Reinforcement learning based NSGA-II for energy-delay trade-off in IAB mmWave Het-Nets

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Abstract

This paper proposes efficient ways for constructing the energy-delay Pareto front in cache-enabled integrated access and backhauling (IAB) heterogeneous wireless network. More specifically, an improved non-dominated sorting genetic algorithm II (NSGA-II) is proposed which is coupled with the operator parameter control ability based on reinforcement learning, to solve the energy-delay trade-off in the multi-objective optimization problem. To estimate the effectiveness of the proposed scheme, key performance indicators that cover the convergence and distribution of the Pareto front solution set are conducted and analyzed. A wide set of numerical investigations show that the proposed algorithm can provide a more evenly distributed result than the state of the art techniques with a 15% gain compared to the nominal case which is the weighted-sum method. Furthermore, and maybe more importantly, the undesirable large gaps between solutions in the Pareto front which are caused by the weighting coefficient choices are avoided. By enabling the operator parameter control ability, the exploration and exploitation process of the proposed algorithm can be balanced, which prevents the frequently faced problems of early convergence and being trapped at a local optimum in the genetic algorithm. The proposed technique can have significant implications in improving the avoidable choices regarding the network operation, and compared with the traditional NSGA-II, the proposed algorithm can provide a near-optimal solution set with 20% more diversity.

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
Pages4206-4211
Number of pages6
ISBN (Electronic)9781538674628
DOIs
Publication statusPublished - 2023

Publication series

NameIEEE International Conference on Communications
Volume2023-May
ISSN (Print)1550-3607

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