Learning Multi-Objective Network Optimizations

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages91-96
Number of pages6
ISBN (Electronic)9781665426718
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 - Seoul, Korea, Republic of
Duration: 2022 May 162022 May 20

Publication series

Name2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

Conference

Conference2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period22/5/1622/5/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization

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