Federated Learning-Assisted Predictive Beamforming for Extremely Large-Scale Antenna Array Systems With Rate-Splitting Multiple Access

Achieving perfect Channel State Information at the Transmitter (CSIT) is often infeasible in Extremely Large-scale Antenna Array (ELAA) systems due to user mobility and feedback/processing delay. This results in severe multi-user interference. Therefore, how to effectively and efficiently manage int...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE journal of selected topics in signal processing Ročník 19; číslo 2; s. 461 - 476
Hlavní autoři:	Zhang, Shengyu, Mao, Yijie, Chen, Zihan, Clerckx, Bruno, Quek, Tony Q.S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Antenna arrays Antennas Array signal processing Beamforming Computational modeling Design optimization Effectiveness Extremely large-scale antenna array (ELAA) Federated learning federated learning (FL) Feedback control systems Information systems Interference Multiple access Optimization Parallel processing patch-mixing Precoding Predictive models Protocols rate-splitting multiple access (RSMA) Segments Simulation Spatial resolution Splitting Training Transmitting antennas
ISSN:	1932-4553, 1941-0484
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Achieving perfect Channel State Information at the Transmitter (CSIT) is often infeasible in Extremely Large-scale Antenna Array (ELAA) systems due to user mobility and feedback/processing delay. This results in severe multi-user interference. Therefore, how to effectively and efficiently manage interference with partial/historical CSIT is one of the most important challenges for implementing ELAA. In this paper, we propose a Federated Learning (FL)-assisted predictive beamforming framework for ELAA systems to address this challenge. Specifically, we introduce Rate-Splitting Multiple Access (RSMA) to relax the sensitivity to imperfect CSIT while still benefiting from the spatial resolution. Moreover, a predictive beamforming protocol is designed to optimize the precoder design under the imperfections in the channel estimate quality originating from user mobility and latency. To calculate the beamformers, we first propose a lightweight patch-mixing approach to split the historical CSIT data samples into smaller manageable segments. Then, we propose an FL-based training method that enables parallel processing of these CSI segments, thereby accelerating the training process. Simulation results show the effectiveness and efficacy of the proposed FL-assisted predictive beamforming framework, which paves the way for real-world implementation of ELAA.
AbstractList	Achieving perfect Channel State Information at the Transmitter (CSIT) is often infeasible in Extremely Large-scale Antenna Array (ELAA) systems due to user mobility and feedback/processing delay. This results in severe multi-user interference. Therefore, how to effectively and efficiently manage interference with partial/historical CSIT is one of the most important challenges for implementing ELAA. In this paper, we propose a Federated Learning (FL)-assisted predictive beamforming framework for ELAA systems to address this challenge. Specifically, we introduce Rate-Splitting Multiple Access (RSMA) to relax the sensitivity to imperfect CSIT while still benefiting from the spatial resolution. Moreover, a predictive beamforming protocol is designed to optimize the precoder design under the imperfections in the channel estimate quality originating from user mobility and latency. To calculate the beamformers, we first propose a lightweight patch-mixing approach to split the historical CSIT data samples into smaller manageable segments. Then, we propose an FL-based training method that enables parallel processing of these CSI segments, thereby accelerating the training process. Simulation results show the effectiveness and efficacy of the proposed FL-assisted predictive beamforming framework, which paves the way for real-world implementation of ELAA.
Author	Chen, Zihan Zhang, Shengyu Mao, Yijie Clerckx, Bruno Quek, Tony Q.S.
