MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intell...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE journal on selected areas in communications Vol. 39; no. 7; pp. 2182 - 2197
Main Authors:	Hu, Jingzhi, Zhang, Hongliang, Bian, Kaigui, Renzo, Marco Di, Han, Zhu, Song, Lingyang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Accuracy Algorithms Antennas beamformer pattern design Deep learning deep reinforcement learning Electronics Engineering Sciences Entropy (Information theory) Machine learning Mapping metasurface Metasurfaces Object recognition Optimization policy gradient algorithm Radio frequency RF 3D sensing RF signals Sensors Three-dimensional displays
ISSN:	0733-8716, 1558-0008
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.
AbstractList	Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy. Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the sizes of the metasurface and the target space influence the sensing accuracy
Author	Zhang, Hongliang Bian, Kaigui Han, Zhu Renzo, Marco Di Hu, Jingzhi Song, Lingyang
Author_xml	– sequence: 1 givenname: Jingzhi orcidid: 0000-0002-1965-3576 surname: Hu fullname: Hu, Jingzhi email: jingzhi.hu@pku.edu.cn organization: Department of Electronics, Peking University, Beijing, China – sequence: 2 givenname: Hongliang orcidid: 0000-0003-3393-8612 surname: Zhang fullname: Zhang, Hongliang email: hongliang.zhang92@gmail.com organization: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA – sequence: 3 givenname: Kaigui orcidid: 0000-0003-0136-6082 surname: Bian fullname: Bian, Kaigui email: bkg@pku.edu.cn organization: Department of Computer Science, Peking University, Beijing, China – sequence: 4 givenname: Marco Di surname: Renzo fullname: Renzo, Marco Di email: marco.di-renzo@universite-paris-saclay.fr organization: Université Paris-Saclay, CNRS, Centrale-Supélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France – sequence: 5 givenname: Zhu orcidid: 0000-0002-6606-5822 surname: Han fullname: Han, Zhu email: hanzhu22@gmail.com organization: Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA – sequence: 6 givenname: Lingyang orcidid: 0000-0001-8644-8241 surname: Song fullname: Song, Lingyang email: lingyang.song@pku.edu.cn organization: Department of Electronics, Peking University, Beijing, China
BackLink	https://hal.science/hal-03357994$$DView record in HAL
BookMark	eNp9kE1PGzEQhq2KSg3QH1D1YokThw1je_3FLQrlS6kqAadeLO9mlhot3mA7SPz77ioRBw6cRpp5npnRe0gO4hCRkB8M5oyBPbu9XyznHDibC9CmtvwLmTEpTQUA5oDMQAtRGc3UN3KY8xMAq2vDZ-Tvbyz-HmMO8fGc3sSCfR8eMRY6DfI2db5Fusg55IJrendJxQXd87R5oxeIG3qHIXZDavF5ElfoUxzHx-Rr5_uM3_f1iDxc_npYXlerP1c3y8WqaoWGUmluNWtQSqaUsQCiBgVSaa-aVnnpjUWGYq1F1-i1WCsrDCpuQSvddLUUR-R0t_af790mhWef3tzgg7terNzUAyGktrZ-ZSN7smM3aXjZYi7uadimOH7nuKy5BGYZHym2o9o05Jywe1_LwE1puyltN6Xt9mmPjv7gtKH4EoZYkg_9p-bPnRkQ8f2SrbkGa8R_uf2L2A
CODEN	ISACEM
CitedBy_id	crossref_primary_10_1109_TAP_2022_3221613 crossref_primary_10_1109_TWC_2022_3145842 crossref_primary_10_1109_JSEN_2023_3332862 crossref_primary_10_3390_s23208561 crossref_primary_10_3390_s24061870 crossref_primary_10_1109_JPROC_2022_3174030 crossref_primary_10_1007_s44291_025_00084_9 crossref_primary_10_1007_s11390_025_5484_y crossref_primary_10_1109_JIOT_2024_3400960 crossref_primary_10_1109_JPROC_2022_3169771 crossref_primary_10_1109_JSAC_2023_3322788 crossref_primary_10_1109_TAP_2023_3298134 crossref_primary_10_1109_LPT_2024_3427138 crossref_primary_10_1109_TWC_2024_3365215 crossref_primary_10_1109_TIM_2024_3472800 