Optical and wireless network convergence in 5G systems – an experimental approach

Marques, Paulo; Carmo, Alexandre P. do; Frascolla, Valerio; Silva, Carlos; Sena, Emanuel D. R.; Braga, Raphael J.; Pinheiro, J. F.; Astudillo, Carlos A.; Andrade, Tiago Faustino; Gama, Eduardo S.; Bittencourt, Luiz F.; Villas, Leandro A.; Madeira, Edmundo Roberto Mauro; Fonseca, Nelson L. S. da; Both, Cristiano Bonato; Landó, Gabriel; Schimuneck, Matias Artur Klafke; Wickboldt, Juliano Araújo; Trevisan, Ana P. V.; Martins, Rafael de Jesus; Vassallo, Raquel Frizera; Queiroz, Felippe M. de; Picoreti, Rodolfo; Gomes, Roberta Lima; Dominicini, Cristina K.; Garcia, Victor Soto; Guimarães, Rafael Lima; Villaça, Rodolfo da Silva; Martinello, Magnos; Ribeiro, Moisés R. N.; Macedo, Daniel F.; Silva, Vinicius F.; Guimaraes, Julio C. T.; Colman-Meixner, Carlos; Nejabati, Reza; Simeonidou, Dimitra; Zhang, Yi; Slyne, Frank; Álvarez, Pedro; Collins, Diarmuid; Ruffini, Marco; DaSilva, Luiz A.; Marquez-Barja, Johann M.

doi:10.1109/camad.2018.8514972

Cited by 3 publications

(1 citation statement)

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“…They observed that the 5G operation of the robots was superior in terms of control-loop reliability to the one of Wi-Fi 6 and the potential of 5G for reliably operating autonomous mobile robots. Marques et al [2018] analysed and presented the impact of software-defined networks and cloud computing technologies in systems running real-time applications with low end-to-end latency and high bandwidth requirements. Thus, to achieve URLLC in wireless networks to support robot mobility, they proposed and implemented a splitting of the WiFi architecture functionalities that provides multiconnectivity and redundant communication by offering seamless mobility and failover resilience.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0

Bernardo¹,

Miranda²,

Macedo³

2022

Anais Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2022)

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One of the three core use cases of 5G is ultra-reliable low-latency communications, which encompasses remote operation for Industry 4.0. It is expected that autonomous robots, driven by artificial intelligence agents running on the cloud, will operate without human assistance. This paper investigates how the network performance affects the behavior of AI-based remote operation control that is based on deep reinforcement learning. We investigate two separate aspects: (i) the performance of a pre-trained model on ideal network conditions and (ii) how the varying network conditions impact the agent performance in the environment. The results show the influence of different network quality conditions on agent's performance, as well as the benefit of using agents pre-trained on ideal conditions.

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Section: Related Workmentioning

confidence: 99%

Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0

Bernardo¹,

Miranda²,

Macedo³

2022

Anais Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2022)

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Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy

Slamnik-Kriještorac

Kremo

Ruffini

et al. 2020

IEEE Commun. Surv. Tutorials

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Regardless of the context to which it is applied, sharing resources is well-recognized for its considerable benefits. Since 5G networks will be service-oriented, on-demand, and highly heterogeneous, it is utmost important to approach the design and optimization of the network from an end-to-end perspective. In addition, in order to ensure end-to-end performance, this approach has to entail both wireless and optical domains, altogether with the IoT, edge, and cloud paradigms which are an indispensable part of the 5G network architecture. Shifting from the exclusive ownership of network resources toward sharing enables all participants to cope with stringent service requirements in 5G networks, gaining significant performance improvements and cost savings at the same time. The main objective of this paper is to survey the literature on resource sharing, providing an in-depth and comprehensive perspective of sharing by recognizing the main trends, the techniques which enable sharing, and the challenges that need to be addressed. By providing a taxonomy which brings the relevant features of a comprehensive sharing model into focus, we aim to enable the creation of sharing models for more efficient future communication networks. We also summarize and discuss the relevant issues arising from network sharing, that should be properly tackled in the future.

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An Energy Efficient Resource Provisioning Scheme For Joint all Photonic and Wireless Networks

Molefe

Nleya

2022

2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)

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Optical and wireless network convergence in 5G systems – an experimental approach

Cited by 3 publications

References 1 publication

Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0

Analysis of Network Performance over Deep Reinforcement Learning Control Loops for Industry 4.0

Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy

An Energy Efficient Resource Provisioning Scheme For Joint all Photonic and Wireless Networks

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