Learning Multi-Objective Network Optimizations

Hoon Lee; Sang Hyun Lee; Tony Q.S. Quek

doi:10.1109/ICCWorkshops53468.2022.9814461

Learning Multi-Objective Network Optimizations

Hoon Lee, Sang Hyun Lee, Tony Q.S. Quek

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper studies a deep learning approach for multi-objective network optimizations. Heterogeneous performance measures are maximized simultaneously to identify complete Pareto-optimal tradeoffs. To this end, a multi-objective optimization (MOO) problem is first reformulated as a collection of constrained single objective optimization (SOO) problems, each associated with a Pareto-optimal point. A novel MOO learning mechanism is developed to address multiple instances of such SOO problems concurrently. A constrained optimization technique is parameterized with neural networks to find an individual solution of the Pareto boundary points. The developed scheme proves efficient in characterizing the optimal tradeoffs of conflicting performance metrics in interfering networks.

Original language	English
Title of host publication	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	91-96
Number of pages	6
ISBN (Electronic)	9781665426718
DOIs	https://doi.org/10.1109/ICCWorkshops53468.2022.9814461
Publication status	Published - 2022
Event	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 - Seoul, Korea, Republic of Duration: 2022 May 16 → 2022 May 20

Publication series

Name	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

Conference

Conference	2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Country/Territory	Korea, Republic of
City	Seoul
Period	22/5/16 → 22/5/20

Bibliographical note

Funding Information:
This work is supported in part by the NRF grant funded by the Korea government Ministry of Science and ICT (MSIT) under Grant 2021R1I1A3054575 and Grant 2019R1A2C1084855, in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the MSIT (Intelligent 6G Wireless Access System) under Grant 2021-0-00467, and in part by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm Media Development Authority.

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing
Control and Optimization

Access to Document

10.1109/ICCWorkshops53468.2022.9814461

Cite this

Lee, H., Lee, S. H., & Quek, T. Q. S. (2022). Learning Multi-Objective Network Optimizations. In 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 (pp. 91-96). (2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCWorkshops53468.2022.9814461

Learning Multi-Objective Network Optimizations. / Lee, Hoon; Lee, Sang Hyun; Quek, Tony Q.S.
2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 91-96 (2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, H, Lee, SH & Quek, TQS 2022, Learning Multi-Objective Network Optimizations. in 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022. 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022, Institute of Electrical and Electronics Engineers Inc., pp. 91-96, 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022, Seoul, Korea, Republic of, 22/5/16. https://doi.org/10.1109/ICCWorkshops53468.2022.9814461

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note = "Funding Information: This work is supported in part by the NRF grant funded by the Korea government Ministry of Science and ICT (MSIT) under Grant 2021R1I1A3054575 and Grant 2019R1A2C1084855, in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the MSIT (Intelligent 6G Wireless Access System) under Grant 2021-0-00467, and in part by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and Infocomm Media Development Authority. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 ; Conference date: 16-05-2022 Through 20-05-2022",

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AB - This paper studies a deep learning approach for multi-objective network optimizations. Heterogeneous performance measures are maximized simultaneously to identify complete Pareto-optimal tradeoffs. To this end, a multi-objective optimization (MOO) problem is first reformulated as a collection of constrained single objective optimization (SOO) problems, each associated with a Pareto-optimal point. A novel MOO learning mechanism is developed to address multiple instances of such SOO problems concurrently. A constrained optimization technique is parameterized with neural networks to find an individual solution of the Pareto boundary points. The developed scheme proves efficient in characterizing the optimal tradeoffs of conflicting performance metrics in interfering networks.

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Learning Multi-Objective Network Optimizations

Abstract

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Bibliographical note

ASJC Scopus subject areas

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