crossref_primary_10_1109_COMST_2022_3202813 crossref_primary_10_1109_TVT_2024_3426042 crossref_primary_10_1109_MNET_128_2200446 crossref_primary_10_1109_TVT_2024_3442574 crossref_primary_10_1109_TWC_2023_3244369 crossref_primary_10_1109_TWC_2024_3433011 crossref_primary_10_1109_JPROC_2024_3397910 crossref_primary_10_1016_j_optlastec_2025_113828 crossref_primary_10_1109_TSP_2025_3586551 crossref_primary_10_3390_a18040235 crossref_primary_10_1093_nsr_nwad150 crossref_primary_10_1109_TVT_2022_3225218 crossref_primary_10_1109_TWC_2022_3178445 crossref_primary_10_1364_PRJ_538732 crossref_primary_10_1051_e3sconf_202451203003 crossref_primary_10_1109_MCOM_001_2300710 crossref_primary_10_1109_TCOMM_2023_3344143 crossref_primary_10_1038_s41598_021_99722_x crossref_primary_10_1109_TWC_2022_3144340 crossref_primary_10_1016_j_iot_2025_101658 crossref_primary_10_1109_TAP_2022_3150762
Cites_doi	10.3414/ME0592 10.1145/3241539.3241548 10.1109/JSAC.2020.3000813 10.1017/CBO9780511841224 10.1109/MVT.2019.2921627 10.1145/3290605.3300778 10.1109/ACCESS.2020.2977772 10.1109/TWC.2020.3024887 10.1109/MVT.2020.3017927 10.1145/3300061.3345442 10.1186/s13638-019-1438-9 10.1109/LCOMM.2020.3023130 10.1145/2816795.2818072 10.1103/PhysRevLett.59.2229 10.1023/B:NALA.0000005556.40898.a5 10.1016/j.cam.2008.11.012 10.1109/TCCN.2020.2992604 10.1109/PIMRC.2009.5449841 10.1109/TCI.2018.2808762 10.1109/TCOMM.2019.2924010 10.1017/CBO9780511804441 10.1109/JSAC.2020.3007041 10.1109/MSP.2015.2502784 10.1137/140983938 10.1038/nature14236 10.1109/INICA.2007.4353972 10.1109/MNET.2017.1500151 10.1145/3230543.3230579 10.1145/3234463 10.1109/TVT.2020.3024756 10.1109/MWC.001.2000142 10.1109/ACCESS.2019.2935192 10.1007/978-1-84800-070-4_4 10.1109/TVT.2020.2973073 10.1145/1138127.1138128 10.1038/s41467-019-09103-2 10.1109/iWEM.2018.8536630 10.1109/ICNN.1993.298687 10.1145/3379092.3379102 10.1109/JIOT.2016.2624800
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 – notice: Distributed under a Creative Commons Attribution 4.0 International License
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD L7M 1XC VOOES
DOI	10.1109/JSAC.2021.3078492
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore Digital Library CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access)
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	2197
ExternalDocumentID	oai:HAL:hal-03357994v1 10_1109_JSAC_2021_3078492 9427098
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61931019; 61829101; 61625101; 61941101 funderid: 10.13039/501100001809 – fundername: European Commission through the H2020 ARIADNE grantid: 871464 funderid: 10.13039/501100000780 – fundername: NSF grantid: EARS-1839818; CNS-1717454; CNS-1731424; CNS-1702850 funderid: 10.13039/100000001 – fundername: H2020 RISE-6G grantid: 101017011
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 1XC VOOES
ID	FETCH-LOGICAL-c370t-72971be55166890034060567a6bc6a5a89e1e3d73fb7d3d6938e6290767bf453
IEDL.DBID	RIE
ISICitedReferencesCount	50
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000663506900001&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	Tue Oct 14 20:44:06 EDT 2025 Mon Jun 30 10:10:02 EDT 2025 Tue Nov 18 22:12:13 EST 2025 Sat Nov 29 03:23:03 EST 2025 Wed Aug 27 02:50:51 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	7
Language	English
License	https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c370t-72971be55166890034060567a6bc6a5a89e1e3d73fb7d3d6938e6290767bf453
