Cache-Enabled Multicast Content Pushing With Structured Deep Learning

The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strat...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE journal on selected areas in communications Ročník 39; číslo 7; s. 2135 - 2149
Hlavní autoři:	Chen, Qi, Wang, Wei, Chen, Wei, Yu, F. Richard, Zhang, Zhaoyang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Caching content pushing Convolution Coupling Couplings Datasets Decisions Deep learning Dynamic programming edge caching Multicast algorithms Multicasting Neural networks Optimization Pushing Training Upper bound Upper bounds Wireless networks
ISSN:	0733-8716, 1558-0008
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strategy while considering both the transmission and caching resources, which jointly result in the complicated coupling among pushing decisions and lead to high computational complexity. Unlike most existing approaches which simplify the pushing problem via bypassing the complicated coupling, in this paper, we propose a multicast content pushing strategy to maximize the offloaded traffic with the cost on content caching based on structured deep learning. Specifically, we design the convolution stage to extract the spatio-temporal correlations of one content item between different pushing decisions, and construct the fully-connected stage to capture the spatial coupling among the decisions of pushing different content items to different user devices. Moreover, to address the absence of the ground truth on multicast content pushing, we relax the transmission constraint to derive a performance upper bound for guiding the training direction. This relaxed problem is solved based on dynamic programming in a bottom-up manner. Compared to the state-of-the-art baselines including both the traditional model-based and the general neural network-based strategies, the proposed pushing strategy achieves significant performance gain in both the random-generated dataset and the real LastFM dataset. In addition, it is also shown that the proposed strategy is robust to the uncertainty of user request information.
AbstractList	The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strategy while considering both the transmission and caching resources, which jointly result in the complicated coupling among pushing decisions and lead to high computational complexity. Unlike most existing approaches which simplify the pushing problem via bypassing the complicated coupling, in this paper, we propose a multicast content pushing strategy to maximize the offloaded traffic with the cost on content caching based on structured deep learning. Specifically, we design the convolution stage to extract the spatio-temporal correlations of one content item between different pushing decisions, and construct the fully-connected stage to capture the spatial coupling among the decisions of pushing different content items to different user devices. Moreover, to address the absence of the ground truth on multicast content pushing, we relax the transmission constraint to derive a performance upper bound for guiding the training direction. This relaxed problem is solved based on dynamic programming in a bottom-up manner. Compared to the state-of-the-art baselines including both the traditional model-based and the general neural network-based strategies, the proposed pushing strategy achieves significant performance gain in both the random-generated dataset and the real LastFM dataset. In addition, it is also shown that the proposed strategy is robust to the uncertainty of user request information.
