Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme
Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal i...
Gespeichert in:
| Veröffentlicht in: | IEEE internet of things journal Jg. 11; H. 2; S. 1 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Piscataway
IEEE
15.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 2327-4662, 2327-4662 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes. |
|---|---|
| AbstractList | Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become a pivotal issue in semantic communications. This article proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data-oriented semantic communications (JTD-SCs) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes. Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes. |
| Author | Li, Dongxu Huang, Jianhao Huang, Chuan Zhang, Wei Qin, Xiaoqi |
| Author_xml | – sequence: 1 givenname: Jianhao orcidid: 0000-0003-1490-2390 surname: Huang fullname: Huang, Jianhao organization: School of Science and Engineering and the Future Network of Intelligence Institute, Chinese University of Hong Kong, Shenzhen, China – sequence: 2 givenname: Dongxu surname: Li fullname: Li, Dongxu organization: School of Science and Engineering and the Future Network of Intelligence Institute, Chinese University of Hong Kong, Shenzhen, China – sequence: 3 givenname: Chuan orcidid: 0000-0001-5965-0823 surname: Huang fullname: Huang, Chuan organization: School of Science and Engineering and the Future Network of Intelligence Institute, Chinese University of Hong Kong, Shenzhen, China – sequence: 4 givenname: Xiaoqi orcidid: 0000-0002-5788-0657 surname: Qin fullname: Qin, Xiaoqi organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 5 givenname: Wei orcidid: 0000-0002-1059-3642 surname: Zhang fullname: Zhang, Wei organization: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia |
| BookMark | eNp9kMtOAjEUhhujiYg8gImLJq4He5lb3RHwAiFhAa4nnc6pFJkOtmXh21uEBXHhqifp_52__W7Qpe0sIHRHyZBSIh5n08VqyAjjQ84Ep1l6gXqMsyJJ85xdns3XaOD9hhASsYyKvIfqWWdswCvpP7G0DZ7IIPHCGbABGryEVtpgFB53bbu3RslgOuuf8AhPAHbxfiedDICX3d4pSNRaWgvbGG-M_cBLtYYWbtGVllsPg9PZR-8vz6vxWzJfvE7Ho3mimEhDAgC5lIzWJdNlmlJe1FQ3da0ypRlXTcqKXAJtdA6ZFkqVClINJZUEaJmB5n30cNy7c93XHnyoNvFVNlZWTNDYwYTIYooeU8p13jvQ1c6ZVrrvipLqYLM62KwONquTzcgUfxhlwq-K4KTZ_kveH0kTv3fWRIuMC8p_AFcZhOI |
| CODEN | IITJAU |
| CitedBy_id | crossref_primary_10_1109_JIOT_2025_3564304 crossref_primary_10_1109_TCOMM_2024_3511949 crossref_primary_10_1109_JIOT_2025_3542753 crossref_primary_10_1109_JSAC_2025_3536557 crossref_primary_10_1109_TMC_2025_3564543 crossref_primary_10_1109_TWC_2024_3427675 crossref_primary_10_1109_TCOMM_2024_3364990 crossref_primary_10_3390_e27040429 crossref_primary_10_1109_COMST_2024_3416309 crossref_primary_10_1109_TCOMM_2024_3480982 crossref_primary_10_1109_JIOT_2024_3477314 crossref_primary_10_1109_TWC_2025_3554442 crossref_primary_10_1109_TCOMM_2025_3529221 crossref_primary_10_1016_j_jnca_2025_104181 |
| Cites_doi | 10.1109/TCOMM.2022.3180997 10.1109/JSAC.2021.3126087 10.1145/3474085.3475533 10.1109/ICC45041.2023.10279073 10.1109/49.848253 10.1109/MSP.2017.2765202 10.1016/j.eng.2021.11.003 10.1145/3065386 10.1145/103085.103089 10.1109/TCCN.2019.2919300 10.1109/JSTSP.2020.3034501 10.1017/CBO9780511804441 10.1109/CVPR.2016.90 10.1109/TCCN.2017.2758370 10.2352/EI.2022.34.14.COIMG-220 10.1109/JSAIT.2020.2987203 10.1109/TCSVT.2012.2221191 10.1109/30.920468 10.1109/TSP.2021.3071210 10.1007/978-3-030-04167-0_9 10.1109/ICIP40778.2020.9191247 10.1109/TSP.2018.2889951 10.1145/212094.212114 10.1145/3295748 10.1109/CVPR.2009.5206848 10.1109/MWC.2019.8752473 10.1109/CVPR.2013.57 10.1109/JSAC.2022.3180802 10.1007/978-3-7091-2945-6 10.1109/ICCV.2013.441 10.1017/CBO9780511807213 10.1109/JSAC.2016.2525418 10.1145/214762.214771 |
| 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 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/JIOT.2023.3293154 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems 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 Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2327-4662 |