Author_xml	– sequence: 1 givenname: Shengyu orcidid: 0000-0002-6727-8336 surname: Zhang fullname: Zhang, Shengyu email: shengyu_zhang@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 2 givenname: Yijie orcidid: 0000-0001-5077-2998 surname: Mao fullname: Mao, Yijie email: maoyj@shanghaitech.edu.cn organization: School of Information Science and Technology, ShanghaiTech University, Shanghai, China – sequence: 3 givenname: Zihan surname: Chen fullname: Chen, Zihan email: zihan_chen@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 4 givenname: Bruno orcidid: 0000-0001-5949-6459 surname: Clerckx fullname: Clerckx, Bruno email: bruno.clerckx@gmail.com organization: Department of Electrical and Electronic Engineering, Imperial College London, London, U.K – sequence: 5 givenname: Tony Q.S. orcidid: 0000-0002-4037-3149 surname: Quek fullname: Quek, Tony Q.S. email: tonyquek@sutd.edu.sg organization: Singapore University of Technology and Design, Singapore
BookMark	eNp9kM1u2zAQhInAAfL7AkEOBHKWyx_REo9ukKQtXCSIEuQorKlVSkOiHJIO4hfoc5eqfSh6yGkXu_PNAHNCJm5wSMgFZ1POmf7yo3qqHqaCCTWVSgqWswNyzHXOM5aX-WTcpchypeQROQlhxZgqZjw_Jr9vsUEPERu6QPDOutdsHoIN4-XBY2NNtO9IvyL07eD79Kdp0puP6LHHbksX4F8xqwx0SOcuonNA597Dllbb5NIH-mLjL_qYMrJq3dkYR4-fmy7a9YgYgyGckcMWuoDn-3lKnm9vnq6_ZYv7u-_X80VmhJ7FTORatxJRQtFKaFjBCzNrikJBmz58VkiFZWt4aXSxbEoGS80akLwFAVqVjTwlVzvftR_eNhhivRo23qXIWnKtFc-5FkkldirjhxA8tvXa2x78tuasHvuu__Zdj33X-74TVP4HGRsh2sFFD7b7HL3coRYR_8kqlUg6-Qdi8JJl
CODEN	IJSTGY
CitedBy_id	crossref_primary_10_1007_s11432_024_4423_4
Cites_doi	10.1109/TSP.2024.3350329 10.1109/TWC.2020.3002891 10.1109/JSAC.2020.3000803 10.1109/JSAC.2022.3180803 10.1186/s13638-018-1104-7 10.48550/arXiv.1406.3269 10.1109/OJVT.2020.3032844 10.1109/MAP.2017.2706648 10.1109/TVT.2022.3191085 10.1109/TCOMM.2020.3014153 10.1109/TVT.2024.3454481 10.1109/COMST.2022.3191937 10.1109/TVT.2022.3193443 10.1109/LCOMM.2022.3172437 10.1109/TCOMM.2021.3138437 10.1109/TNNLS.2021.3131614 10.1109/tmc.2024.3462960 10.1109/TWC.2023.3323770 10.1109/JPROC.2024.3409428 10.1109/TWC.2021.3133433 10.1109/ACCESS.2023.3301468 10.1109/LWC.2023.3329065 10.1109/TWC.2024.3395670 10.1109/TWC.2021.3126384 10.1109/JSAC.2023.3280984 10.1109/98.656151 10.1109/ISWCS56560.2022.9940330 10.1109/OJVT.2020.3031656 10.1109/MCOM.2016.7470942 10.1109/TWC.2022.3218897 10.1109/TWC.2024.3489219 10.1109/LWC.2023.3339653 10.1109/LWC.2023.3329036 10.1109/TVT.2023.3321657 10.1109/LWC.2020.3009951 10.1109/LCOMM.2022.3160511 10.1109/TSP.2022.3214376 10.1109/OJCOMS.2023.3240163 10.1109/JIOT.2022.3152382 10.1109/TCOMM.2016.2603991 10.1109/LCOMM.2021.3129317 10.1109/TWC.2021.3095174 10.1109/LCOMM.2023.3347720 10.1109/TWC.2024.3486673 10.1109/MWC.008.2200157 10.1109/LWC.2020.3045150 10.1002/9781118631980 10.1109/TCOMM.2021.3098695 10.1109/TWC.2022.3217145 10.1109/TWC.2016.2543212 10.1109/OJCOMS.2021.3084799 10.1109/ICCAIS56082.2022.9990383 10.1109/TCOMM.2021.3067642 10.1109/lwc.2025.3543567 10.1109/TWC.2023.3330373 10.1109/TWC.2020.3046766 10.1109/LCOMM.2018.2855935 10.1109/LWC.2024.3358494 10.1109/TSP.2019.2923164 10.1109/TVT.2022.3180786 10.1109/LWC.2023.3333531 10.1109/LCOMM.2022.3170735 10.1109/TCOMM.2021.3085343 10.1109/LCOMM.2017.2724553 10.1109/LWC.2024.3381154 10.1109/TVT.2022.3168371 10.1109/JSAC.2023.3240716 10.1109/TMC.2024.3507286 10.1109/ISWCS49558.2021.9562192
