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19. ITG-Fachtagung Photonische Netze - 19th ITG Conference on Photonic networks

作者： Parthasarathy, Swaminathan Giggenbach, Dirk Fuchs, Christian Mata-Calvo, Ramon Barrios, Ricardo Kirstädter, Andreas WesslingD-82234 Germany Institute of Communication Networks and Computer Engineering University of Stuttgart StuttgartD-70569 Germany

ISBN: (纸本)9783800746842

High-Altitude Platforms/High-Altitude Pseudo Satellites (HAPs) will extend Internet access to currently uncovered regions. Free-Space Optical (FSO) links will interconnect HAPs over long distances through the stratosphere. Their Forward Error Correction has to be carefully adapted to the atmospheric scintillation channel. Long delay impairs the exploitation of receiver-based channel state information (CSI). Only inherent CSI provided by channel reciprocity allows in time control. Investigation results of this fading-signal correlation phenomenon over long distances however have not yet been reported. Therefore, we experimentally study this effect in a long-range (63km) monostatic bidirectional atmospheric FSO link for a wide range of parameters. Numerical phase-screen simulations of the transmission scenario confirm the results. © 2020 Photonische Netze - 19. ITG-Fachtagung. All rights reserved.

关键词： Channel state information

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MIMO-Terahertz in 6G Nano-communications:Channel Modeling and Analysis

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computers, Materials & Continua 2021年第1期66卷 263-274页

作者： Shahid Bashir Mohammed H.Alsharif Imran Khan Mahmoud A.Albreem Aduwati Sali Borhanuddin Mohd Ali Wonjong Noh Business Studies Department Namal InstituteMianwali42250Pakistan Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006South Korea Department of Electrical Engineering University of Engineering and Technology PeshawarPeshawar814Pakistan Department of Electronics and Communications Engineering College of EngineeringA’Sharqiyah UniversityIbraOman Wireless and Photonics Networks Research Centre of Excellence Department of Computer and Communication Systems EngineeringUnversiti Putra MalaysiaSelangor43400Malaysia School of Software Hallym UniversityHallymdaehak-gilChunchoen-si24252South Korea

With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by *** wireless mobile communication frequency tends to be saturated,which demands for new ***(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information *** communication has great application prospects in military-civilian integration and coordinated *** China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the ***,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond *** paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel ***,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the *** results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.

关键词： Wireless communication 6G mobile communication terahertz communication MIMO channel modeling

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Ensemble Learning-based Traffic Classification with Small-Scale Datasets for Wireless networks

Ensemble Learning-based Traffic Classification with Small-Sc...

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IEEE Conference on computer communications Workshops, INFOCOM Wksps

作者： Xiaorong Wang Wenting Wei Xiaoshan Yu Danyang Zheng Neeraj Kuma Lei Liu State Key Laboratory of Integrated Service Networks Xidian University Xi'an China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Guangzhou Institute of Technology Xidian University Xi'an China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Traffic classification is an essential part of intelligent network management in Fifth generation (5G) networks, from quality of service guarantees to security monitoring. With the evolution of diverse services and applications over 5G networks, traffic patterns and their implications also becoming more complicated, especially in wireless networks. How to dynamically manage the growing traffic demand and satisfy the quality of service (QoS) needs of new applications is particularly critical. The biggest challenge is how to identify traffic types accurately without application information and manual features. However, the existing deep learning (DL)-based classifiers heavily rely on extensive and high-quality samples, which proves impractical to obtain in wireless networks. To overcome this limitation, we propose an ensemble Learning based traffic classification model to provide accurate identification with small-scale datasets, where Attention-based Long Short-Term Memory networks are bagged to train this classifier. The ensemble learning approach employs a combinatorial design involving five base models to construct more accurate and robust learning models. This enables effective feature absorption, making it feasible to train classifiers on limited sample sets. Comprehensive experiments are conducted using a real-world dataset covering 16 applications. Results demonstrate that, even with a mere 10% of training samples, our proposed model achieves a classification accuracy of 95.57% and a precision of 95.83%, surpassing multiple cutting-edge methods.

关键词： Training Accuracy 5G mobile communication Wireless networks Computational modeling Quality of service Telecommunication traffic

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Federated Edge Learning via Integrated Sensing, Computation, and communication

Federated Edge Learning via Integrated Sensing, Computation,...

