CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate t...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on wireless communications Jg. 23; H. 9; S. 11168 - 11183
Hauptverfasser:	Wu, Tong, Chen, Zhiyong, He, Dazhi, Qian, Liang, Xu, Yin, Tao, Meixia, Zhang, Wenjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Artificial intelligence Channel estimation Channel noise Codes Communications systems Decoding Diffusion layers Diffusion models Error correcting codes Generative adversarial networks Image coding Image communication Image transmission joint source-channel coding Mean square errors Noise reduction Sampling semantic communications Semantics Wireless communication Wireless communications wireless image transmission
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models. We also theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.
AbstractList	Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models. We also theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.
Author	He, Dazhi Chen, Zhiyong Wu, Tong Xu, Yin Qian, Liang Zhang, Wenjun Tao, Meixia
Author_xml	– sequence: 1 givenname: Tong surname: Wu fullname: Wu, Tong email: wu_tong@sjtu.edu.cn organization: Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China – sequence: 2 givenname: Zhiyong orcidid: 0000-0003-3540-389X surname: Chen fullname: Chen, Zhiyong email: zhiyongchen@sjtu.edu.cn organization: Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China – sequence: 3 givenname: Dazhi orcidid: 0000-0001-6637-0088 surname: He fullname: He, Dazhi email: hedazhi@sjtu.edu.cn organization: Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China – sequence: 4 givenname: Liang surname: Qian fullname: Qian, Liang email: lqian@sjtu.edu.cn organization: Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai, China – sequence: 5 givenname: Yin orcidid: 0000-0003-4429-5982 surname: Xu fullname: Xu, Yin email: xuyin@sjtu.edu.cn organization: Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China – sequence: 6 givenname: Meixia orcidid: 0000-0002-0799-0954 surname: Tao fullname: Tao, Meixia email: mxtao@sjtu.edu.cn organization: Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai, China – sequence: 7 givenname: Wenjun orcidid: 0000-0001-8799-1182 surname: Zhang fullname: Zhang, Wenjun email: zhangwenjun@sjtu.edu.cn organization: Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai, China
BookMark	eNp9kDtPwzAURi1UJNrCzsAQiTnFrzzMhlJeUisGijparnMNrhK72MnAvyelHRAD073DOffxTdDIeQcIXRI8IwSLm9W6mlFM-YyxQlDOT9CYZFmZUsrL0b5neUpokZ-hSYxbjEmRZ9kYLav5fHmbVB_KOWiSOThvo3Xvydwa00frXbL0NTQxMT4kaxuggRiTV2iV66xOKt-2vbNadQMaz9GpUU2Ei2OdoreH-1X1lC5eHp-ru0WqqaBdWhdCb-pSiY3OTE1FjTNqCGwEMKKM4QXLNWVgOB9wVZYZBkWpgeEHrgjkbIquD3N3wX_2EDu59X1ww0rJCKai4KIgA5UfKB18jAGM1Lb7ObQLyjaSYLmPTg7RyX108hjdIOI_4i7YVoWv_5Srg2IB4BfOS8x4yb4BkTp7UA
CODEN	ITWCAX
CitedBy_id	crossref_primary_10_1109_LWC_2024_3515656 crossref_primary_10_1109_LWC_2025_3542811 crossref_primary_10_1109_LCOMM_2025_3571544 crossref_primary_10_1109_TWC_2024_3512652 crossref_primary_10_1016_j_eng_2025_06_018 crossref_primary_10_1016_j_eng_2025_07_022 crossref_primary_10_1109_JIOT_2025_3530462 crossref_primary_10_1109_TWC_2025_3556118 crossref_primary_10_1016_j_phycom_2025_102836 crossref_primary_10_1007_s40435_025_01849_6 crossref_primary_10_1109_TWC_2025_3543373 crossref_primary_10_1016_j_knosys_2025_114464 crossref_primary_10_1109_TWC_2025_3535714 crossref_primary_10_1109_JSAC_2025_3559160 crossref_primary_10_1109_TWC_2025_3543167 crossref_primary_10_1109_LWC_2024_3488859 crossref_primary_10_3390_s25010269 crossref_primary_10_1109_JIOT_2024_3496491 crossref_primary_10_1109_JSAC_2024_3460084 crossref_primary_10_1109_LWC_2024_3474570 crossref_primary_10_1109_TMC_2025_3558793 crossref_primary_10_1109_TCCN_2025_3567613 crossref_primary_10_3390_app15063210 crossref_primary_10_1109_TCCN_2025_3537103 crossref_primary_10_1109_TWC_2025_3553851 crossref_primary_10_1109_JSAC_2025_3559158 crossref_primary_10_1109_TCCN_2025_3531486
