A Learning-Aided Flexible Gradient Descent Approach to MISO Beamforming
This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iteratio...
Saved in:
| Published in | IEEE wireless communications letters Vol. 11; no. 9; pp. 1895 - 1899 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-2337 2162-2345 2162-2345 |
| DOI | 10.1109/LWC.2022.3186160 |
Cover
| Abstract | This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD . |
|---|---|
| AbstractList | This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD . |
| Author | Yang, Zhixiong Luo, Junshan Gunduz, Deniz Xia, Jing-Yuan Zhang, Shuanghui |
| Author_xml | – sequence: 1 givenname: Zhixiong orcidid: 0000-0002-2667-3903 surname: Yang fullname: Yang, Zhixiong email: yzx21@nudt.edu.cn organization: College of Electronic Engineering, National University of Defense Technology, Changsha, China – sequence: 2 givenname: Jing-Yuan orcidid: 0000-0003-4329-0354 surname: Xia fullname: Xia, Jing-Yuan email: j.xia16@imperial.ac.uk organization: College of Electronic Engineering, National University of Defense Technology, Changsha, China – sequence: 3 givenname: Junshan surname: Luo fullname: Luo, Junshan email: luojunshan10@nudt.edu.cn organization: College of Electronic Engineering, National University of Defense Technology, Changsha, China – sequence: 4 givenname: Shuanghui orcidid: 0000-0002-7496-5433 surname: Zhang fullname: Zhang, Shuanghui email: shzhang3@126.com organization: College of Electronic Engineering, National University of Defense Technology, Changsha, China – sequence: 5 givenname: Deniz orcidid: 0000-0002-7725-395X surname: Gunduz fullname: Gunduz, Deniz email: d.gunduz@imperial.ac.uk organization: Department of Electrical and Electronic Engineering, Imperial College London, London, U.K |
| BookMark | eNptkL1PwzAUxC1UJErpjsQSiTnFH03ijKHQUimoAyBGy3GewVXqBCdV6X-Pq1QdKt5yb7jf6XTXaGBrCwjdEjwhBKcP-edsQjGlE0Z4TGJ8gYaUxDSkbBoNTj9LrtC4bdfYX4wJJXyIFlmQg3TW2K8wMyWUwbyCX1NUECycLA3YLniCVh00axpXS_UddHXwunxbBY8gN7p2Gw_foEstqxbGRx2hj_nz--wlzFeL5SzLQ8UY60IqI445xyRlKWgsZakV0xpkoVPtC8ZRATKeMlVwHjHOirSUqZoSXQCBBGs2QqTP3dpG7neyqkTjzEa6vSBYHMYQ1U6JwxjiOIZn7nvG1__ZQtuJdb111tcUNCEURzHjqXfFvUu5um0daKFMJztT285JU53i_dbn8fgMPG_0D3LXIwYATvaU4yhJGPsDD9SIjw |
| CODEN | IWCLAF |
| CitedBy_id | crossref_primary_10_1109_TPAMI_2024_3400041 crossref_primary_10_1007_s00034_024_02976_9 crossref_primary_10_1016_j_phycom_2024_102429 crossref_primary_10_1109_ACCESS_2024_3406527 crossref_primary_10_1109_LWC_2024_3436576 crossref_primary_10_1109_LWC_2023_3329036 crossref_primary_10_1109_TCSVT_2023_3318401 crossref_primary_10_1109_TWC_2023_3326091 crossref_primary_10_1109_TSP_2023_3238275 crossref_primary_10_1109_TWC_2022_3220784 |
| Cites_doi | 10.1109/COMST.2017.2750201 10.1109/TWC.2021.3071480 10.1109/ICASSP39728.2021.9414561 10.1109/ACCESS.2018.2887308 10.1109/JSAC.2019.2929380 10.1109/T-WC.2008.070851 10.1109/TCOMM.2019.2960361 10.1109/LGRS.2018.2866567 10.1109/VTCFall.2013.6692147 10.1109/LGRS.2022.3184311 10.1109/TCOMM.2004.840638 10.1109/TSP.2018.2866382 10.1109/TWC.2021.3111843 10.1109/JSEN.2020.3025053 10.1109/ACSSC.2009.5470055 10.1109/TSP.2011.2147784 10.1109/LWC.2020.3007198 10.1109/TNNLS.2022.3165627 10.1109/JSAC.2010.101206 10.1109/TSP.2020.3021257 10.1109/JSTSP.2007.914876 10.1109/ISIT45174.2021.9518251 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 8FD L7M ADTOC UNPAY |
| DOI | 10.1109/LWC.2022.3186160 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2162-2345 |