Author	Chen, Wei Chen, Qi Zhang, Zhaoyang Wang, Wei Yu, F. Richard
Author_xml	– sequence: 1 givenname: Qi surname: Chen fullname: Chen, Qi email: chenqi_123@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Wei orcidid: 0000-0003-2153-9075 surname: Wang fullname: Wang, Wei email: wangw@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Wei surname: Chen fullname: Chen, Wei email: wchen@tsinghua.edu.cn organization: Department of Electronic Engineering, Tsinghua University, Beijing, China – sequence: 4 givenname: F. Richard orcidid: 0000-0003-1006-7594 surname: Yu fullname: Yu, F. Richard email: richard.yu@carleton.ca organization: Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada – sequence: 5 givenname: Zhaoyang surname: Zhang fullname: Zhang, Zhaoyang email: ning_ming@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
BookMark	eNp9kD1PwzAQQC1UJNrCD0AskZhT_BmfxyqULxWBVBCj5aRX6qokxXYG_j2pihgYmDzce3fyG5FB0zZIyDmjE8aouXpYTMsJp5xNBNUgjTgiQ6YU5JRSGJAh1ULkoFlxQkYxbihlUgIfklnp6jXms8ZVW1xmj902-drFlJVtk7BJ2XMX1755z958WmeLFLo6daEnrxF32RxdaPrpKTleuW3Es593TF5vZi_lXT5_ur0vp_O85kakHFZcGCgq6eSSCwfMCG5AYw2gsJDMACqtGEpwnDNecUVdoaolR1kBainG5PKwdxfazw5jspu2C01_0nIlmdQSjOgpfaDq0MYYcGVrn1zy_Y-C81vLqN03s_tmdt_M_jTrTfbH3AX_4cLXv87FwfGI-MsbyTUDKr4Bmlh3QQ
CODEN	ISACEM
CitedBy_id	crossref_primary_10_1007_s10922_022_09665_5 crossref_primary_10_1109_TMC_2023_3340733 crossref_primary_10_1109_TBC_2025_3541889 crossref_primary_10_1109_TCOMM_2023_3341899 crossref_primary_10_1109_TMC_2023_3349315 crossref_primary_10_1109_JIOT_2024_3437452 crossref_primary_10_1109_TNSM_2022_3218081 crossref_primary_10_1109_JIOT_2023_3327656 crossref_primary_10_1109_TNSM_2024_3361796 crossref_primary_10_1109_JSYST_2024_3442958 crossref_primary_10_3390_network1020010
Cites_doi	10.1109/TCOMM.2014.2351796 10.1109/JSAC.2016.2545413 10.1109/JSAC.2018.2844939 10.1109/TNET.2018.2825460 10.1109/GLOCOM.2017.8254142 10.1109/ICC.2014.6883600 10.1109/MCOM.2012.6353684 10.1109/TNET.2015.2478476 10.1109/SPAWC.2017.8227766 10.1109/ADPRL.2014.7010638 10.1109/ACCESS.2017.2679198 10.1109/LCOMM.2019.2954311 10.1109/TCOMM.2019.2912384 10.1109/MCOM.001.1900103 10.1109/SPAWC.2019.8815489 10.1109/TCOMM.2019.2929129 10.1109/TCOMM.2017.2773522 10.1137/S0097539700382820 10.1109/MCOM.2014.6736753 10.1109/MWC.2018.1700317 10.1109/JSAC.2018.2844985 10.1109/INFCOM.2012.6195469 10.1109/TWC.2019.2947591 10.1109/ISIT.2014.6874793 10.1109/TCOMM.2017.2648800 10.1109/LCOMM.2016.2543698 10.1109/JSAC.2019.2904352 10.1109/MCOM.2015.7081087 10.1109/ADPRL.2013.6615006 10.1109/TWC.2020.3027596 10.1109/WCNC.2014.6952688 10.1109/TWC.2016.2514418
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD L7M
DOI	10.1109/JSAC.2021.3078493
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	1558-0008
EndPage	2149
ExternalDocumentID	10_1109_JSAC_2021_3078493 9427180
Genre	orig-research
