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On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey
<span title="">2021</span>
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ACKNOWLEDGMENT The authors would like to give special thanks to the National program for the urgent aid and reception of scientists in exile (PAUSE program) from Collège de France for its funding of this ...
The authors in [141] proposed a deep learning algorithm to optimize 5G base station allocation for platooning vehicles underway. ...
[ suggests an effective RAN slicing scheme based on off-line reinforcement learning and a low-complex heuristic algorithm to improve network performance in terms of resource use, latency, achievable ...
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5G PPP Architecture Working Group - View on 5G Architecture, Version 2.0
<span title="2017-12-01">2017</span>
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technical directions for the architecture design in the 5G era. ...
The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural ...
The authors used reinforcement learning for dynamic resource management in virtual networks to (2) improve the quality of service by reducing packet drop rate and virtual link delay, which showed promising ...
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Distributed Vehicular Computing at the Dawn of 5G: a Survey
[article]
<span title="2022-04-25">2022</span>
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arXiv
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In this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. ...
Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. ...
ACKNOWLEDGMENT This research has been supported in part by project 16214817 from the Research Grants Council of Hong Kong, and the 5GEAR and FIT projects from Academy of Finland. ...
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Deep Learning at the Mobile Edge: Opportunities for 5G Networks
<span title="2020-07-09">2020</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a>
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for 5G networks. ...
Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically ...
Several teams have already used deep Q-learning to address ad-hoc mobile edge computing vehicular networks for 5G [30] . ...
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5G PPP Architecture Working Group - View on 5G Architecture, Version 4.0
<span title="2021-10-29">2021</span>
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Since then, this effort continuously captured the technology trends as developed by the different phases of 5G PPP projects: the first phase (Phase I), that laid the foundation of the network slicing-aware ...
The overall goal of the Architecture Working Group (WG) within the 5G PPP Initiative is to consolidate the main technology enablers and the bleeding-edge design trends in the context of the 5G Architecture ...
Spark 6 is used to train classic supervised and unsupervised models, BigDL 7 is used for Deep Neural Networks, and Ray 8 can be used for Reinforcement Learning models. ...
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AI and ML – Enablers for Beyond 5G Networks
<span title="2020-12-01">2020</span>
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The white paper introduces the main relevant mechanisms in Artificial Intelligence and Machine Learning currently investigated and exploited for 5G and beyond 5G networks. ...
Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g. to improve a property of the system. ...
Table 3 - 3 5 Use cases for non-real-time RAN aspects
Use Case
5GPPP Project
Additional references
RAN slicing in multi-tenant networks
5G-CLARITY
[325], [326]
Radio Resource Provisioning in ...
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5G Norma Network Architecture – Final Report
<span title="2017-10-16">2017</span>
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The key achievement of 5G NORMA WP3 is the design of a conceptually novel, adaptive, and future-proof 5G mobile network architecture allowing to adapt the use of the mobile network (radio access, core, ...
The security analysis in radio and core network domain has fuelled novel security concepts specifically addressing the challenges of multi-tenant and multi-service mobile networks with decomposed network ...
This module, that runs in the ISRB block, adopts a reinforcement learning based technique to perform admission control. ...
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Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges
[article]
<span title="2018-05-20">2018</span>
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arXiv
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The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. ...
that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. ...
With the fifth generation (5G) of mobile broadband systems, which shall integrate new technology components (e.g. massive MIMO, mm-Wave communication, network slicing, vehicular networks, more and broader ...
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D5.1: 5G Security Test Cases
<span title="2021-03-01">2021</span>
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This set of test cases were selected by performing an exhaustive requirements elicitation of 5G security use cases defined in WP2, stemming from the new and enhanced 5G security and trust/liability enablers ...
developed in WP3 and WP4. ...
the network. Reinforcement Learning and rule-based schemes are used in to evaluate the solution space and pick optimal decisions. ...
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Resource Management and Quality of Service Provisioning in 5G Cellular Networks
[article]
<span title="2020-08-21">2020</span>
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arXiv
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In this paper we will review 5G networks characteristics and specifications, then carry out a survey on resource management and QoS provisioning to improve and manage resource utilization in 5G networks ...
On the other hand, guaranteeing quality of service requirements for the wide range of new services is another challenge that must be met in 5G networks. ...
In [104] , slice admission control based on reinforcement learning was proposed. ...
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Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach
[article]
<span title="2020-11-12">2020</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand ...
To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. ...
Index Terms 5G NR, resource slicing, eMBB, URLLC, risk-sensitive, deep reinforcement learning.
I. ...
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Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends
<span title="2021-12-30">2021</span>
<i title="Institution of Engineering and Technology (IET)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hilohscapjgk3kxfrc3gt64bvm" style="color: black;">IET Communications</a>
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The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management ...
One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. ...
In [18] Nassar and Yilmaz discussed the resources allocation in the network using slicing technique by DL and RL. DRL is used for optimal and adaptive vehicular network slicing approaches. ...
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Guest Editorial Special Issue on Intent-Based Networking for 5G-Envisioned Internet of Connected Vehicles
<span title="">2021</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/in6o6x6to5e2dls4y2ff52dy6u" style="color: black;">IEEE transactions on intelligent transportation systems (Print)</a>
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However, as we are moving towards the 5G communication era, the data generated by connected vehicles are expected to grow exponentially; which in turn brings significant challenges to 5G. ...
W ITH the recent advances in wireless communications, the automotive industry is leading to evolution. ...
networks using 5G network slicing to deal with the heterogeneous requirements of different ITS applications in the complex and dynamic environment of vehicular networks. ...
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5G and beyond networks
[chapter]
<span title="">2021</span>
<i title="Elsevier">
Inclusive Radio Communications for 5G and Beyond
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Inclusive Radio Communications for 5G and Beyond. https://doi. ...
Vehicular scenarios for 5G use cases are quite challenging for a cellular network not specifically planned for it. ...
In [DMM + 16], a dynamic data traffic algorithm to enable hybrid vehicular communication is proposed. ...
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Man-made Brain Power for 5G Wireless Systems
<span title="2022-05-10">2022</span>
<i title="Zenodo">
Zenodo
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new insight into future exploration headings for utilizing simulated intelligence in 5G remote interchanges. ...
In this paper, we give an inside and out survey of man-made intelligence for 5G remote correspondence frameworks. ...
localization scheduling in vehicular networks. ...
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