Cites_doi	10.1109/TCSVT.2021.3082521 10.1109/ICASSP49357.2023.10094735 10.1109/VTC2022-Fall57202.2022.10012860 10.1109/TCOMM.2018.2814603 10.1109/ICCV48922.2021.00986 10.1109/CVPR52729.2023.01770 10.1109/JSAC.2022.3180802 10.1007/978-3-319-24574-4_28 10.1109/JSAC.2023.3287547 10.1109/MSP.2010.938080 10.1109/GLOBECOM54140.2023.10436728 10.1109/TCCN.2019.2919300 10.1109/ICCV48922.2021.01410 10.1109/ACSSC.2003.1292216 10.1109/INFOCT.2019.8710893 10.1109/TIP.2017.2662206 10.1109/COMST.2022.3223224 10.1109/49.947033 10.1002/j.1538-7305.1948.tb01338.x 10.5244/C.30.87 10.48550/ARXIV.1706.03762 10.1109/CVPRW.2017.149 10.23919/JCIN.2021.9663101 10.1109/tcomm.2024.3386577 10.5555/2969033.2969125 10.1109/CACRE54574.2022.9834193
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TWC.2024.3379244
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) Online IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
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	1558-2248
EndPage	11183
ExternalDocumentID	10_1109_TWC_2024_3379244 10480348
Genre	orig-research
GrantInformation_xml	– fundername: National Key Research and Development Project of China grantid: 2023YFB2906200 – fundername: Fundamental Research Funds for the Central Universities funderid: 10.13039/501100012226 – fundername: NSF of China grantid: 62222111; 62125108 funderid: 10.13039/501100001809
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 IES IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c292t-d79cbd8a9bc5fd29d052f1eb9e31aff4736c23ef44292a8850ea22fe5364a1e63
IEDL.DBID	RIE
ISICitedReferencesCount	45
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001312963400019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1536-1276
IngestDate	Fri Jul 25 12:10:24 EDT 2025 Tue Nov 18 22:38:01 EST 2025 Sat Nov 29 06:24:00 EST 2025 Wed Aug 27 01:52:35 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	9
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-c292t-d79cbd8a9bc5fd29d052f1eb9e31aff4736c23ef44292a8850ea22fe5364a1e63
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-3540-389X 0000-0001-8799-1182 0000-0002-0799-0954 0000-0001-6637-0088 0000-0003-4429-5982
PQID	3102974971
PQPubID	105736
PageCount	16
ParticipantIDs	crossref_citationtrail_10_1109_TWC_2024_3379244 ieee_primary_10480348 crossref_primary_10_1109_TWC_2024_3379244 proquest_journals_3102974971
PublicationCentury	2000
PublicationDate	2024-09-01
PublicationDateYYYYMMDD	2024-09-01
PublicationDate_xml	– month: 09 year: 2024 text: 2024-09-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on wireless communications
PublicationTitleAbbrev	TWC
PublicationYear	2024
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 Loshchilov (ref49) ref15 ref14 Rezende (ref12) ref11 (ref45) 2008 Nichol (ref46); 139 ref18 Song (ref4) Ho (ref3); 33 Zhu (ref17) 2023 Kingma (ref10) Chen (ref22) 2023 Grassucci (ref24) 2023 ref42 Ajay (ref16) ref41 ref44 Zheng (ref8) 2023 ref7 ref9 Yang (ref5) 2022 Yilmaz (ref23) 2023 Xu (ref34) 2022 Diederik (ref48) ref40 ref35 ref37 ref36 ref31 ref30 ref33 ref32 ref1 ref39 ref38 Chenlin (ref6) Grassucci (ref21) 2023 ref26 ref25 Chen (ref47) 2023 Choukroun (ref20) ref28 Kim (ref19) ref27 ref29 Krizhevsky (ref43) 2009 Sohl-Dickstein (ref2)
