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Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis
[article]
2020
arXiv
pre-print
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In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis ...
However, the neural network architectures adopted by existing works suffer from poor scalability, generalization, and lack of interpretability. ...
NEURAL NETWORK ARCHITECTURE DESIGN FOR RADIO RESOURCE MANAGEMENT In this section, we endeavor to develop a scalable neural network architecture for radio resource management problems. ...
arXiv:2007.07632v2
fatcat:mayhhep24rhqvpvggylkohnq3i
QoS-Based Resource Management for Ambient Intelligence
[chapter]
2003
Ambient Intelligence: Impact on Embedded Sytem Design
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QoS based resource management enables these tradeoffs in resource-constrained systems. ...
In this paper we present our QoS approach, and we explore an integrated approach that addresses terminal and network resources, and takes power issues into account. ...
His main interests include progressive transmission in still image, video and 3D object coding, as well as scalability and resource monitoring for advanced, scalable video and 3D coding applications. ...
doi:10.1007/0-306-48706-3_9
fatcat:2qc4de3vqvbolca45oxfyjabpq
Resource Management in Cloud Radio Access Network: Conventional and New Approaches
2020
Sensors
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Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. ...
The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. ...
The same concept can be implemented for a C-RAN by training neural networks to obtain optimal resource management. ...
doi:10.3390/s20092708
pmid:32397540
fatcat:hwomwvzo4ngzzj3ci5bxs6atma
ALACA: A platform for dynamic alarm collection and alert notification in network management systems
2017
International Journal of Network Management
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It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using ...
The solution includes a dynamic index for matching active alarms, an algorithm for generating candidate alarm rules, a sliding window-based approach to save system resources, and a graph-based solution ...
The Root Cause Analysis module executes the proposed scalable rule and graph mining algorithm for root cause analysis on alarm events. ...
doi:10.1002/nem.1980
fatcat:ov5fru3rsvec3g7enjpu2z5xgi
Series Editorial: Inauguration Issue of the Series on Machine Learning in Communications and Networks
2021
IEEE Journal on Selected Areas in Communications
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for their support and ...
ACKNOWLEDGMENT We would like to express our great gratitude to all authors for their submissions and over 400 anonymous reviewers for their insightful reviews and suggestions that have helped maintain ...
In the paper, titled "Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis," by Shen et al., graph neural networks (GNNs) are proposed to solve large-scale ...
doi:10.1109/jsac.2020.3036785
fatcat:pdmsdg6zrreqzgqye3qrj5f3na
How Neural Architectures Affect Deep Learning for Communication Networks?
[article]
2022
arXiv
pre-print
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This paper endeavors to theoretically validate the importance and effects of neural architectures when applying deep learning to design communication networks. ...
The early attempts adopt neural architectures inherited from applications such as computer vision, which suffer from poor generalization, scalability, and lack of interpretability. ...
The wireless channel graph convolution network (WCGCN) proposed in [11] is a special case of MPGNNs designed for interference management in wireless networks. ...
arXiv:2111.02215v2
fatcat:k5vajidqojajffxi47dtwssrm4
Resource Management in Fog/Edge Computing: A Survey
[article]
2018
arXiv
pre-print
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This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources ...
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets ...
System Software System software for the fog/edge is a platform designed to operate directly on fog/edge devices and manage the computation, network, and storage resources of the devices. ...
arXiv:1810.00305v1
fatcat:erskczbjtjh5jigiu2dy4jgmdu
Machine Learning Methods for Management UAV Flocks - a Survey
2021
IEEE Access
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This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management. ...
Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. ...