| EndPage | 1 |
| ExternalDocumentID | 10_1109_JIOT_2023_3293154 10175391 |
| Genre | orig-research |
| GroupedDBID | 0R~ 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS IFIPE IPLJI JAVBF M43 OCL PQQKQ RIA RIE AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c294t-eee6aa21b82f844137b1fdbbc5cf23cd4276ae1df6e5f9cc8ce4fe81a0e185ef3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 20 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001153911600019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2327-4662 |
| IngestDate | Mon Jun 30 13:58:46 EDT 2025 Sat Nov 29 06:17:19 EST 2025 Tue Nov 18 21:14:41 EST 2025 Wed Aug 27 02:56:32 EDT 2025 |
| IsPeerReviewed | false |
| 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-c294t-eee6aa21b82f844137b1fdbbc5cf23cd4276ae1df6e5f9cc8ce4fe81a0e185ef3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-5965-0823 0000-0002-5788-0657 0000-0002-1059-3642 0000-0003-1490-2390 |
| PQID | 2912942995 |
| PQPubID | 2040421 |
| PageCount | 1 |
| ParticipantIDs | crossref_primary_10_1109_JIOT_2023_3293154 crossref_citationtrail_10_1109_JIOT_2023_3293154 proquest_journals_2912942995 ieee_primary_10175391 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-01-15 |
| PublicationDateYYYYMMDD | 2024-01-15 |
| PublicationDate_xml | – month: 01 year: 2024 text: 2024-01-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE internet of things journal |
| PublicationTitleAbbrev | JIoT |
| 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 ref35 ref12 ref34 Liu (ref5) 2022 ref15 ref37 ref30 ref11 ref33 ref10 ref2 Tan (ref31) ref1 ref17 ref39 ref38 Ballé (ref9) ref19 ref18 Zaccone (ref45) 2017 Theis (ref16) 2017 Roy (ref25) 2018 Krizhevsky (ref42) 2009 Ballé (ref14) ref24 ref23 ref26 Kingma (ref36) 2013 ref20 ref41 ref22 ref21 ref43 ref28 ref27 Gardiner (ref32) 2009 ref29 ref8 ref7 ref4 ref3 ref6 Kingma (ref44) 2014 Ballé (ref40) 2015 |
| References_xml | – volume-title: Deep Learning With TensorFlow year: 2017 ident: ref45 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Repres. (ICLR) ident: ref9 article-title: End-to-end optimized image compression – ident: ref11 doi: 10.1109/TCOMM.2022.3180997 – ident: ref26 doi: 10.1109/JSAC.2021.3126087 – ident: ref27 doi: 10.1145/3474085.3475533 – ident: ref1 doi: 10.1109/ICC45041.2023.10279073 – ident: ref10 doi: 10.1109/49.848253 – ident: ref21 doi: 10.1109/MSP.2017.2765202 – ident: ref3 doi: 10.1016/j.eng.2021.11.003 – ident: ref23 doi: 10.1145/3065386 – ident: ref8 doi: 10.1145/103085.103089 – ident: ref20 doi: 10.1109/TCCN.2019.2919300 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. (ICLR) ident: ref14 article-title: Variational image compression with a scale hyperprior – ident: ref15 doi: 10.1109/JSTSP.2020.3034501 – ident: ref35 doi: 10.1017/CBO9780511804441 – ident: ref37 doi: 10.1109/CVPR.2016.90 – ident: ref17 doi: 10.1109/TCCN.2017.2758370 – ident: ref30 doi: 10.2352/EI.2022.34.14.COIMG-220 – year: 2017 ident: ref16 article-title: Lossy image compression with compressive autoencoders publication-title: arXiv:1703.00395 – year: 2013 ident: ref36 article-title: Auto-encoding variational Bayes publication-title: arXiv:1312.6114 – ident: ref18 doi: 10.1109/JSAIT.2020.2987203 – volume-title: Stochastic Methods: A Handbook for the Natural and Social Sciences year: 2009 ident: ref32 – ident: ref13 doi: 10.1109/TCSVT.2012.2221191 – ident: ref12 doi: 10.1109/30.920468 – start-page: 6105 volume-title: Proc. Int. Conf. Mach. Learn. ident: ref31 article-title: EfficientNet: Rethinking model scaling for convolutional neural networks – ident: ref6 doi: 10.1109/TSP.2021.3071210 – ident: ref28 doi: 10.1007/978-3-030-04167-0_9 – ident: ref29 doi: 10.1109/ICIP40778.2020.9191247 – ident: ref39 doi: 10.1109/TSP.2018.2889951 – ident: ref41 