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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 7293 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 7279 | - |
dc.citation.title | IEEE Transactions on Communications | - |
dc.citation.volume | 70 | - |
dc.contributor.author | Jang, jeonghyeon | - |
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Kim, Il-Min | - |
dc.contributor.author | Lee, InKyu | - |
dc.date.accessioned | 2023-12-21T13:17:26Z | - |
dc.date.available | 2023-12-21T13:17:26Z | - |
dc.date.created | 2023-09-06 | - |
dc.date.issued | 2022-11 | - |
dc.description.abstract | In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes. | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Communications, v.70, no.11, pp.7279 - 7293 | - |
dc.identifier.doi | 10.1109/TCOMM.2022.3209887 | - |
dc.identifier.issn | 0090-6778 | - |
dc.identifier.scopusid | 2-s2.0-85139519089 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65439 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9904570 | - |
dc.identifier.wosid | 000937284600017 | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalResearchArea | Engineering, Electrical & Electronic;Telecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | MU-MIMO | - |
dc.subject.keywordAuthor | precoder | - |
dc.subject.keywordAuthor | limited feedback | - |
dc.subject.keywordPlus | ROBUST TRANSCEIVER OPTIMIZATION | - |
dc.subject.keywordPlus | BLOCK DIAGONALIZATION | - |
dc.subject.keywordPlus | CHANNEL INVERSION | - |
dc.subject.keywordPlus | MISO SYSTEMS | - |
dc.subject.keywordPlus | PART I | - |
dc.subject.keywordPlus | COMMUNICATION | - |
dc.subject.keywordPlus | MMSE | - |
dc.subject.keywordPlus | RECOVERY | - |
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