Secrecy Throughput Maximization for IRS-aided MIMO Wireless Powered Communication Networks

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secre...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on communications Ročník 70; číslo 11; s. 1
Hlavní autoři:	Shi, Weiping, Wu, Qingqing, Xiao, Fu, Shu, Feng, Wang, Jiangzhou
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Beamforming Communication networks Communications networks Covariance matrix Energy transfer Intelligent reflecting surface Iterative algorithms Iterative methods MIMO MIMO communication Optimization phase shift optimization secrecy throughput Transmission efficiency Wireless communications Wireless networks WPCN
ISSN:	0090-6778, 1558-0857
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.
AbstractList	In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.
Author	Shu, Feng Shi, Weiping Wu, Qingqing Xiao, Fu Wang, Jiangzhou
Author_xml	– sequence: 1 givenname: Weiping surname: Shi fullname: Shi, Weiping organization: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Qingqing orcidid: 0000-0002-0043-3266 surname: Wu fullname: Wu, Qingqing organization: Department of Electronic Engineering, Shanghai Jiao Tong University, China – sequence: 3 givenname: Fu orcidid: 0000-0003-1815-2793 surname: Xiao fullname: Xiao, Fu organization: School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China – sequence: 4 givenname: Feng orcidid: 0000-0003-0073-1965 surname: Shu fullname: Shu, Feng organization: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 5 givenname: Jiangzhou orcidid: 0000-0003-0881-3594 surname: Wang fullname: Wang, Jiangzhou organization: School of Engineering and Digital Arts, University of Kent, Canterbury, U.K
BookMark	eNp9kE1LAzEQhoMoWD_-gF4WPG-dJJtm9yjFj4JrRSuClyVNJhptNzXZpeqvd22LBw-eXhjmmZd59sh27Wsk5IhCn1IoTifDcVn2GTDW54wyybMt0qNC5CnkQm6THkAB6UDKfJfsxfgKABlw3iNP96gD6s9k8hJ8-_yyaJukVB9u7r5U43ydWB-S0d19qpxBk5Sjcpw8uoAzjDG59UsM3XTo5_O2dnpN3GCz9OEtHpAdq2YRDze5Tx4uzifDq_R6fDkanl2nmjHRpNryac6FktYYmBYAbGqMyWxBc8ELlFZzrVjRhTJW2EE-MELlCFRiR3HO98nJ-u4i-PcWY1O9-jbUXWXViZBCDrJMdFtsvaWDjzGgrRbBzVX4rChUPw6rlcPqx2G1cdhB-R9Iu2b1ZROUm_2PHq9Rh4i_XUVBuQDKvwFqnIJf
CODEN	IECMBT
CitedBy_id	crossref_primary_10_1109_TWC_2024_3478252 crossref_primary_10_1016_j_phycom_2025_102602 crossref_primary_10_1109_COMST_2024_3487112 crossref_primary_10_1109_TCOMM_2023_3347768 crossref_primary_10_1007_s11235_025_01309_8 crossref_primary_10_3390_electronics13071402 crossref_primary_10_1109_JIOT_2023_3326440 crossref_primary_10_1109_LWC_2024_3400974 crossref_primary_10_1109_TCOMM_2024_3351758 crossref_primary_10_1109_TWC_2024_3444032 crossref_primary_10_1109_TCOMM_2023_3343384 crossref_primary_10_1109_TWC_2024_3393621 crossref_primary_10_1109_ACCESS_2024_3525036 crossref_primary_10_1109_TGCN_2023_3341443 crossref_primary_10_1109_JSEN_2024_3521452 