| EndPage | 1899 |
| ExternalDocumentID | oai:iris.unimore.it:11380/1286025 10_1109_LWC_2022_3186160 9805773 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: European Research Council through Project BEACON grantid: 677854 funderid: 10.13039/501100000781 – fundername: National Natural Science Foundation of China grantid: 62171448; 61921001; 62131020; 62022091 funderid: 10.13039/501100001809 – fundername: CHIST-ERA grantid: CHISTERA-18-SDCDN-001; EPSRC-EP/T023600/1 funderid: 10.13039/501100001942 – fundername: Natural Science Fund for Young Talents of Hunan Province (NSFYT) of Hunan grantid: 2020RC3029 funderid: 10.13039/501100004735 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IES IFIPE IPLJI JAVBF M43 OCL RIA RIE RNS AAYXX CITATION 7SP 8FD L7M ADTOC UNPAY |
| ID | FETCH-LOGICAL-c333t-2a5808801939ef0aadfc3ffeabf9f33765bea643cb885383b9da9c41fbe1e70f3 |
| IEDL.DBID | RIE |
| ISSN | 2162-2337 2162-2345 |
| IngestDate | Sun Oct 26 03:50:49 EDT 2025 Mon Jun 30 05:51:22 EDT 2025 Wed Oct 01 02:44:34 EDT 2025 Thu Apr 24 22:59:49 EDT 2025 Wed Aug 27 02:14:24 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| 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 cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c333t-2a5808801939ef0aadfc3ffeabf9f33765bea643cb885383b9da9c41fbe1e70f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-2667-3903 0000-0003-4329-0354 0000-0002-7496-5433 0000-0002-7725-395X |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://hdl.handle.net/11380/1286025 |
| PQID | 2712056389 |
| PQPubID | 2040496 |
| PageCount | 5 |
| ParticipantIDs | crossref_citationtrail_10_1109_LWC_2022_3186160 proquest_journals_2712056389 ieee_primary_9805773 crossref_primary_10_1109_LWC_2022_3186160 unpaywall_primary_10_1109_lwc_2022_3186160 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-09-01 |
| PublicationDateYYYYMMDD | 2022-09-01 |
| PublicationDate_xml | – month: 09 year: 2022 text: 2022-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE wireless communications letters |
| PublicationTitleAbbrev | LWC |
| PublicationYear | 2022 |
| 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 ref12 ref23 ref15 ref14 ref20 ref11 ref10 ref21 Kingma (ref22) 2014 ref2 ref1 ref17 ref16 ref19 ref18 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref2 doi: 10.1109/COMST.2017.2750201 – ident: ref17 doi: 10.1109/TWC.2021.3071480 – ident: ref16 doi: 10.1109/ICASSP39728.2021.9414561 – ident: ref20 doi: 10.1109/ACCESS.2018.2887308 – ident: ref9 doi: 10.1109/JSAC.2019.2929380 – volume-title: arXiv:1412.6980 year: 2014 ident: ref22 article-title: Adam: A method for stochastic optimization – ident: ref6 doi: 10.1109/T-WC.2008.070851 – ident: ref12 doi: 10.1109/TCOMM.2019.2960361 – ident: ref18 doi: 10.1109/LGRS.2018.2866567 – ident: ref5 doi: 10.1109/VTCFall.2013.6692147 – ident: ref21 doi: 10.1109/LGRS.2022.3184311 – ident: ref3 doi: 10.1109/TCOMM.2004.840638 – ident: ref11 doi: 10.1109/TSP.2018.2866382 – ident: ref15 doi: 10.1109/TWC.2021.3111843 – ident: ref19 doi: 10.1109/JSEN.2020.3025053 – ident: ref7 doi: 10.1109/ACSSC.2009.5470055 – ident: ref8 doi: 10.1109/TSP.2011.2147784 – ident: ref13 doi: 10.1109/LWC.2020.3007198 – ident: ref23 doi: 10.1109/TNNLS.2022.3165627 – ident: ref4 doi: 10.1109/JSAC.2010.101206 – ident: ref10 doi: 10.1109/TSP.2020.3021257 – ident: ref1 doi: 10.1109/JSTSP.2007.914876 – ident: ref14 doi: 10.1109/ISIT45174.2021.9518251 |
