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arXiv 2021年

作者： Dressler, Falko Chiasserini, Carla Fabiana Fitzek, Frank H.P. Karl, Holger Cigno, Renato Lo Capone, Antonio Casetti, Claudio Malandrino, Francesco Mancuso, Vincenzo Klingler, Florian Rizzo, Gianluca The School of Electrical Engineering and Computer Science TU Berlin Berlin Germany The Dept. of Electronics and Telecommunications Politecnico di Torino Torino Italy The School of Engineering Sciences as well as the Excellence Cluster CeTI TU Dresden Dresden Germany The Hasso-Plattner-Institute Potsdam Germany The Dept. of Information Engineering University of Brescia Brescia Italy The Dept. of Electronics Information and Bioengineering Politecnico di Milano Milano Italy The Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy The Institute of Electronics Computer and Communication Engineering of the National Research Council of Italy Torino Italy IMDEA Networks Madrid Spain The Dept. of Computer Science Paderborn University Paderborn Germany The Research Institute of Information Systems HES-SO Valais-Wallis Switzerland University of Foggia Italy

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays that are just owed to limited propagation speed, edge servers deployed in close proximity to the user, e.g., at some 5G gNB, serve as proxy for the cloud. Yet this promising idea is hampered by the limited availability of such edge servers. In this paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge bridges the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available using well-defined interfaces. V-Edge also acts as an enabler for novel microservices as well as cooperative computing solutions. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome, in order to enable wide-spread and even more powerful edge services. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Classification of spot-welded joints in laser thermography data using convolutional neural networks

arXiv

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arXiv 2020年

作者： Kästner, Linh Ahmadi, Samim Jonietz, Florian Ziegler, Mathias Jung, Peter Caire, Giuseppe Lambrecht, Jens Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany Bundesanstalt für Materialforschung Berlin Germany Chair Communication and Information Theory Berlin Institute of Technology Berlin Germany

Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this paper, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Spot welding

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EDGE AI for Heterogeneous and Massive IoT networks 19

EDGE AI for Heterogeneous and Massive IoT Networks

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19th IEEE International Conference on communication Technology, ICCT 2019

作者： Chen, Sifan Gong, Peng Wang, Bin Anpalagan, Alagan Guizani, Mohsen Yang, Chungang Xidian University State Key Laboratory on Integrated Services Networks Xi'an China Beijing Institute of Technology Beijing China Xi'An University of Science and Technology School of Communication Engineering Xi'an China Ryerson University Department of Electrical and Computer Engineering Toronto Canada Qatar University Computer Science and Engineering Department Qatar

ISBN: (纸本)9781728105352

By combining multiple sensing and wireless access technologies, the Internet of Things (IoT) shall exhibit features with large-scale, massive, and heterogeneous sensors and data. To integrate diverse radio access technologies, we present the architecture of heterogeneous IoT system for smart industrial parks and build an IoT experimental platform. Various sensors are installed on the IoT devices deployed on the experimental platform. To efficiently process the raw sensor data and realize edge artificial intelligence (AI), we describe four statistical features of the raw sensor data that can be effectively extracted and processed at the network edge in real time. The statistical features are calculated and fed into a back-propagation neural network (BPNN) for sensor data classification. By comparing to the k-nearest neighbor classification algorithm, we examine the BPNN-based classification method with a great amount of raw data gathered from various sensors. We evaluate the system performance according to the classification accuracy of BPNN and the performance indicators of the cloud server, which shows that the proposed approach can effectively enable the edge-AI-based heterogeneous IoT system to process the sensor data at the network edge in real time while reducing the demand for computing and network resources of the cloud. © 2019 IEEE.

关键词： Edge computing

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Data-driven based iterative learning control for unknown linear discrete-time system by adaptive system identification 8

Data-driven based iterative learning control for unknown lin...

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8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019

作者： Zhu, Wenbo Fang, Yong Huang, Lixun Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450000 China

ISBN: (纸本)9781728114545

This paper considers an iterative learning control (ILC) scheme for unknown linear discrete-time system. ILC updates the system input using error information from previous iteration to sequentially improve the tracking performance. The asymptotic convergence may not be guaranteed when the system model information is uncertain. In this paper, we introduce the adaptive Fourier decomposition (AFD) algorithm to deal with the system identification problem. This adaptive approximation algorithm estimates a system representation using input/output data only. The learning gain of ILC satisfying the convergence condition is obtained according to the estimated system parameters. The effectiveness of system approximation and the tracking performance of ILC based on input/output measurements are verified with simulation results. © 2019 IEEE.

关键词： Approximation algorithms

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Robust Deep Learning-Based Physical Layer communications: Strategies and Approaches

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Zhu, Chen Gong, Tierui Yang, Zhaohui Huang, Chongwen Chen, Xiaoming Zhang, Zhaoyang Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Polytechnic Institute Zhejiang University Hangzhou310015 China Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

Deep learning (DL) has emerged as a transformative technology with immense potential to reshape the sixth-generation (6G) wireless communication network. By utilizing advanced algorithms for feature extraction and pattern recognition, DL provides unprecedented capabilities in optimizing the network efficiency and performance, particularly in physical layer communications. Although DL technologies present the great potential, they also face significant challenges related to the robustness, which are expected to intensify in the complex and demanding 6G environment. Specifically, current DL models typically exhibit substantial performance degradation in dynamic environments with time-varying channels, interference of noise and different scenarios, which affect their effectiveness in diverse real-world applications. This paper provides a comprehensive overview of strategies and approaches for robust DL-based methods in physical layer communications. First we introduce the key challenges that current DL models face. Then we delve into a detailed examination of DL approaches specifically tailored to enhance robustness in 6G, which are classified into data-driven and model-driven strategies. Finally, we verify the effectiveness of these methods by case studies and outline future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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Robust Deep Learning-Based Physical Layer communications: Strategies and Approaches

