A new design of channel denoiser using residual autoencoder
A joint neural network decoder and denoiser scheme demonstrated superior performance compared to individual modules. However, there is still a limitation that the existing denoisers cannot effectively learn patterns of encoded signals. To overcome the limitation, a novel denoiser based on a residual...
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| Veröffentlicht in: | Electronics letters 2023-01, Vol.59 (2), p.n/a |
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| creator | Han, Soyoung Kim, Junghyun Song, Hong‐Yeop |
| description | A joint neural network decoder and denoiser scheme demonstrated superior performance compared to individual modules. However, there is still a limitation that the existing denoisers cannot effectively learn patterns of encoded signals. To overcome the limitation, a novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up the training process and boosts the performance due to its structure effectively extracting compressed features. For the evaluation, a joint system model with a hyper‐graph‐network decoder that is known for outstanding decoding performance is considered. Simulation results show that this denoiser outperforms the existing denoisers. Furthermore, the proposed joint model shows significant performance improvement compared to the individual hyper‐graph‐network decoder with only 1% of the number of epochs for the training.
A novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up training process and boosts the performance due to its structure effectively extracting compressed features. Simulation results show that this denoiser outperforms all the existing denoisers. |
| doi_str_mv | 10.1049/ell2.12711 |
| format | Article |
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A novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up training process and boosts the performance due to its structure effectively extracting compressed features. Simulation results show that this denoiser outperforms all the existing denoisers.</description><identifier>ISSN: 0013-5194</identifier><identifier>EISSN: 1350-911X</identifier><identifier>DOI: 10.1049/ell2.12711</identifier><language>eng</language><publisher>Stevenage: John Wiley & Sons, Inc</publisher><subject>Algorithms ; artificial intelligence ; channel coding ; Codes ; Decoding ; Neural networks ; Performance evaluation ; wireless communications</subject><ispartof>Electronics letters, 2023-01, Vol.59 (2), p.n/a</ispartof><rights>2023 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.</rights><rights>2023. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3371-fb2c25f049502c99a71ebcd7c460a4e6b8bbcccb8dd23c58b7e5136c19a88e5b3</citedby><cites>FETCH-LOGICAL-c3371-fb2c25f049502c99a71ebcd7c460a4e6b8bbcccb8dd23c58b7e5136c19a88e5b3</cites><orcidid>0000-0003-0265-5169 ; 0000-0001-8764-9424</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1049%2Fell2.12711$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1049%2Fell2.12711$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,1411,11541,27901,27902,45550,45551,46027,46451</link.rule.ids></links><search><creatorcontrib>Han, Soyoung</creatorcontrib><creatorcontrib>Kim, Junghyun</creatorcontrib><creatorcontrib>Song, Hong‐Yeop</creatorcontrib><title>A new design of channel denoiser using residual autoencoder</title><title>Electronics letters</title><description>A joint neural network decoder and denoiser scheme demonstrated superior performance compared to individual modules. However, there is still a limitation that the existing denoisers cannot effectively learn patterns of encoded signals. To overcome the limitation, a novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up the training process and boosts the performance due to its structure effectively extracting compressed features. For the evaluation, a joint system model with a hyper‐graph‐network decoder that is known for outstanding decoding performance is considered. Simulation results show that this denoiser outperforms the existing denoisers. Furthermore, the proposed joint model shows significant performance improvement compared to the individual hyper‐graph‐network decoder with only 1% of the number of epochs for the training.
A novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up training process and boosts the performance due to its structure effectively extracting compressed features. 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A novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up training process and boosts the performance due to its structure effectively extracting compressed features. Simulation results show that this denoiser outperforms all the existing denoisers.</abstract><cop>Stevenage</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1049/ell2.12711</doi><tpages>3</tpages><orcidid>https://orcid.org/0000-0003-0265-5169</orcidid><orcidid>https://orcid.org/0000-0001-8764-9424</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0013-5194 |
| ispartof | Electronics letters, 2023-01, Vol.59 (2), p.n/a |
| issn | 0013-5194 1350-911X |
| language | eng |
| recordid | cdi_proquest_journals_3092807138 |
| source | Wiley-Blackwell Open Access Titles; DOAJ Directory of Open Access Journals; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Algorithms artificial intelligence channel coding Codes Decoding Neural networks Performance evaluation wireless communications |
| title | A new design of channel denoiser using residual autoencoder |
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