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD H8D L7M
DOI	10.1109/JSTSP.2025.3532040
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Aerospace Database Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1941-0484
EndPage	476
ExternalDocumentID	10_1109_JSTSP_2025_3532040 10852202
Genre	orig-research
GrantInformation_xml	– fundername: Infocomm Media Development Authority under its Future Communications Research & Development Programme – fundername: Shanghai Sailing Program grantid: 22YF1428400 – fundername: National Research Foundation Singapore; National Research Foundation, Singapore funderid: 10.13039/501100001381 – fundername: National Natural Science Foundation of China; National Nature Science Foundation of China grantid: 62201347 funderid: 10.13039/501100001809 – fundername: UK Research and Innovation; UKRI grantid: EP/X040569/1; EP/Y037197/1; EP/X04047X/1; EP/Y037243/1 funderid: 10.13039/100014013
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL RIA RIE RNS AAYXX CITATION 7SP 8FD H8D L7M
ID	FETCH-LOGICAL-c296t-2499f3ee3a7f3ad0717c6d775af49916735e8fc18c97bd80ab90da31fa2a958d3
IEDL.DBID	RIE
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001480531900010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1932-4553
IngestDate	Tue Jul 22 15:12:45 EDT 2025 Sat Nov 29 07:58:55 EST 2025 Tue Nov 18 22:45:34 EST 2025 Wed Aug 27 01:53:39 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c296t-2499f3ee3a7f3ad0717c6d775af49916735e8fc18c97bd80ab90da31fa2a958d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-6727-8336 0000-0001-5949-6459 0000-0001-5077-2998 0000-0002-4037-3149
PQID	3199514192
PQPubID	75721
PageCount	16
ParticipantIDs	crossref_primary_10_1109_JSTSP_2025_3532040 ieee_primary_10852202 crossref_citationtrail_10_1109_JSTSP_2025_3532040 proquest_journals_3199514192
PublicationCentury	2000
PublicationDate	2025-03-01
PublicationDateYYYYMMDD	2025-03-01
PublicationDate_xml	– month: 03 year: 2025 text: 2025-03-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE journal of selected topics in signal processing
PublicationTitleAbbrev	JSTSP
PublicationYear	2025
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref68 ref23 ref67 ref26 ref25 ref69 ref20 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 ref60 Rubinstein (ref66) 2016 ref62 ref61
References_xml	– ident: ref14 doi: 10.1109/TSP.2024.3350329 – ident: ref24 doi: 10.1109/TWC.2020.3002891 – ident: ref2 doi: 10.1109/JSAC.2020.3000803 – ident: ref51 doi: 10.1109/JSAC.2022.3180803 – ident: ref11 doi: 10.1186/s13638-018-1104-7 – ident: ref63 doi: 10.48550/arXiv.1406.3269 – ident: ref25 doi: 10.1109/OJVT.2020.3032844 – ident: ref54 doi: 10.1109/MAP.2017.2706648 – ident: ref19 doi: 10.1109/TVT.2022.3191085 – ident: ref23 doi: 10.1109/TCOMM.2020.3014153 – ident: ref64 doi: 10.1109/TVT.2024.3454481 – ident: ref17 doi: 10.1109/COMST.2022.3191937 – ident: ref58 doi: 10.1109/TVT.2022.3193443 – ident: ref10 doi: 10.1109/LCOMM.2022.3172437 – ident: ref39 doi: 10.1109/TCOMM.2021.3138437 – ident: ref68 doi: 10.1109/TNNLS.2021.3131614 – ident: ref65 doi: 10.1109/tmc.2024.3462960 – ident: ref16 doi: 10.1109/TWC.2023.3323770 – ident: ref36 doi: 10.1109/JPROC.2024.3409428 – ident: ref20 doi: 10.1109/TWC.2021.3133433 – ident: ref62 doi: 10.1109/ACCESS.2023.3301468 – ident: ref6 doi: 10.1109/LWC.2023.3329065 – ident: ref32 doi: 10.1109/TWC.2024.3395670 – ident: ref7 doi: 10.1109/TWC.2021.3126384 – ident: ref67 doi: 10.1109/JSAC.2023.3280984 – ident: ref44 doi: 10.1109/98.656151 – ident: ref31 doi: 10.1109/ISWCS56560.2022.9940330 – ident: ref26 doi: 10.1109/OJVT.2020.3031656 – ident: ref1 doi: 10.1109/MCOM.2016.7470942 – ident: ref5 doi: 10.1109/TWC.2022.3218897 – ident: ref34 doi: 10.1109/TWC.2024.3489219 – ident: ref12 doi: 10.1109/LWC.2023.3339653 – ident: ref41 doi: 10.1109/LWC.2023.3329036 – ident: ref61 doi: 10.1109/TVT.2023.3321657 – ident: ref49 doi: 10.1109/LWC.2020.3009951 – ident: ref4 doi: 10.1109/LCOMM.2022.3160511 – ident: ref18 doi: 10.1109/TSP.2022.3214376 – ident: ref28 doi: 10.1109/OJCOMS.2023.3240163 – ident: ref30 doi: 10.1109/JIOT.2022.3152382 – ident: ref35 doi: 10.1109/TCOMM.2016.2603991 – ident: ref8 doi: 10.1109/LCOMM.2021.3129317 – ident: ref55 doi: 10.1109/TWC.2021.3095174 – ident: ref13 doi: 10.1109/LCOMM.2023.3347720 – ident: ref33 doi: 10.1109/TWC.2024.3486673 – ident: ref50 doi: 10.1109/MWC.008.2200157 – ident: ref52 doi: 10.1109/LWC.2020.3045150 – volume-title: Simulation and the Monte Carlo Method, 3rd ed year: 2016 ident: ref66 doi: 10.1002/9781118631980 – ident: ref3 doi: 10.1109/TCOMM.2021.3098695 – ident: ref60 doi: 10.1109/TWC.2022.3217145 – ident: ref38 doi: 10.1109/TWC.2016.2543212 – ident: ref27 doi: 10.1109/OJCOMS.2021.3084799 – ident: ref59 doi: 10.1109/ICCAIS56082.2022.9990383 – ident: ref22 doi: 10.1109/TCOMM.2021.3067642 – ident: ref53 doi: 10.1109/lwc.2025.3543567 – ident: ref40 doi: 10.1109/TWC.2023.3330373 – ident: ref45 doi: 10.1109/TWC.2020.3046766 – ident: ref15 doi: 10.1109/LCOMM.2018.2855935 – ident: ref48 doi: 10.1109/LWC.2024.3358494 – ident: ref46 doi: 10.1109/TSP.2019.2923164 – ident: ref57 doi: 10.1109/TVT.2022.3180786 – ident: ref47 doi: 10.1109/LWC.2023.3333531 – ident: ref9 doi: 10.1109/LCOMM.2022.3170735 – ident: ref21 doi: 10.1109/TCOMM.2021.3085343 – ident: ref37 doi: 10.1109/LCOMM.2017.2724553 – ident: ref43 doi: 10.1109/LWC.2024.3381154 – ident: ref56 doi: 10.1109/TVT.2022.3168371 – ident: ref29 doi: 10.1109/JSAC.2023.3240716 – ident: ref69 doi: 10.1109/TMC.2024.3507286 – ident: ref42 doi: 10.1109/ISWCS49558.2021.9562192
SSID	ssj0057614
Score	2.4417853
Snippet	Achieving perfect Channel State Information at the Transmitter (CSIT) is often infeasible in Extremely Large-scale Antenna Array (ELAA) systems due to user...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	461