crossref_primary_10_1371_journal_pone_0327467 crossref_primary_10_1109_TMC_2025_3549445 crossref_primary_10_1109_JIOT_2025_3568827 crossref_primary_10_1109_TVT_2023_3279840 crossref_primary_10_1109_OJCOMS_2025_3526286 crossref_primary_10_1007_s11432_024_4102_3 crossref_primary_10_3390_drones7060403 crossref_primary_10_1109_JIOT_2025_3560827 crossref_primary_10_1002_dac_70233 crossref_primary_10_1007_s11432_024_4275_x crossref_primary_10_1109_JIOT_2025_3584310
Cites_doi	10.1109/TWC.2020.3024860 10.1109/LWC.2020.2969629 10.1109/TWC.2015.2439673 10.1109/JSAC.2020.3000814 10.1109/JIOT.2020.3046929 10.1109/TVT.2016.2515607 10.1109/TCOMM.2020.3042275 10.1109/LCOMM.2020.3039811 10.1109/TSP.2011.2147784 10.1109/MCOM.001.1900107 10.1109/JSAC.2020.3000802 10.1109/TVT.2021.3096603 10.1109/MCOM.001.2001076 10.1109/TVT.2018.2852631 10.1109/TWC.2015.2502590 10.1109/ACCESS.2019.2915659 10.1109/JSAC.2018.2872372 10.1109/ACCESS.2018.2884953 10.1109/TWC.2019.2936025 10.1109/TWC.2022.3151624 10.1109/TWC.2015.2395414 10.1109/TWC.2013.112513.130760 10.1109/JIOT.2020.3045703 10.1109/TCOMM.2019.2958916 10.1109/JSAC.2018.2824231 10.1109/TCOMM.2014.2370035 10.1109/LCOMM.2020.3043475 10.1109/TWC.2019.2922609 10.1109/TSP.2014.2303422 10.1109/TWC.2020.3004330 10.1109/LWC.2021.3064992 10.1017/CBO9780511804441 10.1109/JIOT.2019.2913219 10.1109/TWC.2021.3133491 10.1109/MWC.001.1900534 10.1109/LWC.2019.2961656 10.1109/TWC.2020.2990766 10.1109/TCOMM.2020.3024621 10.1109/JSAC.2020.3007043 10.1109/LSP.2018.2890164 10.1109/TVT.2013.2285244 10.1109/TSP.2021.3073813 10.1109/TCOMM.2017.2664860 10.1109/JSYST.2018.2827077 10.1109/LWC.2021.3072502
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD L7M
DOI	10.1109/TCOMM.2022.3212734
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Technology Research 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	1558-0857
EndPage	1
ExternalDocumentID	10_1109_TCOMM_2022_3212734 9913501
Genre	orig-research
GroupedDBID	-~X .DC 0R~ 29I 3EH 4.4 5GY 5VS 6IK 85S 97E AAJGR AASAJ AAWTH ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK ACKIV ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD HZ~ H~9 IAAWW IBMZZ ICLAB IES IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 ZCA ZCG AAYXX CITATION 7SP 8FD AARMG ABAZT L7M
ID	FETCH-LOGICAL-c225t-cf3b835a7fdd0b9002bddd4f918539e7fc3ca29fc3adf5f686d5a8e017e35a333
IEDL.DBID	RIE
ISICitedReferencesCount	30
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000937284600033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0090-6778
IngestDate	Mon Jun 30 10:10:45 EDT 2025 Sat Nov 29 04:08:24 EST 2025 Tue Nov 18 22:30:57 EST 2025 Tue Nov 25 14:44:26 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	11
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-c225t-cf3b835a7fdd0b9002bddd4f918539e7fc3ca29fc3adf5f686d5a8e017e35a333
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-1815-2793 0000-0002-0043-3266 0000-0003-0881-3594 0000-0003-0073-1965