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-6606-5822 0000-0002-1965-3576 0000-0001-8644-8241 0000-0003-3393-8612 0000-0003-0136-6082 0000-0003-0772-8793 0000-0002-8602-864X 0000-0002-1797-2311
OpenAccessLink	https://hal.science/hal-03357994
PQID	2542501912
PQPubID	85481
PageCount	16
ParticipantIDs	hal_primary_oai_HAL_hal_03357994v1 proquest_journals_2542501912 ieee_primary_9427098 crossref_primary_10_1109_JSAC_2021_3078492 crossref_citationtrail_10_1109_JSAC_2021_3078492
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) Institute of Electrical and Electronics Engineers
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) – name: Institute of Electrical and Electronics Engineers
References	ref35 ref13 ref12 ref37 ref15 ref36 ref14 mcdonough (ref33) 2004 ref30 ref11 ref32 ref10 ref2 ref1 ref17 ref38 ref16 ref19 ref18 rubinstein (ref39) 2008 bezdek (ref34) 2003; 11 ref46 ref45 ref26 ref25 ref20 ref42 liu (ref24) 2020 ref41 ref22 ref44 ref21 ref43 ref28 ref27 goodfellow (ref31) 2016 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 sutton (ref23) 2018
References_xml	– ident: ref5 doi: 10.3414/ME0592 – ident: ref8 doi: 10.1145/3241539.3241548 – ident: ref26 doi: 10.1109/JSAC.2020.3000813 – ident: ref28 doi: 10.1017/CBO9780511841224 – year: 2018 ident: ref23 publication-title: Reinforcement Learning An Introduction – ident: ref37 doi: 10.1109/MVT.2019.2921627 – ident: ref9 doi: 10.1145/3290605.3300778 – ident: ref25 doi: 10.1109/ACCESS.2020.2977772 – ident: ref29 doi: 10.1109/TWC.2020.3024887 – ident: ref16 doi: 10.1109/MVT.2020.3017927 – ident: ref7 doi: 10.1145/3300061.3345442 – ident: ref15 doi: 10.1186/s13638-019-1438-9 – year: 2008 ident: ref39 publication-title: Simulation and the Monte Carlo Method – ident: ref22 doi: 10.1109/LCOMM.2020.3023130 – ident: ref10 doi: 10.1145/2816795.2818072 – ident: ref41 doi: 10.1103/PhysRevLett.59.2229 – ident: ref40 doi: 10.1023/B:NALA.0000005556.40898.a5 – ident: ref46 doi: 10.1016/j.cam.2008.11.012 – ident: ref19 doi: 10.1109/TCCN.2020.2992604 – ident: ref2 doi: 10.1109/PIMRC.2009.5449841 – year: 2016 ident: ref31 publication-title: Deep Learning – ident: ref12 doi: 10.1109/TCI.2018.2808762 – ident: ref38 doi: 10.1109/TCOMM.2019.2924010 – ident: ref32 doi: 10.1017/CBO9780511804441 – ident: ref27 doi: 10.1109/JSAC.2020.3007041 – ident: ref6 doi: 10.1109/MSP.2015.2502784 – ident: ref42 doi: 10.1137/140983938 – ident: ref35 doi: 10.1038/nature14236 – ident: ref43 doi: 10.1109/INICA.2007.4353972 – ident: ref4 doi: 10.1109/MNET.2017.1500151 – year: 2020 ident: ref24 article-title: Reconfigurable intelligent surfaces: Principles and opportunities publication-title: arXiv 2007 03435 – ident: ref11 doi: 10.1145/3230543.3230579 – ident: ref36 doi: 10.1145/3234463 – ident: ref18 doi: 10.1109/TVT.2020.3024756 – ident: ref20 doi: 10.1109/MWC.001.2000142 – volume: 11 start-page: 351 year: 2003 ident: ref34 article-title: Convergence of alternating optimization publication-title: Neural Parallel Sci Comput – ident: ref17 doi: 10.1109/ACCESS.2019.2935192 – ident: ref44 doi: 10.1007/978-1-84800-070-4_4 – ident: ref30 doi: 10.1109/TVT.2020.2973073 – ident: ref3 doi: 10.1145/1138127.1138128 – ident: ref21 doi: 10.1038/s41467-019-09103-2 – ident: ref13 doi: 10.1109/iWEM.2018.8536630 – ident: ref45 doi: 10.1109/ICNN.1993.298687 – year: 2004 ident: ref33 publication-title: Detection of Signals in Noise – ident: ref14 doi: 10.1145/3379092.3379102 – ident: ref1 doi: 10.1109/JIOT.2016.2624800
SSID	ssj0014482
Score	2.6039307
Snippet	Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the...