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IEEE Network 2025年

作者： Zhu, Fenghao Wang, Xinquan Zhu, Chen Gong, Tierui Yang, Zhaohui Huang, Chongwen Chen, Xiaoming Zhang, Zhaoyang Debbah, Merouane Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Zhejiang University Polytechnic Institute Hangzhou310015 China Nanyang Technological University 639798 Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates

关键词： Feature Selection

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UAV communication Channel Measurement, Modeling, and Application

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Journal of communications and Information networks 2019年第4期4卷 32-43页

作者： Xiang Cheng Yiran Li Lin Bai State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shenzhen Research Institute of Big Data Shenzhen 518000China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

As unmanned aerial vehicles(UAV)deploy-ment is shifting from military to civilian sectors,and in particular as a means of facilitating more flexible and versatile wireless communications,the UAV channels that were previously not well understood are attracting increasing research and *** the key component facilitating the UAV-aided communications,UAV channel characteristics and modeling are of critical importance to the designed UAV system to achieve satisfactory *** this article,we will provide a comprehensive overview and the future perspective of envisioned UAV communication system benefits,the existing and needed UAV channel measurements and modeling approaches,together with the new viewpoint of UAV channel applications in different communication and networking *** existing work and future directions are extensively covered.

关键词： UAV channel measurements modeling application future challenges

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Blackout resilient optical core network

arXiv

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arXiv 2020年

作者： Nasralla, Zaid H. Elgorashi, Taisir E.H. Elmirghani, Jaafar M.H. Computer Science Department College of Science University of Kerbala Karbala56001 Iraq School of Electronic and Electrical Engineering Institute of Communication and Power Networks University of Leeds LeedsLS2 9JT United Kingdom

A disaster may not necessarily demolish the telecommunications infrastructure, but instead it might affect the national grid and cause blackouts, consequently disrupting the network operation unless there is an alternative power source(s). In this paper, power outages are considered, and the telecommunication network performance is evaluated during a blackout. Two approaches are presented to minimize the impact of power outage and maximize the survival time of the blackout node. A mixed integer linear programming (MILP) model is developed to evaluate the network performance under a single node blackout scenario. The model is used to evaluate the network under the two proposed scenarios. The results show that the proposed approach succeeds in extending the network life time while minimizing the required amount of backup energy. Copyright © 2020, The Authors. All rights reserved.

关键词： Outages

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Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging

arXiv

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arXiv 2020年

作者： Ahmadi, Samim Hauffen, Jan Christian Kästner, Linh Jung, Peter Caire, Giuseppe Ziegler, Mathias Bundesanstalt für Materialforschung und -prüfung Berlin Germany Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany Chair Communication and Information Theory Berlin Institute of Technology Berlin Germany

Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameter, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging. © 2020, CC BY.

关键词： Mean square error

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Soliton bursts and deterministic dissipative Kerr soliton generation in auxiliary-assisted microcavities

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Light(Science & Applications) 2019年第1期8卷 739-748页

作者： Heng Zhou Yong Geng Wenwen Cui Shu-Wei Huang Qiang Zhou Kun Qiu Chee Wei Wong Key Lab of Optical Fiber Sensing and Communication Networks University of Electronic Science and Technology of China611731 ChengduChina Fang Lu Mesoscopic Optics and Quantum Electronics Laboratory University of CaliforniaLos AngelesCA 90095USA Department of Electrical Computerand Energy EngineeringUniversity of ColoradoBoulderCO 80309USA Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China611731 ChengduChina

Dissipative Kerr solitons in resonant frequency combs offer a promising route for ultrafast mode-locking,precision spectroscopy and time-frequency *** dynamics for the dissipative soliton generation,however,are intrinsically intertwined with thermal nonlinearities,limiting the soliton generation parameter map and statistical success probabilities of the solitary ***,via use of an auxiliary laser heating approach to suppress thermal dragging dynamics in dissipative soliton comb formation,we demonstrate stable Kerr soliton singlet formation and soliton ***,we access a new soliton existence range with an inverse-sloped Kerr soliton evolution—diminishing soliton energy with increasing pump ***,we achieve deterministic transitions from Turinglike comb patterns directly into the dissipative Kerr soliton singlet pulse bypassing the chaotic *** is achieved by avoiding subcomb overlaps at lower pump power,with near-identical singlet soliton comb generation over twenty ***,with the red-detuned pump entrance route enabled,we uncover unique spontaneous soliton bursts in the direct formation of low-noise optical frequency combs from continuum background *** burst dynamics are due to the rapid entry and mutual attraction of the pump laser into the cavity mode,aided by the auxiliary laser and matching well with our numerical *** by the auxiliary-assisted frequency comb dynamics,we demonstrate an application of automatic soliton comb recovery and long-term stabilization against strong external *** findings hold potential to expand the parameter space for ultrafast nonlinear dynamics and precision optical frequency comb stabilization.

关键词： soliton auxiliary dissipative

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