Attention-Guided Encoder-Decoder Design for Efficient RIS Phase Configuration in Time-Varying IoT Networks
The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs. Reconfigurable Intelligent Surfaces (RIS) offer a promising paradigm for AI-driven control by dynamically adjusting the wireless environment through Phase...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on artificial intelligence S. 1 - 12 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
2025
|
| Schlagworte: | |
| ISSN: | 2691-4581, 2691-4581 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs. Reconfigurable Intelligent Surfaces (RIS) offer a promising paradigm for AI-driven control by dynamically adjusting the wireless environment through Phase Configuration Sequence (PCS) bits sent from the Base Station (BS). By steering signals intelligently, RIS can improve coverage and direct transmissions to specific locations. However, the increasing size of RIS and rapidly changing IoT device positions in dynamic environments impose significant feedback bandwidth constraints due to frequent PCS bits updates. To address this, we propose a novel Attention-Guided Encoder-Decoder with Normalization Enhancement (AGENE) framework that compresses PCS bits at the BS using a lightweight encoder and reconstructs them at the RIS controller using a decoder. Our design incorporates a custom attention mechanism and Generalized Divisive Normalization (GDN)/Inverse GDN layers to enhance feature extraction and normalization. We also evaluate our method under different noise models, including Gaussian and Rician noise, to test robustness in practical scenarios. Finally, to evaluate the effectiveness of our proposed method (AGENE), we compared its performance with existing methods across different compression ratios and noise conditions, focusing on loss reduction and Normalized Mean Square Error (NMSE). In Additive White Gaussian Noise (AWGN) conditions, AGENE achieved a loss reduction of 28.12% compared to PSC-DN, 43.9% compared to DL-CsiNet, and 58.18% compared to CsiNet at a compression ratio of 2/9. Under Rician noise, AGENE showed a reduction of 19.39% compared to PSC-DN, 37.01% compared to DL-CsiNet, and 45.21% compared to CsiNet, again at the same compression ratio. These results consistently demonstrate that AGENE outperforms existing methods by a significant margin across both metrics and noise conditions. |
|---|---|
| AbstractList | The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs. Reconfigurable Intelligent Surfaces (RIS) offer a promising paradigm for AI-driven control by dynamically adjusting the wireless environment through Phase Configuration Sequence (PCS) bits sent from the Base Station (BS). By steering signals intelligently, RIS can improve coverage and direct transmissions to specific locations. However, the increasing size of RIS and rapidly changing IoT device positions in dynamic environments impose significant feedback bandwidth constraints due to frequent PCS bits updates. To address this, we propose a novel Attention-Guided Encoder-Decoder with Normalization Enhancement (AGENE) framework that compresses PCS bits at the BS using a lightweight encoder and reconstructs them at the RIS controller using a decoder. Our design incorporates a custom attention mechanism and Generalized Divisive Normalization (GDN)/Inverse GDN layers to enhance feature extraction and normalization. We also evaluate our method under different noise models, including Gaussian and Rician noise, to test robustness in practical scenarios. Finally, to evaluate the effectiveness of our proposed method (AGENE), we compared its performance with existing methods across different compression ratios and noise conditions, focusing on loss reduction and Normalized Mean Square Error (NMSE). In Additive White Gaussian Noise (AWGN) conditions, AGENE achieved a loss reduction of 28.12% compared to PSC-DN, 43.9% compared to DL-CsiNet, and 58.18% compared to CsiNet at a compression ratio of 2/9. Under Rician noise, AGENE showed a reduction of 19.39% compared to PSC-DN, 37.01% compared to DL-CsiNet, and 45.21% compared to CsiNet, again at the same compression ratio. These results consistently demonstrate that AGENE outperforms existing methods by a significant margin across both metrics and noise conditions. |
| Author | Sahoo, Omm Prakash Bhardwaj, Vikash Kumar Pandey, Om Jee Singh, Abhisekh Shukla, Mahendra K. |
| Author_xml | – sequence: 1 givenname: Vikash Kumar surname: Bhardwaj fullname: Bhardwaj, Vikash Kumar email: vikashkumarbhardwaj.rs.ece22@iitbhu.ac.in organization: Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi, India – sequence: 2 givenname: Abhisekh surname: Singh fullname: Singh, Abhisekh email: abhisheksingh.iiitbh@gmail.com organization: Department of Computer Science and Engineering, IIIT-BH, Bhagalpur, India – sequence: 3 givenname: Omm Prakash surname: Sahoo fullname: Sahoo, Omm Prakash email: ommprakash.sahoo.eee20@iitbhu.ac.in organization: Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi, India – sequence: 4 givenname: Mahendra K. surname: Shukla fullname: Shukla, Mahendra K. email: mahendra@iiitm.ac.in organization: Department of Information Technology at ABV-IIITM Gwalior, India – sequence: 5 givenname: Om Jee surname: Pandey fullname: Pandey, Om Jee email: omjee.ece@iitbhu.ac.in organization: Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi, India |