SourceID	hal proquest crossref ieee
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	2182
SubjectTerms	Accuracy Algorithms Antennas beamformer pattern design Deep learning deep reinforcement learning Electronics Engineering Sciences Entropy (Information theory) Machine learning Mapping metasurface Metasurfaces Object recognition Optimization policy gradient algorithm Radio frequency RF 3D sensing RF signals Sensors Three-dimensional displays
Title	MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning
URI	https://ieeexplore.ieee.org/document/9427098 https://www.proquest.com/docview/2542501912 https://hal.science/hal-03357994
Volume	39
WOSCitedRecordID	wos000663506900001&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/eLvHCXMwlV3fSxwxEB488aF9sFpbetZKKH0qXd1NdvOjb4d6qFgRlSJ9WbLJrC2UU-72hP73zeTiorQIvi27kyXkm93ky0y-AfhUBa-ovBeZt63NStnYTLeGlBFL2eYtKhtT_r-fqNNTfXVlzpbgS38WBhFj8hnu0GWM5fsbN6etsl1TcpUbPYCBUnJxVquPGASaESMGSoiMSECKYBa52T2-GO0FJsiLneDQujT80Rw0-EkZkLG0yj__4zjJjF89r3trsJoWk2y0QH8dlnDyGl4-kBjcgB_fsLMXlKU-uf7KjnoBzo7Rg9l82lqHLIBEcHt2PmZinyV71vxh-4i37ByjvqqLW4ksSbJev4HL8cHl3mGW6ilkTqi8o6q1qmiQQmNS0w5mmMzD-kdZ2ThpK6sNFii8Em2jvPDSCI2SB_YsVdOWlXgLy5ObCb4DFgw1HbVyLbdlFZzAS95wbXnleHidGkJ-P8C1S1rjVPLidx05R25qwqQmTOqEyRA-901uF0IbTxl_DKj1diSRfTg6qeleLkSljCnviiFsEEa9VYJnCFv3INfpe53VgSaHtWDgrnzz_63ewwvqwCJRdwuWu-kcP8CKu-t-zabb0RX_AtCo2IA
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3faxQxEB7aKqgP_qriadUgPonb7ibZ_PDtaD2uej2kPaT4ErJJthbKtdztFfzvzeTSRVEE35bdyRLyzW7yZSbfALyto1fU3rPC29YWXDS2UK1GZUQu2rIN0qaU_68TOZ2q01P9ZQPe92dhQggp-Szs4mWK5ftLt8Ktsj3NqSy12oRbNee0XJ_W6mMGkWikmIFkrEAakGOYVan3Pp0M9yMXpNVudGnFNf1tFtr8jjmQqbjKH3_kNM2MHvxfBx_C_bycJMM1_o9gI8wfw71fRAa34dtR6OwJ5qnPzz6Qw16CsyP4YLlatNYFEmFCwD05HhF2QLI9aX6QgxCuyHFICqsubSaSLMp69gRmo4-z_XGRKyoUjsmyw7q1smoCBseEwj3MOJ3HFZC0onHC1lbpUAXmJWsb6ZkXmqkgaOTPQjYtr9lT2JpfzsMzINFQ4WEr11LL6-gGXtCGKktrR-Pr5ADKmwE2LquNY9GLC5NYR6kNYmIQE5MxGcC7vsnVWmrjX8ZvImq9HYpkj4cTg_dKxmqpNb-uBrCNGPVWGZ4B7NyAbPIXuzSRKMfVYGSv9PnfW72GO-PZ0cRMDqefX8Bd7Mw6bXcHtrrFKryE2-66O18uXiW3_Ak-tdvH
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=MetaSensing%3A+Intelligent+Metasurface+Assisted+RF+3D+Sensing+by+Deep+Reinforcement+Learning&rft.jtitle=IEEE+journal+on+selected+areas+in+communications&rft.au=Hu%2C+Jingzhi&rft.au=Zhang%2C+Hongliang&rft.au=Bian%2C+Kaigui&rft.au=Renzo%2C+Marco+Di&rft.date=2021-07-01&rft.issn=0733-8716&rft.eissn=1558-0008&rft.volume=39&rft.issue=7&rft.spage=2182&rft.epage=2197&rft_id=info:doi/10.1109%2FJSAC.2021.3078492&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSAC_2021_3078492
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