Supervised-Learning for Multi-Hop MU-MIMO Communications with One-Bit Transceivers
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Daeun, Kim | - |
dc.contributor.author | Hong, Song nam | - |
dc.contributor.author | Namyoon, Lee | - |
dc.date.accessioned | 2021-08-02T10:28:50Z | - |
dc.date.available | 2021-08-02T10:28:50Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2019-11 | - |
dc.identifier.issn | 0733-8716 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/11687 | - |
dc.description.abstract | This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multi-hop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labeled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Supervised-Learning for Multi-Hop MU-MIMO Communications with One-Bit Transceivers | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Song nam | - |
dc.identifier.doi | 10.1109/JSAC.2019.2933965 | - |
dc.identifier.scopusid | 2-s2.0-85070708997 | - |
dc.identifier.wosid | 000498024700012 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, v.37, no.11, pp.2559 - 2572 | - |
dc.relation.isPartOf | IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS | - |
dc.citation.title | IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS | - |
dc.citation.volume | 37 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 2559 | - |
dc.citation.endPage | 2572 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | MASSIVE MIMO | - |
dc.subject.keywordPlus | CHANNEL ESTIMATION | - |
dc.subject.keywordPlus | RELAY NETWORKS | - |
dc.subject.keywordPlus | CAPACITY | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordAuthor | Multi-user multiple-input multiple-output (MU-MIMO) | - |
dc.subject.keywordAuthor | multihop relay networks | - |
dc.subject.keywordAuthor | machine learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8792182 | - |
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