PQID	2737576445
PQPubID	85472
PageCount	1
ParticipantIDs	crossref_primary_10_1109_TCOMM_2022_3212734 ieee_primary_9913501 proquest_journals_2737576445 crossref_citationtrail_10_1109_TCOMM_2022_3212734
PublicationCentury	2000
PublicationDate	2022-11-01
PublicationDateYYYYMMDD	2022-11-01
PublicationDate_xml	– month: 11 year: 2022 text: 2022-11-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on communications
PublicationTitleAbbrev	TCOMM
PublicationYear	2022
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	ref35 ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref10 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref18 ref46 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 grant (ref45) 2014
References_xml	– ident: ref15 doi: 10.1109/TWC.2020.3024860 – ident: ref38 doi: 10.1109/LWC.2020.2969629 – ident: ref35 doi: 10.1109/TWC.2015.2439673 – ident: ref16 doi: 10.1109/JSAC.2020.3000814 – ident: ref28 doi: 10.1109/JIOT.2020.3046929 – ident: ref7 doi: 10.1109/TVT.2016.2515607 – ident: ref18 doi: 10.1109/TCOMM.2020.3042275 – ident: ref46 doi: 10.1109/LCOMM.2020.3039811 – ident: ref42 doi: 10.1109/TSP.2011.2147784 – ident: ref20 doi: 10.1109/MCOM.001.1900107 – ident: ref27 doi: 10.1109/JSAC.2020.3000802 – ident: ref31 doi: 10.1109/TVT.2021.3096603 – ident: ref13 doi: 10.1109/MCOM.001.2001076 – ident: ref8 doi: 10.1109/TVT.2018.2852631 – ident: ref2 doi: 10.1109/TWC.2015.2502590 – ident: ref19 doi: 10.1109/ACCESS.2019.2915659 – ident: ref44 doi: 10.1109/JSAC.2018.2872372 – ident: ref6 doi: 10.1109/ACCESS.2018.2884953 – ident: ref21 doi: 10.1109/TWC.2019.2936025 – ident: ref23 doi: 10.1109/TWC.2022.3151624 – ident: ref12 doi: 10.1109/TWC.2015.2395414 – year: 2014 ident: ref45 publication-title: CVX MATLAB Software for Disciplined Convex Programming – ident: ref5 doi: 10.1109/TWC.2013.112513.130760 – ident: ref33 doi: 10.1109/JIOT.2020.3045703 – ident: ref14 doi: 10.1109/TCOMM.2019.2958916 – ident: ref9 doi: 10.1109/JSAC.2018.2824231 – ident: ref3 doi: 10.1109/TCOMM.2014.2370035 – ident: ref29 doi: 10.1109/LCOMM.2020.3043475 – ident: ref22 doi: 10.1109/TWC.2019.2922609 – ident: ref11 doi: 10.1109/TSP.2014.2303422 – ident: ref37 doi: 10.1109/TWC.2020.3004330 – ident: ref24 doi: 10.1109/LWC.2021.3064992 – ident: ref43 doi: 10.1017/CBO9780511804441 – ident: ref36 doi: 10.1109/JIOT.2019.2913219 – ident: ref32 doi: 10.1109/TWC.2021.3133491 – ident: ref1 doi: 10.1109/MWC.001.1900534 – ident: ref26 doi: 10.1109/LWC.2019.2961656 – ident: ref17 doi: 10.1109/TWC.2020.2990766 – ident: ref25 doi: 10.1109/TCOMM.2020.3024621 – ident: ref41 doi: 10.1109/JSAC.2020.3007043 – ident: ref39 doi: 10.1109/LSP.2018.2890164 – ident: ref10 doi: 10.1109/TVT.2013.2285244 – ident: ref34 doi: 10.1109/TSP.2021.3073813 – ident: ref40 doi: 10.1109/TCOMM.2017.2664860 – ident: ref4 doi: 10.1109/JSYST.2018.2827077 – ident: ref30 doi: 10.1109/LWC.2021.3072502
