be prevalent in 6G networks. Different from stateless applications, stateful applications require context data while executing computing tasks from user terminals (UTs). Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation. The coupling among different resources and the impact of UT mobility create challenges in resource management. To address the challenges, we develop digital twin (DT) empowered network planning with two elements, i.e., multi-resource reservation and resource reservation reconfiguration. First, DTs are designed for collecting UT status data, based on which UTs are grouped according to their mobility patterns. Second, an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost. Simulation results demonstrate that the proposed DT-empowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital storage

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Integrating Edge Intelligence and Blockchain: What, Why, and How

TechRxiv

引用

TechRxiv 2022年

作者： Wang, Xiaofei Ren, Xiaoxu Qiu, Chao Xiong, Zehui Yao, Haipeng Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin300072 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100088 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas. © 2022, CC BY-NC-SA.

关键词： Blockchain

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A Review and Benchmark on State-of-The-Art Steel Defects Detection

SSRN

引用

SSRN 2022年

作者： Anthony, Ashwin Chazhoor P. Ho, Edmond S.L. Woo, Wai Lok Gao, Bin Department of Computer and Information Sciences Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu610000 China

Steel has been used for centuries as a structural and fabricating material in various industries and is produced in large quantities across the globe with its demand increasing exponentially. It is imperative to the consumers that the steel products bought are free from any form of defects. The nature of steel manufacturing makes the defects imminent and detecting the defects on the surface of steel sheets during the quality analysis stage can be challenging. This is due to its dispersion across a large surface area along with variety in appearances. The limited availability of the defect data, together with the lengthy training time limits future studies on the identification of defects on steel surfaces. With the advancement of computer vision and deep learning, it is now possible to automate the task of defect detection and remove the human factor from the hazardous environment to a safe one to ensure the quality of the steel manufactured. The research investigates the performance evaluation of stateof-the-art object detection models to detect defects on surface steel strips. This study benchmarks the five existing state-of-theart object detection models (Faster R-CNN, Deformable DETR, Double Head R-CNN, Retinanet, and Deformable Convolutional Network) on steel surfaces on the Northeastern University (NEU) steel dataset yielding a detailed analysis of each model for the future researchers to enhance the models by combining different features of the baseline models in this paper. This research will also help in understanding the complexity of steel defect patterns and how different models behave distinctly to contrasting and homogeneous defects. Experimental results show that the Deformable Convolutional Network achieves the highest accuracy of 77.28% on the NEU dataset following a five-fold cross-validation method. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Research on machining accuracy of workpiece oriented to autonomous behavior of equipment 26

Research on machining accuracy of workpiece oriented to auto...

引用

26th International Conference on Automation and Computing, ICAC 2021

作者： Ma, Honglong Liu, Yefeng Sun, Weitang Liu, Yiming Zhang, Qichun Shenyang Institute of Technology Liaoning Key Lab. of Information Physics Fusion and Intelligent Manufacturing for Grade CNC Machine Fushun113122 China Shenyang Institute of Technology College of Mechanical and Vehicle Engineering Fushun113122 China University of Bradford Department of Computer Science BradfordBD7 1DP United Kingdom

ISBN: (纸本)9781860435577

Around the problem of workpiece machining accuracy, a model of autonomous behavior of equipment and the method of establishing patterns in knowledge base have been developed in this paper. Two self-learning models based on pattern comparison: deterministic knowledge and uncertain knowledge self-learning model are presented, while a scene understanding method for intelligent autonomous behavior of equipment is proposed. The research scheme of intelligent autonomous behavior optimization of equipment based on scene understanding and the specific research framework of workpiece machining precision are produced. Finally, focusing on the actual production process of bearing end cover, the flexible automatic production line of bearing end cover is established, the workpiece machining accuracy research based on independent behavior of equipment for CMM and CNC machine tools is preliminarily realized with the help of automatic line control end, it lays a solid foundation for the overall autonomous and intelligent cooperation between the testing area and the processing area. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Knowledge based systems

