A Learning Approach to Cooperative Communication System Design

The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to tr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 5240 - 5244
Hauptverfasser: Lu, Yuxin, Cheng, Peng, Chen, Zhuo, Mow, Wai Ho, Li, Yonghui
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2020
Schlagworte:
Artificial neural networks
Autoen-coder
Communication systems
Cooperative systems
Neural network
Relay network
Robustness
Signal to noise ratio
Testing
Training
ISSN:2379-190X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.
AbstractList The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.
Author Lu, Yuxin
Mow, Wai Ho
Cheng, Peng
Li, Yonghui
Chen, Zhuo
Author_xml – sequence: 1
  givenname: Yuxin
  surname: Lu
  fullname: Lu, Yuxin
  organization: The Hong Kong University of Science and Technology,Department of Electronic and Computer Engineering,Hong Kong SAR,China
– sequence: 2
  givenname: Peng
  surname: Cheng
  fullname: Cheng, Peng
  organization: The University of Sydney, Maze Crescent,School of Electrical and Information Engineering,NSW,Australia,2006
– sequence: 3
  givenname: Zhuo
  surname: Chen
  fullname: Chen, Zhuo
  organization: Data 61, CSIRO, Marsfield,NSW,Australia,2122
– sequence: 4
  givenname: Wai Ho
  surname: Mow
  fullname: Mow, Wai Ho
  organization: The Hong Kong University of Science and Technology,Department of Electronic and Computer Engineering,Hong Kong SAR,China
– sequence: 5
  givenname: Yonghui
  surname: Li
  fullname: Li, Yonghui
  organization: The University of Sydney, Maze Crescent,School of Electrical and Information Engineering,NSW,Australia,2006
BookMark eNotj8FKxDAURaMoODPOF7jJD7S-JmnTbIRSHRUKClVwN7ykr2PEpqWtwvy9BWd1791czlmzi9AHYownECcJmNvnsqjrVwVaZ7EAAbGBVIGRZ2xrdJ6kYCDLZJKes5WQ2kSJgY8rtp6mLwDItcpX7K7gFeEYfDjwYhjGHt0nn3te9v1AI87-l5bedT_Bu2X1gdfHaaaO39PkD-GaXbb4PdH2lBv2vnt4K5-i6uVxoasiL0DOkUShKUVtMpcDioacAEtoFeZON4SmldIa2TqFi4dc0BxazKQVeWsb5eSG3fz_eiLaD6PvcDzuT7ryDwkOTUc
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/ICASSP40776.2020.9054093
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9781509066315
1509066314
EISSN 2379-190X
EndPage 5244
ExternalDocumentID 9054093
Genre orig-research
GroupedDBID 23M
29P
6IE
6IF
6IH
6IK
6IL
6IM
6IN
AAJGR
AAWTH
ABLEC
ACGFS
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IJVOP
IPLJI
M43
OCL
RIE
RIL
RIO
RNS
ID FETCH-LOGICAL-i203t-3a27e5a796c80a2dec20beab4a8c7dea9f33b93fc4a2023874caba63b28fbd4c3
IEDL.DBID RIE
ISICitedReferencesCount 2
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000615970405100&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:46:49 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i203t-3a27e5a796c80a2dec20beab4a8c7dea9f33b93fc4a2023874caba63b28fbd4c3
PageCount 5
ParticipantIDs ieee_primary_9054093
PublicationCentury 2000
PublicationDate 2020-May
PublicationDateYYYYMMDD 2020-05-01
PublicationDate_xml – month: 05
  year: 2020
  text: 2020-May
PublicationDecade 2020
PublicationTitle Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998)
PublicationTitleAbbrev ICASSP
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0008748
Score 2.1189213
Snippet The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach...
SourceID ieee
SourceType Publisher
StartPage 5240
SubjectTerms Artificial neural networks
Autoen-coder
Communication systems
Cooperative systems
Neural network
Relay network
Robustness
Signal to noise ratio
Testing
Training
Title A Learning Approach to Cooperative Communication System Design
URI https://ieeexplore.ieee.org/document/9054093
WOSCitedRecordID wos000615970405100&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ1LSwMxEMeHVjzoxUcrvsnBo9uumzSPi1CqRUFKoQq9lTwm0ku31K2f32R3rRa8eAuBEPgHMjPJzG8AbiTeGc7QJz0dYlXGtEy0Zi7xjnGmpJOpLZH5L2I0ktOpGjfgdlMLg4hl8hl24rD8y3e5Xcensq6K_oWiTWgKwatarc2tKwWT35k6qeo-D_qTyZhFWE0IArO0U6_daqJS2pDhwf92P4T2TzEeGW_MzBE0cHEM-784gi2475Oak_pO-jUknBQ5GeT5Eiu0N9mqBCEVqJw8lPkbbXgbPr4OnpK6MUIyz1JaJFRnAntaKG5lqjOHNksNahOUtsKhVp5So6i3TMfu6EEaq43m1GTSG8csPYGdRb7AUyAcgwnjTCgXQX9olOh5L4LVD26ctBk7g1ZUYras2BezWoTzv6cvYC-KXSUEXsJOsVrjFezaz2L-sbouD-wL_TiXGg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ1LSwMxEMeHWgX14qOKb3Pw6LZrkt0kF6FUS4u1FFqht5LHrPTSLbX187vZXasFL95CIAT-gcxMMvMbgDuJDybmmASRzmJVzrUMtOYuSByPuZJOhjZH5vdEvy_HYzWowP26FgYR8-QzrPth_pfvUrvyT2UN5f0LxbZgO-KchkW11vrelYLL71ydUDW6reZwOOAeV5OFgTSsl6s32qjkVqR98L_9D-HkpxyPDNaG5ggqODuG_V8kwRo8NklJSn0nzRITTpYpaaXpHAu4N9moBSEFqpw85RkcJ_DWfh61OkHZGiGY0pAtA6apwEgLFVsZaurQ0tCgNpnWVjjUKmHMKJZYrn1_9Ewaq42OmaEyMY5bdgrVWTrDMyAxZkYs5kI5j_pDo0SUJCKz-5kjJy3l51DzSkzmBf1iUopw8ff0Lex2Rq-9Sa_bf7mEPS98kR54BdXlYoXXsGM_l9OPxU1-eF8LBpph
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+of+the+...+IEEE+International+Conference+on+Acoustics%2C+Speech+and+Signal+Processing+%281998%29&rft.atitle=A+Learning+Approach+to+Cooperative+Communication+System+Design&rft.au=Lu%2C+Yuxin&rft.au=Cheng%2C+Peng&rft.au=Chen%2C+Zhuo&rft.au=Mow%2C+Wai+Ho&rft.date=2020-05-01&rft.pub=IEEE&rft.eissn=2379-190X&rft.spage=5240&rft.epage=5244&rft_id=info:doi/10.1109%2FICASSP40776.2020.9054093&rft.externalDocID=9054093