Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information
We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, w...
Uloženo v:
| Vydáno v: | 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) s. 139 - 144 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
05.05.2024
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional separation-based approaches in the practical finite blocklength regimes, and to provide graceful degradation with channel quality. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side. Our results demonstrate that the proposed method succeeds in integrating the side information, yielding improved performance at all channel conditions in terms of the various quality measures considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We have made the source code of the proposed method public to enable further research, and the reproducibility of the results. |
|---|---|
| AbstractList | We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional separation-based approaches in the practical finite blocklength regimes, and to provide graceful degradation with channel quality. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side. Our results demonstrate that the proposed method succeeds in integrating the side information, yielding improved performance at all channel conditions in terms of the various quality measures considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We have made the source code of the proposed method public to enable further research, and the reproducibility of the results. |
| Author | Gunduz, Deniz Yilmaz, Selim F. Erkip, Elza Ozyilkan, Ezgi |
| Author_xml | – sequence: 1 givenname: Selim F. surname: Yilmaz fullname: Yilmaz, Selim F. email: s.yilmaz21@imperial.ac.uk organization: Imperial College,Department of Electrical and Electronic Engineering,London,UK – sequence: 2 givenname: Ezgi surname: Ozyilkan fullname: Ozyilkan, Ezgi email: ezgi.ozyilkan@nyu.edu organization: New York University,Department of Electrical and Computer Engineering,USA – sequence: 3 givenname: Deniz surname: Gunduz fullname: Gunduz, Deniz email: d.gunduz@imperial.ac.uk organization: Imperial College,Department of Electrical and Electronic Engineering,London,UK – sequence: 4 givenname: Elza surname: Erkip fullname: Erkip, Elza email: elza@nyu.edu organization: New York University,Department of Electrical and Computer Engineering,USA |
| BookMark | eNo1j71OwzAYAI0EA5S-AYN5gITPdmzHI3L5CQpkKMyVE3-mllK7SlOhvj1IwHTL6aS7IucpJyTklkHJGJi7xr629k0aqE3JgVclA8UlZ9UZWRptaiFBVIIZc0m6VTzMU-yPM3q6QtzTlxzTTNf5OA1Y2K1LCUdqs4_pk37FeftjDdnjVHRpPNF19EibFPK0c3PM6ZpcBDcecPnHBfl4fHi3z0XbPTX2vi0i02oucAAuA-e6h571A-rgRMWdBnABHXgpA2iUXnkYuPBKehN6Z2qjnEamgliQm99uRMTNfoo7N502_5_iGxWjTnc |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICMLCN59089.2024.10625214 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings Accès Toulouse INP et ENVT - IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) - NZ IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798350343199 |
| EndPage | 144 |
| ExternalDocumentID | 10625214 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: CHIST-ERA funderid: 10.13039/501100001942 |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i176t-ec025f227b0b1bce7fa342a700afea0d55f07e5d6d0c23d65d9fba9896a7e16f3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001307813600025&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Thu May 29 05:57:37 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i176t-ec025f227b0b1bce7fa342a700afea0d55f07e5d6d0c23d65d9fba9896a7e16f3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10625214 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-May-5 |
| PublicationDateYYYYMMDD | 2024-05-05 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-May-5 day: 05 |
| PublicationDecade | 2020 |
| PublicationTitle | 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) |
| PublicationTitleAbbrev | ICMLCN |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.9264351 |
| Snippet | We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 139 |
| SubjectTerms | deep learning Image communication Joint source-channel coding multi-view learning Receivers Robustness Source coding Time-varying channels Wireless communication wireless image transmission Wireless sensor networks Wyner-Ziv coding |
| Title | Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information |
| URI | https://ieeexplore.ieee.org/document/10625214 |
| WOSCitedRecordID | wos001307813600025&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 | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3fS8MwEMeDDhGfVJz4mwi-ZqZJmrTPnUNlboMp7G0kzQUKox2zE_zvbbJO8cEH30K4ELgjJFzuc1-E7nQTZceBEsWNJCLmiqTCGBIJA8JJy5IkgMJDNRols1k6aWH1wMIAQCg-g54fhr98W-VrnyprTnjzWmdetnpXKbmBtfbRbds38_4pexlmIy_i7QkUJnpb-1_KKeHiGBz-c8sj1P1B8PDk-3I5RjtQnqBx37e59QpVYHEfYImfq6Ks8TRk4IknBUpY4Kzya7BPsTZWHlpfkXG5-MTTwgJuASQfkC56Gzy8Zo-kVUQgRaRkTSBvniiOMWWoiUwOymkumFaUagea2jh2VEFspaU541bGNnVGp0kqtYJIOn6KOmVVwhnCxuRGc0O1dlJoDjoSKtHagOOMcqHOUdd7Y77cNL2Ybx1x8cf8JTrwPg-1gPEV6tSrNVyjvfyjLt5XNyFUX1Nll4Y |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3fS8MwEMeDTFGfVJz42wi-ZqZJ2rTPnWPTrhtswt5G0lxgMNoxO8H_3qarig8--BZCQuCOkONyn_si9KAqL1sOlEiuAyJ8LkkktCae0CBsYFgY1qBwItM0nM2icQOr1ywMANTFZ9Bxw_ov3xTZxqXKqhteRevMyVbv-kIwusW19tF90znzcRAPkzh1Mt6OQWGi87Xjl3ZK_XT0jv556DFq_0B4ePz9vJygHchP0ajrGt06jSowuAuwws_FIi_xpM7BE8cK5LDEceH2YJdkrVY5bH1NRvnyA08WBnCDIDmXtNFr72ka90mjiUAWngxKAlkVpFjGpKba0xlIq7hgSlKqLChqfN9SCb4JDM0YN4FvIqtVFEaBkuAFlp-hVl7kcI6w1plWXFOlbCAUB-UJGSqlwXJGuZAXqO2sMV9t217Mvwxx-cf8HTroT4fJPBmkL1fo0Nm_rgz0r1GrXG_gBu1l7-XibX1bu-0TYzqazQ |
| 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%3Abook&rft.genre=proceeding&rft.title=2024+IEEE+International+Conference+on+Machine+Learning+for+Communication+and+Networking+%28ICMLCN%29&rft.atitle=Distributed+Deep+Joint+Source-Channel+Coding+with+Decoder-Only+Side+Information&rft.au=Yilmaz%2C+Selim+F.&rft.au=Ozyilkan%2C+Ezgi&rft.au=Gunduz%2C+Deniz&rft.au=Erkip%2C+Elza&rft.date=2024-05-05&rft.pub=IEEE&rft.spage=139&rft.epage=144&rft_id=info:doi/10.1109%2FICMLCN59089.2024.10625214&rft.externalDocID=10625214 |