GrantInformation_xml	– fundername: Ningbo S&T Major Project grantid: 2019B10079 – fundername: ZJU-SUTD Innovation, Design and Entrepreneurship Alliance – fundername: National Key Research and Development Program of China grantid: 2018YFB1801104 funderid: 10.13039/501100012166 – fundername: Fundamental Research Funds for the Central Universities grantid: 2020QNA5011 funderid: 10.13039/501100012226 – fundername: National Natural Science Foundation of China grantid: U20A20158 funderid: 10.13039/501100001809
GroupedDBID	-~X .DC 0R~ 29I 3EH 4.4 41~ 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ADRHT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 IBMZZ ICLAB IES IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TN5 VH1 AAYXX CITATION 7SP 8FD L7M
ID	FETCH-LOGICAL-c293t-8f23986b4a4d23a81932987ec885e64198e5751e48a2212b250a65bd2e4b8e743
IEDL.DBID	RIE
ISICitedReferencesCount	16
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000663527600021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0733-8716
IngestDate	Sun Nov 30 03:58:39 EST 2025 Tue Nov 18 20:51:56 EST 2025 Sat Nov 29 03:23:03 EST 2025 Wed Aug 27 02:50:50 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	7
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-8f23986b4a4d23a81932987ec885e64198e5751e48a2212b250a65bd2e4b8e743
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-2153-9075 0000-0003-1006-7594
PQID	2541474893
PQPubID	85481
PageCount	15
ParticipantIDs	crossref_citationtrail_10_1109_JSAC_2021_3078493 ieee_primary_9427180 crossref_primary_10_1109_JSAC_2021_3078493 proquest_journals_2541474893
PublicationCentury	2000
PublicationDate	2021-07-01
PublicationDateYYYYMMDD	2021-07-01
PublicationDate_xml	– month: 07 year: 2021 text: 2021-07-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE journal on selected areas in communications
PublicationTitleAbbrev	J-SAC
PublicationYear	2021
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 ref15 ref14 ref31 ref30 ref33 ref11 ref32 bastug (ref10) 2015 ref2 ref1 ref17 ref16 ref19 ref18 shukla (ref28) 2017 ref24 ref23 ref26 ref20 ref22 ref21 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 dai (ref25) 2012
References_xml	– ident: ref24 doi: 10.1109/TCOMM.2014.2351796 – ident: ref19 doi: 10.1109/JSAC.2016.2545413 – ident: ref1 doi: 10.1109/JSAC.2018.2844939 – ident: ref21 doi: 10.1109/TNET.2018.2825460 – ident: ref12 doi: 10.1109/GLOCOM.2017.8254142 – ident: ref26 doi: 10.1109/ICC.2014.6883600 – ident: ref27 doi: 10.1109/MCOM.2012.6353684 – ident: ref35 doi: 10.1109/TNET.2015.2478476 – ident: ref16 doi: 10.1109/SPAWC.2017.8227766 – ident: ref30 doi: 10.1109/ADPRL.2014.7010638 – start-page: 2444 year: 2012 ident: ref25 article-title: Collaborative hierarchical caching with dynamic request routing for massive content distribution publication-title: Proc IEEE InfoCom – ident: ref3 doi: 10.1109/ACCESS.2017.2679198 – start-page: 1 year: 2017 ident: ref28 article-title: Hold'em caching: Proactive retention-aware caching with multi-path routing for wireless edge networks publication-title: Proc ACM Mobihoc – ident: ref14 doi: 10.1109/LCOMM.2019.2954311 – ident: ref2 doi: 10.1109/TCOMM.2019.2912384 – ident: ref9 doi: 10.1109/MCOM.001.1900103 – ident: ref13 doi: 10.1109/SPAWC.2019.8815489 – ident: ref4 doi: 10.1109/TCOMM.2019.2929129 – ident: ref5 doi: 10.1109/TCOMM.2017.2773522 – ident: ref29 doi: 10.1137/S0097539700382820 – ident: ref23 doi: 10.1109/MCOM.2014.6736753 – ident: ref18 doi: 10.1109/MWC.2018.1700317 – ident: ref8 doi: 10.1109/JSAC.2018.2844985 – ident: ref22 doi: 10.1109/INFCOM.2012.6195469 – ident: ref15 doi: 10.1109/TWC.2019.2947591 – ident: ref20 doi: 10.1109/ISIT.2014.6874793 – ident: ref34 doi: 10.1109/TCOMM.2017.2648800 – ident: ref33 doi: 10.1109/LCOMM.2016.2543698 – ident: ref17 doi: 10.1109/JSAC.2019.2904352 – ident: ref6 doi: 10.1109/MCOM.2015.7081087 – start-page: 161 year: 2015 ident: ref10 article-title: A transfer learning approach for cache-enabled wireless networks publication-title: Proc WiOpt – ident: ref31 doi: 10.1109/ADPRL.2013.6615006 – ident: ref32 doi: 10.1109/TWC.2020.3027596 – ident: ref7 doi: 10.1109/WCNC.2014.6952688 – ident: ref11 doi: 10.1109/TWC.2016.2514418
SSID	ssj0014482
Score	2.4449198
Snippet	The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	2135
SubjectTerms	Caching content pushing Convolution Coupling Couplings Datasets Decisions Deep learning Dynamic programming edge caching Multicast algorithms Multicasting Neural networks Optimization Pushing Training Upper bound Upper bounds Wireless networks
Title	Cache-Enabled Multicast Content Pushing With Structured Deep Learning
URI	https://ieeexplore.ieee.org/document/9427180 https://www.proquest.com/docview/2541474893
Volume	39
WOSCitedRecordID	wos000663527600021&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: 1558-0008 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014482 issn: 0733-8716 databaseCode: RIE dateStart: 19830101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH_M4UEPfk1xOiUHT2Jnm6ZNehxzQ0TGYIq7lTZ91YF0Y-v8-03SrCiK4K2Hl1J-L837yvs9gCuGSJM8RRXk6JYc6aIjQk86lPMgzXQiEg2J6yMfjcR0Go0bcFP3wiCiuXyGXf1oavnZXK51quw2YlQdpSpA3-KcV71adcVAhRmmYsB939FBgK1gem50-zDp9VUkSL2u2tCCRf43G2SGqvw4iY15Ge7_78MOYM-6kaRX6f0QGlgcwe4XcsEWDPqaq9kZmOaojJhOW5msSmIIqYqSjNcm_UReZuUbmRge2fVSSd4hLojlXX09hufh4Kl_79ihCY5Ulrt0RK4Z_cKUJSyjfiK0gxYJjlKIAEPmRQJ1qQWZSKgyW6lygZJQqYUiSwUqf-IEmsW8wFMg3E8zn-euZIyzIBEpz_LAD7KMhlK5HbwN7gbGWFpGcT3Y4j02kYUbxRr5WCMfW-TbcF0vWVR0Gn8JtzTUtaBFuQ2dja5i-8OtYqrHmRsmnbPfV53Djn53ddO2A02FKl7AtvwoZ6vlpdlLn2rOw-A
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEJ4QNVEPvtCIovbgyVhot9vu9kgQgoqEBIzcmnY7VRJTCBR_v7vb0mg0Jt56mE2bb7Y7r51vAK4pIgmTCGWQo1pyhIUm92xhEsbcKFaJSNQkrn02GPDJxB9W4LbshUFEffkMG-pR1_LjmVipVFnTp0QepTJA33QpJXberVXWDGSgoWsGzHFMFQYUNUzb8psPo1ZbxoLEbsgtzanvfLNCeqzKj7NYG5ju_v8-7QD2CkfSaOWaP4QKpkew-4VesAqdtmJrNju6PSo2dK-tCJeZoSmp0swYrnQCyniZZm_GSDPJrhZS8g5xbhTMq6_H8NztjNs9sxibYAppuzOTJ4rTz4toSGPihFy5aD5nKDh30aO2z1EVW5DykEjDFUknKPSkYgjSiKP0KE5gI52leAoGc6LYYYklKGXUDXnE4sR13DgmnpCOB6uBtYYxEAWnuBpt8R7o2MLyA4V8oJAPCuRrcFMumeeEGn8JVxXUpWCBcg3qa10FxS-3DIgaaK65dM5-X3UF273xUz_o3w8ez2FHvSe_d1uHDYkwXsCW-Mimy8Wl3lefyaTHJw
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=Cache-Enabled+Multicast+Content+Pushing+With+Structured+Deep+Learning&rft.jtitle=IEEE+journal+on+selected+areas+in+communications&rft.au=Chen%2C+Qi&rft.au=Wang%2C+Wei&rft.au=Chen%2C+Wei&rft.au=Yu%2C+F+Richard&rft.date=2021-07-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0733-8716&rft.eissn=1558-0008&rft.volume=39&rft.issue=7&rft.spage=2135&rft_id=info:doi/10.1109%2FJSAC.2021.3078493&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0733-8716&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0733-8716&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0733-8716&client=summon