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Path Loss Modeling for NLoS Ultraviolet Channels Incorporating Scattering and Reflection Effects

arXiv

引用

arXiv 2024年

作者： Wu, Tianfeng Yang, Fang Li, Fei Yuan, Renzhi Cao, Tian Cheng, Ling Song, Jian Cheng, Julian Han, Zhu Department of Electronic Engineering BNRist Tsinghua University Beijing100084 China Hanjiang National Laboratory Wuhan430060 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Telecommunications Engineering Xidian University Xi’an710071 China School of Electrical and Information Engineering University of the Witwatersrand Johannesburg2000 South Africa Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

This paper tackles limitations in existing non-line-of-sight (NLoS) ultraviolet (UV) channel models, where conventional approaches assume obstacle-free propagation or uniform radiation intensity. In this paper, we develop a path loss model incorporating scattering and reflection, and then propose an obstacle-boundary approximation method to achieve computational tractability. Our framework systematically incorporates spatial obstacle properties, including dimensions, coordinates, contours, and orientation angles, while employing the Lambertian radiation pattern for source modeling. Additionally, the proposed path loss model is validated by comparing it with the Monte-Carlo photon-tracing model and analytical integral model via numerical results, which indicate that when obstacle reflection is prominent, an approximation treatment of obstacle boundaries has a negligible influence on the path loss estimation of NLoS UV communication channels. Copyright © 2024, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Wu, Wen Yang, Peng Zhang, Weiting Zhou, Conghao Shen, Xuemin The Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada The School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this paper, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability. © 2022, CC BY.

关键词： Deep neural networks

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Interplay between Physical Layer Security and Blockchain technology for 5G and Beyond: A Comprehensive Survey

TechRxiv

引用

TechRxiv 2022年

作者： Ghourab, Esraa M. Jaafar, Wael Bariah, Lina Naser, Shimaa Muhaidat, Sami Yanikomeroglu, Halim The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates The Department of Software and Information Technology Engineering Ecole de Technologie Supérieure MontrealQCH3C 1K3 Canada Abu Dhabi9639 United Arab Emirates The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada The Department of Systems and Computer Engineering Carleton University OttawaON Canada

Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of vertical applications like high traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, their deployment poses several challenges, including a lack of network transparency, management decentralization, and reliability. Moreover, the heterogeneity of future networks raises security concerns, e.g., confidentiality, privacy, and trustworthiness. Indeed, due to the emergence of novel paradigms, e.g., quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Hence, 5G/xG networks must consider smart security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of communications, while blockchain may enable networks’ decentralization, integrity, and trustworthiness. Motivated by these advancements, we provide an in-depth review of the existing PLS and blockchain literature. Then, for the first time in the literature, we present a framework to integrate PLS with blockchain in 5G/xG systems. We first provide a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Then, we present our vision of a cross-layer architecture that leverages PLS and blockchain. Through a case study, we demonstrate the high potential of cross-layer design to improve the security of vehicular networks. Finally, we identify related challenges and shed light on future research directions. © 2022, CC BY.

关键词： MIMO systems

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Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities

SSRN

引用

SSRN 2022年

作者： Khalid, Muhammad Wang, Liang Wang, Kezhi Pan, Cunhua Aslam, Nauman Cao, Yue Computer Science and Technology University of Hull United Kingdom Department of Computer and Information Science Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS United Kingdom School of Cyber Science and Engineering Wuhan University Wuhan430072 China

In this paper, to reduce the congestion rate at the city center and increase the travelling quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAVP) is presented, where an Autonomous Vehicle (AV) is deployed can pick up, drop off users at their required spots, and then drive to the car park around well organized places of city autonomously. In this framework, we aim to minimize the overall distance of the AV, while guarantee all users are served with great QoE, i.e., picking up, and dropping off users at their required spots through optimizing the path planning of the AV and number of serving time slots. To this end, we first propose a learning based algorithm, which is named as DoubleLayer Ant Colony Optimization (DL-ACO) algorithm to solve the above problem in an iterative way. Then, to make the realtime decision, while considers the dynamic environment (i.e., the AV may pick up and drop off users from different locations), we further present a deep reinforcement learning (DRL) based algorithm, which is known as deep Q-learning network (DQN). The experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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