doi: 10.1145/212094.212114 – year: 2009 ident: ref42 article-title: Learning multiple layers of features from tiny images – ident: ref24 doi: 10.1145/3295748 – ident: ref43 doi: 10.1109/CVPR.2009.5206848 – ident: ref4 doi: 10.1109/MWC.2019.8752473 – ident: ref38 doi: 10.1109/CVPR.2013.57 – year: 2015 ident: ref40 article-title: Density modeling of images using a generalized normalization transformation publication-title: arXiv:1511.06281 – ident: ref19 doi: 10.1109/JSAC.2022.3180802 – ident: ref33 doi: 10.1007/978-3-7091-2945-6 – year: 2022 ident: ref5 article-title: Task-oriented semantic communication with semantic reconstruction: An extended rate-distortion theory based scheme publication-title: arXiv:2201.10929 – year: 2014 ident: ref44 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – ident: ref22 doi: 10.1109/ICCV.2013.441 – ident: ref2 doi: 10.1017/CBO9780511807213 – ident: ref7 doi: 10.1109/JSAC.2016.2525418 – year: 2018 ident: ref25 article-title: Effects of degradations on deep neural network architectures publication-title: arXiv:1807.10108 – ident: ref34 doi: 10.1145/214762.214771 |
| SSID | ssj0001105196 |
| Score | 2.4866142 |
| Snippet | Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithms Bayesian analysis Coding Data compression Data recovery Data transmission deep learning Distortion Feature extraction Image classification Image coding Image transmission Iterative methods Machine learning Optimization Rate-distortion rate-distortion theory Semantic communications Semantics separate source-channel coding Statistical inference Task analysis Training variational autoencoder |
| Title | Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme |
| URI | https://ieeexplore.ieee.org/document/10175391 https://www.proquest.com/docview/2912942995 |
| Volume | 11 |
| WOSCitedRecordID | wos001153911600019&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: 2327-4662 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001105196 issn: 2327-4662 databaseCode: RIE dateStart: 20140101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA86PHhxfuJ0Sg6ehMykbZrG2_AD3WETnOCtpMkLDrUbW-ffb5J1fiAK3gpN2pDfS9_rS37vh9AJ5YoXygARiTIkKbQmmRWWUCMNtYoyECqITYh-P3t8lHc1WT1wYQAgHD6Djr8Me_lmrOc-VXbmzYfHnqu-KkS6IGt9JlSYj0bSeueSUXnWux0MO14evBM7p8Z48s33BDGVH1_g4Faum_8c0CbaqONH3F0AvoVWoNxGzaU2A66X6g4qeuNRWeGhmj1jVRp8qSqFB76osQsx8T28uhkdafyNHzI7x118CTBx90NJcMD3IbdPPD24hBfX3Ls695YneIVd9HB9Nby4IbWeAtGRTCrihp8qFbEii2zmwqBYFMyaotBc2yjWJolEqoAZmwK3UutMQ2IhY4qC8-pg4z3UKMcl7CMsrMiMlpBya5KUWmmVs4aE2kJyzSS0EF3OdK7rYuNe8-IlDz8dVOYenNyDk9fgtNDpR5fJotLGX413PRpfGi6AaKH2Es-8XoyzPJIuqPF-lx_80u0Qrbun--M4hPE2alTTORyhNf1WjWbT42Bn792Q1BA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA8yBX3xW5yfefBJyJa0Tdv4Jn4wdU7BCXsraXLB4daJq_79JlmnDlHwrdALCfe79K6X3P0QOqJc8lxqIEkkNYlypUhqEkOoFpoaSRkk0pNNJJ1O2uuJ-6pY3dfCAIC_fAYN9-jP8vVIvblUWdOZDw9drfq8o84Sk3Ktr5QKc_FIXJ1dMiqa11d33YYjCG-E1q0xHs14H0-n8uMb7B3L5co_l7SKlqsIEp9OIF9Dc1Cso5UpOwOuNusGyq9H_aLEXTl-xrLQ-FyWEt-5tsY2yMQPMLQ67Ss8UyEyPsGn-Bzgxb73TcEBP_jsPnEFwgUMrLhzdnaWJxjCJnq8vOietUjFqEBUIKKS2OXHUgYsTwOTWg2GSc6MznPFlQlCpaMgiSUwbWLgRiiVKogMpExSsH4dTLiFasWogG2EE5OkWgmIudFRTI0w0tpDRE0uuGIC6ohONZ2pqt24Y70YZP63g4rMgZM5cLIKnDo6_hzyMum18ZfwpkPjm-AEiDram-KZVdtxnAXChjXO8_KdX4YdosVW97adta86N7toyc7kLucQxvdQrXx9g320oN7L_vj1wNvcBx9J118 |
| 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=Joint+Task+and+Data+Oriented+Semantic+Communications%3A+A+Deep+Separate+Source-channel+Coding+Scheme&rft.jtitle=IEEE+internet+of+things+journal&rft.au=Huang%2C+Jianhao&rft.au=Li%2C+Dongxu&rft.au=Huang%2C+Chuan&rft.au=Qin%2C+Xiaoqi&rft.date=2024-01-15&rft.pub=IEEE&rft.eissn=2327-4662&rft.spage=1&rft.epage=1&rft_id=info:doi/10.1109%2FJIOT.2023.3293154&rft.externalDocID=10175391 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2327-4662&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2327-4662&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2327-4662&client=summon |