| BookMark | eNpNkM1OwzAQhC1UJErpnQMHv0CK17Hzc6zaUiJVgCBwjRJnXVyojexUFW9PSivBaWalmdHquyQD6ywScg1sAsDy23JaTDjjchInwOIsPyNDnuQQCZnB4J-_IOMQNoz1UeCcp0OymXYd2s44Gy13psWWLqxyLfpojr9K5xjM2lLtPF1obZTp4_S5eKFP73VAOnNWm_XO14cNaiwtzRajt9p_G7umhSvpA3Z75z_CFTnX9WfA8UlH5PVuUc7uo9XjsphNV5GCWHQRl1IBa0DXbZ6oJmk040I3CjIBNVMib0FK3epMY5NojFMlMM0U9EcuNWPxiLDjrvIuBI-6-vJm2z9UAasOuKoeV3XAVZ1w9ZWbY8Ug4l8cIMmETOMfVUNpPw |
| CODEN | ITAICB |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION |
| DOI | 10.1109/TAI.2025.3610389 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2691-4581 |
| EndPage | 12 |
| ExternalDocumentID | 10_1109_TAI_2025_3610389 11168457 |
| Genre | orig-research |
| GroupedDBID | 0R~ 97E AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS IEDLZ IFIPE JAVBF M~E OCL RIA RIE AAYXX CITATION |
| ID | FETCH-LOGICAL-c134t-255c10b1fad96cb6bf024fbc1841a0c49d155fdf8feb6fe37c4e78c1b6f95f003 |
| IEDL.DBID | RIE |
| ISSN | 2691-4581 |
| IngestDate | Sat Nov 29 07:27:40 EST 2025 Wed Oct 01 07:05:16 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| 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-c134t-255c10b1fad96cb6bf024fbc1841a0c49d155fdf8feb6fe37c4e78c1b6f95f003 |
| PageCount | 12 |
| ParticipantIDs | crossref_primary_10_1109_TAI_2025_3610389 ieee_primary_11168457 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-00-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 2025-00-00 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE transactions on artificial intelligence |
| PublicationTitleAbbrev | TAI |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0002512227 |
| Score | 2.2784297 |
| Snippet | The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs.... |
| SourceID | crossref ieee |
| SourceType | Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Artificial intelligence Bandwidth Base Station (BS) Decoding Internet of Things Internet of Things (IoT) Noise Noise reduction Reconfigurable Intelligent Surface (RIS) Reconfigurable intelligent surfaces Rician channels Training Wireless communication |
| Title | Attention-Guided Encoder-Decoder Design for Efficient RIS Phase Configuration in Time-Varying IoT Networks |
| URI | https://ieeexplore.ieee.org/document/11168457 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE customDbUrl: eissn: 2691-4581 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002512227 issn: 2691-4581 databaseCode: RIE dateStart: 20200101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2691-4581 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002512227 issn: 2691-4581 databaseCode: M~E dateStart: 20200101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV05T8MwFLZoxcBCOYoolzywMLjN6cRjRVOoBFUFpeoW-YQwpChNGPnt2E7KMTAw5ZATWe_56Z3fewBchiL2qcA-UsJnKMCcIq0VXWRapVMaCqxson1xF02n8XJJZg1Y3WJhpJS2-Ez2za3N5YsVr0yobKDlEsdBGLVAK4pwDdb6CqgYRe150SYV6ZDBfDjRDqAX9n1s2oCTX6rnxywVq0rGnX9uYg_sNjYjHNZM3gdbMj8Anc08BtiI5yF4HZZlXb2IbqpMSAGT3EDWCzSS9gpHtl4DakMVJrZ3hF4OHyaPcPaitRk08L_suaoPBcxyaAAiaEELg4WCk9UcTuuq8XUXPI2T-fUtamYpIO76QYm058Bdh7mKCoI5w0xp5awY1w6eSx0eEKENCyVUrCTDSvoRD2QUc1c_kFBp0T8C7XyVy2MAJZaa1k7AsEu1MRYSLCL9r4B6AWVMeD1wtSFz-la3zEitq-GQVLMkNSxJG5b0QNdQ-HtdQ9yTP96fgh3zeR0DOQPtsqjkOdjm72W2Li5A6_4jubDH4hPWlrdl |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV05T8MwFLagIMFCOYoopwcWBrc5HCcZK9rSilJVUKpukU8IQ4rSlN-P7aQcAwNTEsuyovf89M7vPQCuAxH5VBAfKeEzhAmnSGtFF5lW6ZQGgiibaJ-NwvE4ms_jSQVWt1gYKaUtPpMt82pz-WLBVyZU1tZySSIchJtgy4zOquBaXyEVo6o9L1wnI524Pe0MtQvoBS2fmEbg8S_l82OailUm_fo_f2Mf7FVWI-yUbD4AGzI7BPX1RAZYCegReOsURVm_iO5WqZAC9jIDWs9RV9on7NqKDahNVdiz3SP0dvg4fIKTV63PoAEApi-r8lrANIMGIoJmNDdoKDhcTOG4rBtfNsBzvze9HaBqmgLiro8LpH0H7jrMVVTEhDPClFbPinHt4rnU4TgW2rRQQkVKMqKkH3Isw4i7-iMOlBb-Y1DLFpk8AVASqWntYEZcqs2xICYi1Gdh6mHKmPCa4GZN5uS9bJqRWGfDiRPNksSwJKlY0gQNQ-HvfRVxT_9YvwI7g-nDKBkNx_dnYNccVUZEzkGtyFfyAmzzjyJd5pf2cnwCN1S5fQ |
| 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=Attention-Guided+Encoder-Decoder+Design+for+Efficient+RIS+Phase+Configuration+in+Time-Varying+IoT+Networks&rft.jtitle=IEEE+transactions+on+artificial+intelligence&rft.au=Bhardwaj%2C+Vikash+Kumar&rft.au=Singh%2C+Abhisekh&rft.au=Sahoo%2C+Omm+Prakash&rft.au=Shukla%2C+Mahendra+K.&rft.date=2025&rft.issn=2691-4581&rft.eissn=2691-4581&rft.spage=1&rft.epage=12&rft_id=info:doi/10.1109%2FTAI.2025.3610389&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TAI_2025_3610389 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2691-4581&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2691-4581&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2691-4581&client=summon |