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IEEE International Conference on communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

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Energy, Materials and communication engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

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Comparative Study of Quantum and Classical Error Correction for Future Quantum-6G networks

TechRxiv

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TechRxiv 2023年

作者： Das, Siddharth Bassoli, Riccardo Ferrara, Roberto Nötzel, Janis Deppe, Christian Fitzek, Frank H.P. Boche, Holger The Deutsche Telekom Chair of Communication Networks Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany research group Technische Universität Dresden Dresden Germany The Department of Electrical and Computer Engineering Technische Universität München München Germany München Germany Ruhr University Bochum Bochum44801 Germany

The research on 6G communication networks has started. Currently, the proposed architectures envision the realisation of a fully softwarized network continuum, with in-network intelligence for network management and operations. Moreover, more stringent requirements than 5G have been set in order to support new use cases like telepresence and massive twinning. However, these requirements implies some critical tradeoffs, which are going to limit the promises of 6G. In order to go beyond the intrinsic limitations of 6G, new resources and technologies should be found. That is why, some research groups are now investigating how quantum technologies can be integrated into 6G. This works deals with the scenario of sending classical information via a quantum channel, using the so-called dense coding protocol. If this channel is not ideal, quantum errors can affect the reliability of the communication. Nevertheless, quantum error-correcting codes are still very demanding in terms of resources and redundancy. Then, this work investigates how classical error-correcting codes (in particular, Reed-Solomon codes and turbo codes) can be applied to make reliable classical communications via quantum channels. The preliminary results of this paper show that the employment of classical forward error correction in future hybrid 6G-quantum networks can be very promising. © 2023, CC BY.

关键词： Reed-Solomon codes

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UAV-Assisted Downlink-and-Uplink communication in the Presence of Multiple Malicious Jammers

UAV-Assisted Downlink-and-Uplink Communication in the Presen...

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IEEE Conference on Wireless communications and Networking

作者： Zhiyu Huang Zhichao Sheng Ali A. Nasir Cheng Yin Antonino Masaracchia Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Department of Electrical Engineering and Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Institute for Communication Systems University of Surrey U.K. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast U.K.

This paper investigates the unmanned aerial vehicle (UAV)-assisted communication network with multiple downlink users (DLUs) and uplink users (ULUs) in the presence of multiple malicious jammers. To guarantee fairness among the users and their uplink and downlink communication throughput, we aim to maximize the minimum average throughput by jointly optimizing the scheduling of ULUs/DLUs, three dimensional (3D) trajectory and the UAV transmission power. Although the optimization problem is computationally intractable due to its non-convexity, we develop an iterative algorithm based on the block coordinate descend approach and the successive convex approximation technique to solve the problem efficiently. Numerical outcomes show that our proposed algorithm can improve throughput significantly over several benchmark schemes.

关键词： Three-dimensional displays Processor scheduling Benchmark testing Throughput Autonomous aerial vehicles Downlink Scheduling

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E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

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Co‑packaged optics(CPO):status,challenges,and solutions

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Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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Deep Reinforcement Learning Based Multistage Profit Aware Task Scheduling Algorithm for Computing Power Network

Deep Reinforcement Learning Based Multistage Profit Aware Ta...

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IEEE Conference on Global communications (GLOBECOM)

作者： Xiaoyao Huang Remington R. Liu Bo Lei Guanglun Huang Baoxian Zhang Network Technology Institute Research Institute China Telecom China School of Information and Communication Engineering Beijing University Of Posts and Telecommunications China School of Computer Science and Information Security Guilin University of Electronic Technology China Research Center of Ubiquitous Sensor Networks University of Chinese Academy of Sciences China

Computing power network (CPN), which integrates heterogeneous computing resources and communication network, can tackle the challenges brought by the pervasiveness of mobile and Internet of Things applications. In this paper, we study the optimization of task scheduling in a CPN network by considering the unbalancing between task distribution and resource cost. The design objective is to maximize the system profit while satisfying tasks' delay requirements. We formulate this problem as an integer programming problem. To address this NP-hard problem, we propose a Deep Reinforcement Learning (DRL) based multistage profit-aware task scheduling algorithm which first makes coarse grained task allocation using DRL among regions and then determines an optimized intra-region task assignment by using profit-aware balancing algorithm. Extensive simulations are conducted for performance evaluation and the results show the high performance of the proposed algorithm as compared with baseline algorithms.

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