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5g and Edge: Orchestrating Resource Allocation in a Multidimensional and Distributed Infrastructure

https://doi.org/10.2139/ssrn.4685969
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inferredcites →https://doi.org/10.1016/j.comnet.2019.05.004openalex-extracted
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Deep Reinforcement Learning for intrusion detection in Internet of Things: Best practices, lessons learnt, and open challenges
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CRLM: A cooperative model based on reinforcement learning and metaheuristic algorithms of routing protocols in wireless sensor networks
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Satellite-assisted edge computing management based on deep reinforcement learning in industrial internet of things
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A novel routing optimization strategy based on reinforcement learning in perception layer networks
inferredcites →https://doi.org/10.1017/cbo9781316417744openalex-extracted
5G Mobile and Wireless Communications Technology
inferredcites →https://doi.org/10.1038/nature14236openalex-extracted
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1109/access.2019.2940292openalex-extracted
Edge Computing Assisted Joint Quality Adaptation for Mobile Video Streaming
inferredcites →https://doi.org/10.1109/infocom.2014.6847918openalex-extracted
Toward profit-seeking virtual network embedding algorithm via global resource capacity
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Software-Defined Networking: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/noms.2014.6838258openalex-extracted
Design and evaluation of learning algorithms for dynamic resource management in virtual networks
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RDAM: A Reinforcement Learning Based Dynamic Attribute Matrix Representation for Virtual Network Embedding
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Quality of Experience-Aware Mobile Edge Caching through a Vehicular Cloud
inferredcites →https://doi.org/10.1109/tnet.2011.2159308openalex-extracted
ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping
inferredcites →https://doi.org/10.1109/wetice.2019.00046openalex-extracted
Autonomous Cars, 5G Mobile Networks and Smart Cities: Beyond the Hype
inferredcites →https://doi.org/10.1145/3127540.3127555openalex-extracted
Quality of Experience-Aware Mobile Edge Caching through a Vehicular Cloud
inferredcites →https://doi.org/10.23919/cnsm46954.2019.9012674openalex-extracted
A Reinforcement Learning Based Approach for 5G Network Slicing Across Multiple Domains
inferredcites →https://doi.org/10.25080/tcwv9851openalex-extracted
Exploring Network Structure, Dynamics, and Function using NetworkX
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The NIST definition of cloud computing
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