SSID	ssj0004033
Score	2.5291328
Snippet	In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1
SubjectTerms	Algorithms Beamforming Communication networks Communications networks Covariance matrix Energy transfer Intelligent reflecting surface Iterative algorithms Iterative methods MIMO MIMO communication Optimization phase shift optimization secrecy throughput Transmission efficiency Wireless communications Wireless networks WPCN
Title	Secrecy Throughput Maximization for IRS-aided MIMO Wireless Powered Communication Networks
URI	https://ieeexplore.ieee.org/document/9913501 https://www.proquest.com/docview/2737576445
Volume	70
WOSCitedRecordID	wos000937284600033&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: 1558-0857 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004033 issn: 0090-6778 databaseCode: RIE dateStart: 19720101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEJ4A8aAHX2hE0ezBmxbqbmm7R0MkcigQwYR4abb7SEgQCA-j_97ZtqBEY-KpTbPbNDu7O99055sP4DoJbVaY1A7lSjqeDKXDtTCOwgmTCG48IZNUbCLodMLhkPcKcLvhwmit0-QzXbO36Vm-msqV_VVWRyxjz8GKUAwCP-NqfXEgXZZXnLTp7EG4Jsi4vD5odqMIQ0FKa8wWNGfelhNKVVV-bMWpf2kd_O_LDmE_x5HkPjP8ERT05Bj2vlUXLMNL30JC-UEGmRbPbLUkkXgfvebUS4J4lbSf-o6tEqlI1I66xCbDjnHzIz2rnoZPtwgkpJMljS9O4Ln1MGg-OrmUgiNxwS4daViCWEsERik34bgNJkopz3DrrrkOjGRSUI4XoUzD-KGvGiLUuFw19mKMnUJpMp3oMyCeoSJAUEaZQA_oitBFyEMlxbjL6n36Fbhbj20s8zrjVu5iHKfxhsvj1B6xtUec26MCN5s-s6zKxp-ty9YCm5b54FegujZhnC_ERYztAwypPK9x_nuvC9i1787ohVUoLecrfQk78m05Wsyv0jn2CUMuzrA
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD7MC6gP3sV5zYNvWleTbG0eRZQN7TbcBPGlpLnAYG5jF9F_70nbTYci-NRSElpykpzvNOc7H8BZErqsMGU8KrTyuAqVJ4y0nsYJk0hhuVRJKjYR1Ovh87NoFuBixoUxxqTJZ-bS3aZn-bqvJu5XWQmxjDsHW4ClMufUz9haXyxIn-U1J11CexBOKTK-KLVvGlGEwSCll8yVNGd8zg2luio_NuPUw9xt_O_bNmE9R5LkOjP9FhRMbxvWvtUX3IGXlgOF6oO0MzWewWRMIvneec3JlwQRK6k9tjxXJ1KTqBY1iEuH7eL2R5pOPw2fzlFISD1LGx_twtPdbfum6uViCp7CJTv2lGUJoi0ZWK39ROBGmGituRXOYQsTWMWUpAIvUtuyrYQVXZahwQVrsBdjbA8We_2e2QfCLZUBwjLKJPpAX4Y-gh6qKEZeTvGzUoSr6djGKq807gQvunEacfgiTu0RO3vEuT2KcD7rM8jqbPzZesdZYNYyH_wiHE1NGOdLcRRj-wCDKs7LB7_3OoWVajt6iB9q9ftDWHXvyciGR7A4Hk7MMSyrt3FnNDxJ59snLrfR9w
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=Secrecy+Throughput+Maximization+for+IRS-Aided+MIMO+Wireless+Powered+Communication+Networks&rft.jtitle=IEEE+transactions+on+communications&rft.au=Shi%2C+Weiping&rft.au=Wu%2C+Qingqing&rft.au=Xiao%2C+Fu&rft.au=Shu%2C+Feng&rft.date=2022-11-01&rft.issn=0090-6778&rft.eissn=1558-0857&rft.volume=70&rft.issue=11&rft.spage=7520&rft.epage=7535&rft_id=info:doi/10.1109%2FTCOMM.2022.3212734&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TCOMM_2022_3212734
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0090-6778&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0090-6778&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0090-6778&client=summon