They reviewed research problems pertaining to UAV network performance analysis and optimization, including the physical layer design, trajectory path planning, resource management, multiple access, cooperative ...
doi:10.1109/access.2021.3117451
fatcat:f6xli6srencw3ezqg5fyzwmuie
NOMS 2020 Author Index
2020
NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium
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22, 2020, 11:00 -13:00)
Francesco Tusa, University College of
London
Design and Implementation of an Elastic Monitoring Architecture for Cloud
Network Slices (NOMS 2020 Mini-Conference Sessions, Monday ...
and Implementation of an Elastic Monitoring Architecture for Cloud Network Slices (NOMS 2020 Mini-Conference Sessions, Monday, April 20, 2020, 09:00 -11:00); Elastic Monitoring Architecture for Cloud ...
doi:10.1109/noms47738.2020.9110412
fatcat:g5xipmrxgzc2bctaunnoqdvsdi
Graph Neural Networks for Wireless Communications: From Theory to Practice
[article]
2022
arXiv
pre-print
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For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance ...
For theoretical guarantees, we prove that GNNs achieve near-optimal performance in wireless networks with much fewer training samples than traditional neural architectures. ...
The authors are with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong (E-mail: yshenaw@connect.ust.hk, {eejzhang, eeshsong eekhaled}@ust.hk ...
arXiv:2203.10800v1
fatcat:6o3jz7n3pjh3zhg76p3u3l2ugm
2014 Index IEEE Transactions on Parallel and Distributed Systems Vol. 25
2015
IEEE Transactions on Parallel and Distributed Systems
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., +, TPDS Nov. 2014 3036-3045
Computer network management
In-Network Cache Management and Resource Allocation for Information-
Centric Networks. ...
-H., +, TPDS Sept. 2014 2342-2352
In-Network Cache Management and Resource Allocation for Information-
Centric Networks. ...
on Homogeneous Multiprocessors. 1510 -1521 In-Network Cache Management and Resource Allocation for Information-Centric Networks. ...
doi:10.1109/tpds.2014.2371591
fatcat:qxyljogalrbfficryqjowgv3je
A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control
2021
IEEE Access
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In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. ...
Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. ...
[40] implemented a handover mechanism and resource allocation to enhance the scalability and flexibility in 5G networks under Network Slicing for macrocells and small cells. ...
doi:10.1109/access.2021.3074024
doaj:b63132ee7999451f8ea69f254571f0c8
fatcat:iyld5eythzeb3ojtlgzfjtqy5i
A Survey on Spectrum Management for Unmanned Aerial Vehicles (UAVs)
2021
IEEE Access
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It then introduces deployment scenarios (applications and architectures) as standalone or heterogeneous networks. ...
The operation of unmanned aerial vehicles (UAV) imposes various challenges on radio spectrum management to achieve safe operation, efficient spectrum utilization, and coexistence with legacy wireless networks ...
A distinct attribute from existing surveys is the proposal of a management paradigm that spans the physical, MAC, and network layers, thus providing a hierarchical design analysis for underlying structures ...
doi:10.1109/access.2021.3138048
fatcat:2h5ls7ywo5g3bbz454gqatvkw4
SIEM4GS: Security Information and Event Management for a Virtual Ground Station Testbed
2022
Proceedings of the ... European conference on information warfare and security
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Based on the latest literature on ground stations, a logical architecture and an implementation plan involving only open-source software building blocks for SIEM4GS are proposed. ...
A distinguishing feature of the testbed is the integration of a security information and event management (SIEM) system justifying the name of the testbed, "SIEM4GS". ...
Acknowledgements The authors thank David Culpin (Raytheon Australia), Dr Ronald Mulinde (UniSA), Dr David Ormrod (UniSA) for their initial input; and Brandon Klar (UniSA) for undertaking the implementation ...
doi:10.34190/eccws.21.1.228
fatcat:ihg7vcbokjbehfmooxcfbjkmi4
Computationally Intelligent Techniques for Resource Management in MmWave Small Cell Networks
2018
IEEE wireless communications
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for all network entities. ...
Ultra densification in heterogeneous networks (HetNets) and the advent of millimeter wave (mmWave) technology for fifth generation (5G) networks have led the researchers to redesign the existing resource ...
ACKNOWLEDGMENT We would like to acknowledge the support of the University of Surrey 5GIC members for this work. ...
doi:10.1109/mwc.2018.1700400
fatcat:563lbionifcwnieu7l6yspbnqe
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