References_xml	– year: 2023 ident: ref8 article-title: A reparameterized discrete diffusion model for text generation publication-title: arXiv:2302.05737 – ident: ref33 doi: 10.1109/TCSVT.2021.3082521 – ident: ref36 doi: 10.1109/ICASSP49357.2023.10094735 – year: 2023 ident: ref17 article-title: MADiff: Offline multi-agent learning with diffusion models publication-title: arXiv:2305.17330 – start-page: 2256 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref2 article-title: Deep unsupervised learning using non-equilibrium thermodynamics – ident: ref26 doi: 10.1109/VTC2022-Fall57202.2022.10012860 – ident: ref31 doi: 10.1109/TCOMM.2018.2814603 – ident: ref38 doi: 10.1109/ICCV48922.2021.00986 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. ident: ref49 article-title: SGDR: Stochastic gradient descent with warm restarts – start-page: 1530 volume-title: Proc. 32nd Int. Conf. Int. Conf. Mach. Learn. ident: ref12 article-title: Variational inference with normalizing flows – ident: ref9 doi: 10.1109/CVPR52729.2023.01770 – volume: 33 start-page: 6840 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref3 article-title: Denoising diffusion probabilistic models – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent ident: ref6 article-title: SDEdit: Guided image synthesis and editing with stochastic differential equations – start-page: 1 volume-title: Proc. 11th Int. Conf. Learn. Represent. ident: ref16 article-title: Is conditional generative modeling all you need for decision making? – ident: ref35 doi: 10.1109/JSAC.2022.3180802 – volume: 139 start-page: 8162 volume-title: Proc. 38th Int. Conf. Mach. Learn. ident: ref46 article-title: Improved denoising diffusion probabilistic models – year: 2009 ident: ref43 article-title: Learning multiple layers of features from tiny images – ident: ref40 doi: 10.1007/978-3-319-24574-4_28 – year: 2022 ident: ref34 article-title: Deep joint source-channel coding for semantic communications publication-title: arXiv:2211.08747 – year: 2022 ident: ref5 article-title: Diffusion models: A comprehensive survey of methods and applications publication-title: arXiv:2209.00796 – ident: ref18 doi: 10.1109/JSAC.2023.3287547 – ident: ref29 doi: 10.1109/MSP.2010.938080 – start-page: 1 volume-title: Proc. 26th Int. ITG Workshop Smart Antennas 13th Conf. Syst. Commun. Coding (WSA SCC) ident: ref19 article-title: Learning end-to-end channel coding with diffusion models – ident: ref1 doi: 10.1109/GLOBECOM54140.2023.10436728 – ident: ref32 doi: 10.1109/TCCN.2019.2919300 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. ident: ref48 article-title: Adam: A method for stochastic optimization – year: 2023 ident: ref23 article-title: High perceptual quality wireless image delivery with denoising diffusion models publication-title: arXiv:2309.15889 – ident: ref7 doi: 10.1109/ICCV48922.2021.01410 – year: 2023 ident: ref22 article-title: CommIN: Semantic image communications as an inverse problem with INN-guided diffusion models publication-title: arXiv:2310.01130 – ident: ref39 doi: 10.1109/ACSSC.2003.1292216 – ident: ref14 doi: 10.1109/INFOCT.2019.8710893 – ident: ref13 doi: 10.1109/TIP.2017.2662206 – ident: ref27 doi: 10.1109/COMST.2022.3223224 – ident: ref30 doi: 10.1109/49.947033 – year: 2023 ident: ref47 article-title: On the importance of noise scheduling for diffusion models publication-title: arXiv:2301.10972 – volume-title: Frame Stucture Channel Coding and Modulation for the Second Generation Digital Terrestrial Television Broadcasting System (DVB-T2) year: 2008 ident: ref45 – ident: ref28 doi: 10.1002/j.1538-7305.1948.tb01338.x – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. ident: ref4 article-title: Denoising diffusion implicit models – ident: ref41 doi: 10.5244/C.30.87 – year: 2023 ident: ref24 article-title: Diffusion models for audio semantic communication publication-title: arXiv:2309.07195 – ident: ref42 doi: 10.48550/ARXIV.1706.03762 – ident: ref44 doi: 10.1109/CVPRW.2017.149 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. (ICLR) ident: ref10 article-title: Auto-encoding variational Bayes – ident: ref25 doi: 10.23919/JCIN.2021.9663101 – start-page: 1 volume-title: Proc. 11th Int. Conf. Learn. Represent. ident: ref20 article-title: Denoising diffusion error correction codes – ident: ref37 doi: 10.1109/tcomm.2024.3386577 – year: 2023 ident: ref21 article-title: Generative semantic communication: Diffusion models beyond bit recovery publication-title: arXiv:2306.04321 – ident: ref11 doi: 10.5555/2969033.2969125 – ident: ref15 doi: 10.1109/CACRE54574.2022.9834193
SSID	ssj0017655
Score	2.6691716
Snippet	Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	11168
SubjectTerms	Adaptation models Algorithms Artificial intelligence Channel estimation Channel noise Codes Communications systems Decoding Diffusion layers Diffusion models Error correcting codes Generative adversarial networks Image coding Image communication Image transmission joint source-channel coding Mean square errors Noise reduction Sampling semantic communications Semantics Wireless communication Wireless communications wireless image transmission
Title	CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications
URI	https://ieeexplore.ieee.org/document/10480348 https://www.proquest.com/docview/3102974971
Volume	23
WOSCitedRecordID	wos001312963400019&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: 1558-2248 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017655 issn: 1536-1276 databaseCode: RIE dateStart: 20020101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LS8MwGA9ueNCDz4nTKTl48dCtTdKm8Sarw8uG4MTdSpoHFGon6-bfb5J2Y0MUvPWQlPJ9_d6PHwB3IUVYoCDyAiqJR2TIPeOFSM_HxrxmnAaaawc2QSeTeDZjL82wupuFUUq55jPVt4-uli_nYmVTZUbCSexjErdAi9KoHtbalAxo5CBOjQRbYBm6qUn6bDB9H5pIEJE-xtTEG2THBjlQlR-a2JmX0fE_P-wEHDV-JHysGX8K9lR5Bg63tgueg_EwScYP0M4PlKqAiSrnuc0MwCTXemWzZNAioRUVNI4rtG2whVF78FV9GGrnAu7MjlQd8DZ6mg6fvQY9wROIoaUnKROZjDnLRKglYtIPkQ5UxhQOuNaE4kggrDSxgFU8jkNfcYS0MvQjPFARvgDtcl6qSwBRoJGUGfWFJMQXNnUYxdIOtUqijabtgsGanqloVotbhIsidSGGz1LDgdRyIG040AX3mxuf9VqNP852LMW3ztXE7oLemmdpI3hVarxVZEIkRoOrX65dgwP79rpPrAfay8VK3YB98bXMq8Wt-6e-AY11x-w
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LS8MwGP_QKagH3-J0ag5ePFTTJG0ab7I5JrohONFbafOAwezEbf79JmkdG6LgrYeElu_r9378AM4jTqgkYRyEXLGAqSgLrBeiAkytec0zHprMeLAJ3uslr6_isRpW97MwWmvffKYv3aOv5auRnLpUmZVwlmDKkmVYiRgjuBzXmhUNeOxBTq0MO2gZPqtKYnHVf2naWJCwS0q5jTjYghXysCo_dLE3MO2tf37aNmxWniS6KVm_A0u62IWNuf2Ce9Bttlrda-QmCAo9RC1djAYuN4BaA2OmLk-GHBbacIys64pcI-zQKj70pN8svQcSLUyPjPfhuX3bb3aCCj8hkESQSaC4kLlKMpHLyCgiFI6ICXUuNA0zYxinsSRUG-Ygq7IkibDOCDHa0o9loY7pAdSKUaEPAZHQEKVyjqViDEuXPIwT5cZaFTNW19bh6pueqayWizuMi2HqgwwsUsuB1HEgrThQh4vZjfdyscYfZ_cdxefOlcSuQ-ObZ2kleuPU-qvEBkmCh0e_XDuDtU6_-5A-3PXuj2HdvansGmtAbfIx1SewKj8ng_HHqf-_vgAfOssz
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=CDDM%3A+Channel+Denoising+Diffusion+Models+for+Wireless+Semantic+Communications&rft.jtitle=IEEE+transactions+on+wireless+communications&rft.au=Wu%2C+Tong&rft.au=Chen%2C+Zhiyong&rft.au=He%2C+Dazhi&rft.au=Liang%2C+Qian&rft.date=2024-09-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1536-1276&rft.eissn=1558-2248&rft.volume=23&rft.issue=9&rft.spage=11168&rft_id=info:doi/10.1109%2FTWC.2024.3379244&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1536-1276&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1536-1276&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1536-1276&client=summon