SubjectTerms	Antenna arrays Antennas Array signal processing Beamforming Computational modeling Design optimization Effectiveness Extremely large-scale antenna array (ELAA) Federated learning federated learning (FL) Feedback control systems Information systems Interference Multiple access Optimization Parallel processing patch-mixing Precoding Predictive models Protocols rate-splitting multiple access (RSMA) Segments Simulation Spatial resolution Splitting Training Transmitting antennas
Title	Federated Learning-Assisted Predictive Beamforming for Extremely Large-Scale Antenna Array Systems With Rate-Splitting Multiple Access
URI	https://ieeexplore.ieee.org/document/10852202 https://www.proquest.com/docview/3199514192
Volume	19
WOSCitedRecordID	wos001480531900010&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
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0484 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0057614 issn: 1932-4553 databaseCode: RIE dateStart: 20070101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEA66eNCDb3F9kYM3ibZN2zTHVVw8qCyuoreS5qELuspaxf0D_m5n0qwsiIKnFpqkhS9Nvsl8M0PIflSZKHc6Y0qaFAwULVjFs4IBGdCJjpNcG-eLTYjLy-LuTvZCsLqPhbHWevGZPcRb78s3z_oNj8qOUCmfJJg6claIvAnWmiy7wJvj4EJOWJplfBIhE8kjmOP9HtiCSXbIsRACnnRM7UK-rMqPtdhvMN2lf37aMlkMTJJ2GuhXyIwdrpKFqfyCa-Szi6kigE0aGvKo3jPAA5E1tDdCHw2udvTYqickr_CcwpWeftR4bPg4pucoFGd9ANLSDqrdhwreOFJjGlKd09tB_UCv4B2sD3zWq6jpRVAp0o4vx7hObrqn1ydnLBReYDqRec3AJJOOW8uVcFwZNPl0boTIlEuRTwqe2cLpuNBSVKaIVCUjo3jsVKJkVhi-QVrD56HdJBT4mRGycpoXLjVRoWBsnlapVikH5lS1STwBotQhKzkWx3gsvXUSydKDVyJ4ZQCvTQ6--7w0OTn-bL2OcE21bJBqk50J4GX4b19LjhHrMXrGt37ptk3mcfRGhrZDWvXoze6SOf1eD15He35KfgGntN-e
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwELYqqNRygD6oWB6tD9yQIfFjEx8XxArUZbXqUpVb5PhBkWBBS0DwB_jdzDhetBIqUk-JFDuO9Dn2N56ZbwjZzmqXdYNVzGgnwUCxBauFKhmQActtzrvWhVhsohgOy7MzPUrJ6jEXxnsfg8_8Lt5GX767tnd4VLaHkfKco3TkopKSZ2261mzhBeacJycyZ1IpMcuRyfQezPLxCKxBrnYFlkLAs465fSgWVnm1Gsctpr_ynx_3iSwnLkl7LfifyTs_-UKW5hQGv5KnPopFAJ90NCmpnjNABLF1dDRFLw2ud3Tfmyukr_CcwpUePjR4cHj5SAcYKs7GAKWnPYx3nxgYcWoeaRI7p38umr_0F4zBxsBoYxw1PUlxirQXCzKukt_9w9ODI5ZKLzDLdbdhYJTpILwXpgjCODT6bNcVhTJBIqMshPJlsHlpdVG7MjO1zpwReTDcaFU68Y0sTK4nfo1QYGiu0HWwogzSZaWBdwtZS2ukAO5Ud0g-A6KySZccy2NcVtE-yXQVwasQvCqB1yE7L31uWlWON1uvIlxzLVukOmRzBniV_tzbSmDOeo6-8fV_dPtBPhydngyqwfHw5wb5iCO1QWmbZKGZ3vkt8t7eNxe30-9xej4Dbgni5Q
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%3Ajournal&rft.genre=article&rft.atitle=Federated+Learning-Assisted+Predictive+Beamforming+for+Extremely+Large-Scale+Antenna+Array+Systems+With+Rate-Splitting+Multiple+Access&rft.jtitle=IEEE+journal+of+selected+topics+in+signal+processing&rft.au=Zhang%2C+Shengyu&rft.au=Mao%2C+Yijie&rft.au=Chen%2C+Zihan&rft.au=Clerckx%2C+Bruno&rft.date=2025-03-01&rft.pub=IEEE&rft.issn=1932-4553&rft.volume=19&rft.issue=2&rft.spage=461&rft.epage=476&rft_id=info:doi/10.1109%2FJSTSP.2025.3532040&rft.externalDocID=10852202
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-4553&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-4553&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-4553&client=summon