research-article

Opportunities for AI/ML in Telecommunications Networks

Authors:

Alberto Castro,

Matías Richart,

Javier Baliosian,

Eduardo GrampínAuthors Info & Claims

LANC '18: Proceedings of the 10th Latin America Networking Conference

Pages 89 - 95

https://doi.org/10.1145/3277103.3277131

Published: 03 October 2018 Publication History

Abstract

While it is true that we are in the middle of one of the Artificial Intelligence hypes, it is also true that the combination of unprecedented computation-power and data availability with new variations of well seasoned Machine Learning algorithms is dramatically changing the optimization strategies for large ICT industries. Especially, the telecommunications industry has always had to deal with complex systems, stochastic contexts, combinatorial problems, and hard to predict users; Machine Learning-aided optimization was just waiting there to be used by telecoms. In this paper, we introduce some basic Machine Learning concepts, and discuss how it can be used in the context of telecommunications networks, particularly in optical and wireless networks.

References

[1]

O. G. Aliu, A. Imran, M. A. Imran, and B. Evans. 2013. A Survey of Self Organisation in Future Cellular Networks. IEEE Communications Surveys Tutorials 15, 1 (First 2013), 336--361.

[2]

J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. CK. Soong, and J. C. Zhang. 2014. What will 5G be? Selected Areas in Communications, IEEE Journal on 32, 6 (2014), 1065--1082.

[3]

A. Castro, L. Velasco, Ll. Gifre, C. Chen, J. Yin, Z. Zhu, R. Proietti, and S.J. B. Yoo. 2016. Brokered orchestration for end-to-end service provisioning across heterogeneous multi-operator (multi-AS) optical networks. Journal of Lightwave Technology 34, 23 (2016), 5391--5400.

[4]

O. Chapelle, B. Schlkopf, and A. Zien. 2010. Semi-Supervised Learning. MIT press Cambridge.

Digital Library

[5]

Cisco. 2017. Cisco Visual Networking Index: Forecast and Methodology, 2016--2021. White Paper. Cisco Systems.

[6]

R. O. Duda, P. E. Hart, and D. G. Stork. 2012. Pattern classification. John Wiley & Sons.

[7]

Ericsson. 2015. 5G Systems (White Paper). Technical Report Uen 284 23--3244. Ericsson.

[8]

Finisar. 2017. Flexgrid high resolution optical channel monitor (OCM). http://www.finisar.com

[9]

F. Hu, Q. Hao, and K. Bao. 2014. A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation. IEEE Communications Surveys Tutorials 16, 4 (Fourthquarter 2014), 2181--2206.

[10]

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas. 2007. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering 160 (2007), 3--24.

Digital Library

[11]

U. C. Kozat, G. Liang, K. Kokten, and J. Tapolcai. 2016. On Optimal Topology Verification and Failure Localization for Software Defined Networks. IEEE/ACM Transactions on Networking 24, 5 (October 2016), 2899--2912.

Digital Library

[12]

Z. Q. J. Lu. 2010. The elements of statistical learning:data mining, inference, and prediction. Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, 3 (2010), 693--694.

[13]

I. Malanchini, S. Valentin, and O. Aydin. 2016. Wireless resource sharing for multiple operators:Generalization, fairness, and the value of prediction. Computer Networks 100 (2016), 110--123.

Digital Library

[14]

S. Marsland. 2011. Machine learning: an algorithmic perspective. Chapman and Hall/CRC.

Digital Library

[15]

R. Mijumbi, J. Serrat, J. L. Gorricho, N. Bouten, F. De Turck, and R. Boutaba. 2016. Network Function Virtualization: State-of-the-Art and Research Challenges. IEEE Communications Surveys Tutorials 18, 1 (Firstquarter 2016), 236--262.

[16]

N. Ogino and H. Yokota. 2014. Heuristic Computation Method for All-Optical Monitoring Trails Terminated at Specified Nodes. Journal of Lightwave Technology 32, 3 (Feb 2014), 467--482.

[17]

P. Poggiolini. 2012. The GN model of non-linear propagation in uncompensated coherent optical systems. Journal of Lightwave Technology 30, 24 (2012), 3857--3879.

[18]

R. Proietti, X. Chen, K. Zhang, G. Liu, M. Shamsabardeh, A. Castro, L. Velasco, Z. Zhu, and S. J. B. Yoo. 2019. Experimental Demonstration of Machine-Learning-Aided QoT Estimation in Multi-Domain Elastic Optical Networks with Alien Wavelengths. J. Opt. Commun. Netw. 11, 1 (Jan 2019), A1-A10.

