There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 17796 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 17780 | - |
dc.citation.title | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.volume | 11 | - |
dc.contributor.author | Kim, Mintae | - |
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Hwang, Sangwon | - |
dc.contributor.author | Kim, Minseok | - |
dc.contributor.author | Debbah, Mérouane | - |
dc.contributor.author | Lee, Inkyu | - |
dc.date.accessioned | 2024-06-07T09:35:09Z | - |
dc.date.available | 2024-06-07T09:35:09Z | - |
dc.date.created | 2024-06-06 | - |
dc.date.issued | 2024-05 | - |
dc.description.abstract | This article presents a flexible deep-learning strategy that tackles decentralized optimization tasks in multitier networks where wireless nodes are deployed in a hierarchical structure. Practical multitier networks have arbitrary node populations as well as their backhaul connections. Thus, node operations in the multitier network request versatile inference rules for arbitrary network configurations. To this end, we present a tree-based learning strategy which transforms the multitier network optimization into a collaborative inference process over random trees. For the decentralized structure, each node in a tree is equipped with dedicated deep neural network (DNN) modules. A group of these component DNNs builds a tree deep neural network (TNN) where forward pass calculations define the node interaction policy. The TNN is carefully designed such that it can be universally applied to random trees. The training mechanism is developed to involve a number of random tree instances so that the TNN can be generalized to arbitrary network configurations. As a consequence, the TNN can scale up with a large number of nodes which requires only a single training process. The scalability of the proposed framework is validated for various multitier network optimization problems. Numerical results demonstrate the effectiveness of the TNN over existing approaches. | - |
dc.identifier.bibliographicCitation | IEEE INTERNET OF THINGS JOURNAL, v.11, no.10, pp.17780 - 17796 | - |
dc.identifier.doi | 10.1109/JIOT.2024.3359674 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.scopusid | 2-s2.0-85184320594 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/82901 | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Decentralized Learning Framework for Hierarchical Wireless Networks: A Tree Neural Network Approach | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | mobile edge computing (MEC) | - |
dc.subject.keywordAuthor | power control | - |
dc.subject.keywordAuthor | tree neural network | - |
dc.subject.keywordAuthor | Deep learning (DL) | - |
dc.subject.keywordAuthor | hierarchical wireless networks | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.