TY - GEN
T1 - Learning Multi-Objective Network Optimizations
AU - Lee, Hoon
AU - Lee, Sang Hyun
AU - Quek, Tony Q.S.
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85134722211&partnerID=8YFLogxK
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U2 - 10.1109/ICCWorkshops53468.2022.9814461
DO - 10.1109/ICCWorkshops53468.2022.9814461
M3 - Conference contribution
AN - SCOPUS:85134722211
T3 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
SP - 91
EP - 96
BT - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Y2 - 16 May 2022 through 20 May 2022
ER -