[19]

M. Richart, J. Baliosian, J. Serrat, and J. Gorricho. 2016. Resource Slicing in Virtual Wireless Networks: A Survey. IEEE Transactions on Network and Service Management 13, 3 (Sept 2016), 462--476.

Digital Library

[20]

M. Richart, J. Baliosian, J. Serrati, J. Gorricho, R. Agüero, and N. Agoulmine. 2017. Resource allocation for network slicing in WiFi access points. In 2017 13th International Conference on Network and Service Management (CNSM). 1--4.

[21]

S. J. Russell and P. Norvig. 2016. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.

[22]

R. S. Sutton and A. G. Barto. 1998. Introduction to reinforcement learning. Vol. 135. MIT press Cambridge.

Digital Library

[23]

A. P. Vela, B. Shariati, M. Ruiz, F. Cugini, A. Castro, H. Lu, R. Proietti, J. Comellas, P. Castoldi, S. J. B. Yoo, and L. Velasco. 2018. Soft failure localization during commissioning testing and lightpath operation. IEEE/OSA Journal of Optical Communications and Networking 10, 1 (Jan 2018), A27--A36.

[24]

L. Velasco, A. Castro, A. Asensio, M. Ruiz, G. Liu, C. Qin, R. Proietti, and S. J. B. Yoo. 2017. Meeting the requirements to deploy cloud RAN over optical networks. IEEE/OSA Journal of Optical Communications and Networking 9, 3 (March 2017), B22-B32.

[25]

L. Velasco, A. Castro, M. Ruiz, and G. Junyent. 2014. Solving Routing and Spectrum Allocation Related Optimization Problems: From Off-Line to In-Operation Flexgrid Network Planning. Journal of Lightwave Technology 32, 16 (Aug 2014), 2780--2795.

[26]

B. Wu, P. Ho, and K. L. Yeung. 2009. Monitoring Trail: On Fast Link Failure Localization in All-Optical WDM Mesh Networks. Journal of Lightwave Technology 27, 18 (Sept 2009), 4175--4185.

Cited By

Pidpalyi O(2024)Future prospects: AI and machine learning in cloud-based SIP trunkingВісник Черкаського державного технологічного університету10.62660/bcstu/1.2024.2429:1(24-35)Online publication date: 18-Mar-2024
https://doi.org/10.62660/bcstu/1.2024.24
Singh NPranay GRavi V(2024)Network Slicing and Traffic Classification in 5G Networks with Explainable Machine LearningData Management, Analytics and Innovation10.1007/978-981-97-3242-5_42(641-658)Online publication date: 23-Jul-2024
https://doi.org/10.1007/978-981-97-3242-5_42
Chen HLi LChen Y(2021)Sustainable growth research – A study on the telecom operators in ChinaJournal of Management Analytics10.1080/23270012.2021.1980445(1-15)Online publication date: 28-Sep-2021
https://doi.org/10.1080/23270012.2021.1980445

Index Terms

Opportunities for AI/ML in Telecommunications Networks
1. Computing methodologies
  1. Artificial intelligence
2. Networks
  1. Network types
    1. Cyber-physical networks
    2. Wireless access networks

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Published In

cover image ACM Other conferences

LANC '18: Proceedings of the 10th Latin America Networking Conference

October 2018

130 pages

ISBN:9781450359221

DOI:10.1145/3277103

Conference Chairs:
Leandro Villas
University of Campinas, Brazil
,
Julião Braga
Mackenzie Presbyterian University, Brazil
,
General Chair:
Denis Rosário
Federal University of Pará, Brazil
,
Publications Chair:
Eric Gamess
Jacksonville State University, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication
CLEI: Centro Latinoamericano de Estudios en Informática

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 October 2018

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LANC '18

LANC '18: Latin America Networking Conference

October 3 - 4, 2018

São Paulo, Brazil

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Cited By

Pidpalyi O(2024)Future prospects: AI and machine learning in cloud-based SIP trunkingВісник Черкаського державного технологічного університету10.62660/bcstu/1.2024.2429:1(24-35)Online publication date: 18-Mar-2024
https://doi.org/10.62660/bcstu/1.2024.24
Singh NPranay GRavi V(2024)Network Slicing and Traffic Classification in 5G Networks with Explainable Machine LearningData Management, Analytics and Innovation10.1007/978-981-97-3242-5_42(641-658)Online publication date: 23-Jul-2024
https://doi.org/10.1007/978-981-97-3242-5_42
Chen HLi LChen Y(2021)Sustainable growth research – A study on the telecom operators in ChinaJournal of Management Analytics10.1080/23270012.2021.1980445(1-15)Online publication date: 28-Sep-2021
https://doi.org/10.1080/23270012.2021.1980445

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