| SSID | ssj0000601218 |
| Score | 2.4228287 |
| Snippet | This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input... |
| SourceID | unpaywall proquest crossref ieee |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1895 |
| SubjectTerms | Algorithms Array signal processing Beamforming Computational complexity Heuristic algorithms implicit gradient descent Iterative methods Iterative solution Machine learning Multi-user MISO downlink Neural networks Optimization Signal processing Signal to noise ratio Training unsupervised learning |
| SummonAdditionalLinks | – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3LT9wwEIdHdDm0HPqiiKUU-dBLK4WN441jH1PoQqtCK7Wr0lPkJ0KEBUFWqP3rO06cLQ8JqbdEsuXIM_b8rIy_AXjrMKZJFAaJVYVPQolCXFLCJmzsjXXcGtViFw8O-f50_PkoP1qCvuDdHbwApUykI9xCOYbmR7DMcxTcA1ieHn4rf4WycZRnScZaMmZ8Huf9z8hUjurrQCnMMjyZCk5bDOW_4NNWU7klLB_PZxfq97Wq6xsxZvIMdvubOl1qyen2vNHb5s99cONDn_8cnkaNScrOKV7Akpu9hJUb5MFV2CtJJKseJ-WJdZZMAhlT147sXbZZYA3Z7UhPpIzYcdKck4NP37-SD06dBa2LnV_BdPLxx85-EmsqJIYx1iSZygVuLCjsmHQ-Vcp6w7x3SnvpcSJ5rp1ClWK0wEAumJZWSTOmXjvqitSzNRjMzmduHYjUXGqtuaMCjxlGCWUKq1KVo4q0lPkhjPrJrkwEjoe6F3XVHjxSWX35uVMF81TRPEN4t-hx0cE2Hmi7Guy3aCcFKs-CDWGzt2cVV-NVlRU0Q6GH2mwI7xc2vjcEesutITb-p_FreBJeu_SzTRg0l3P3BvVKo7eiv_4FkDfgng priority: 102 providerName: Unpaywall |
| Title | A Learning-Aided Flexible Gradient Descent Approach to MISO Beamforming |
| URI | https://ieeexplore.ieee.org/document/9805773 https://www.proquest.com/docview/2712056389 http://hdl.handle.net/11380/1286025 |
| UnpaywallVersion | submittedVersion |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2162-2345 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000601218 issn: 2162-2345 databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB615QAceJWKLaXygQuI7Cb2bmIfQ2FbEFuQYEU5RX6MESLsVm1WFfx6xk42tIAQtxzGmmTGznxjj78BeIwU0xQBg8TpwiehRSEtKekSMfbWYe6sjrSLs-P8aD5-fTI52YBn_V0YRIzFZzgMj_Es3y3tKmyVjZQkdFGITdgsZN7e1er3UwKvCI_beTzLecKFKNankqkavfl4QLkg55SiyjyLfJS_olBsq3IFYV5fLU719wtd15eCzfQ2zNav2daYfB2uGjO0P35jcPzf77gDtzrUycp2mtyFDVzcg5uXuAi34bBkHdfq56T84tCxaeDKNDWyw7NYF9awFy33Eys7InLWLNns1fu37DnqbwH90uD7MJ--_HBwlHRdFhIrhGgSrieSfjUE9YRCn2rtvBXeozZeebJiPjGoCbdYIym0S2GU08qOM28wwyL1Yge2FssFPgCmTK6MMTlmkhIPq6W2hdOpnhCudJnwAxitrV7ZjoI8dMKoq5iKpKoiP1XBT1XnpwE86UectvQb_5DdDqbu5TorD2Bv7diqW5_nFS8yTtCP0NoAnvbO_kNFfWGvqNj9u4qHcCNItaVne7DVnK3wEWGVxuzHSboP1-bH78pPPwHj0OTP |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VcigceJWKLQV84AIiu4mdl49LYbuF3XKgFb1FfowRIuxWbVYV_HrGTja0gBC3HDyaZMbOfGOPvwF4jhTTJAGDyKrCRb5FIS2p0kYidcZibo0KtIvzo3x6kr47zU434FV_FwYRQ_EZDv1jOMu3S7PyW2UjWRK6KMQNuJmlaZq1t7X6HRXPLMLDhh5Pch5xIYr1uWQsR7NP-5QNck5JapkngZHyVxwKjVWuYcyt1eJMfb9UdX0l3Ezuwnz9om2VydfhqtFD8-M3Dsf__ZJ7cKfDnWzcTpT7sIGLB3D7ChvhNhyMWce2-jkaf7Fo2cSzZeoa2cF5qAxr2JuW_YmNOypy1izZ_PDjB_Ya1TePf0n4IZxM3h7vT6Ouz0JkhBBNxFVW0s-GwJ6Q6GKlrDPCOVTaSUdWzDONipCL0SUF91JoaZU0aeI0JljETuzA5mK5wEfApM6l1jrHpKTUw6hSmcKqWGWELG0i3ABGa6tXpiMh970w6iokI7GsyE-V91PV-WkAL3qJs5aA4x9jt72p-3GdlQewt3Zs1a3Qi4oXCSfwR3htAC97Z_-hor4011Ts_l3FM9iaHs9n1ezw6P1juOUl2kK0Pdhszlf4hJBLo5-GCfsT0oXmbA |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3LT9wwEIdHdDm0HPqiiKUU-dBLK4WN441jH1PoQqtCK7Wr0lPkJ0KEBUFWqP3rO06cLQ8JqbdEsuXIM_b8rIy_AXjrMKZJFAaJVYVPQolCXFLCJmzsjXXcGtViFw8O-f50_PkoP1qCvuDdHbwApUykI9xCOYbmR7DMcxTcA1ieHn4rf4WycZRnScZaMmZ8Huf9z8hUjurrQCnMMjyZCk5bDOW_4NNWU7klLB_PZxfq97Wq6xsxZvIMdvubOl1qyen2vNHb5s99cONDn_8cnkaNScrOKV7Akpu9hJUb5MFV2CtJJKseJ-WJdZZMAhlT147sXbZZYA3Z7UhPpIzYcdKck4NP37-SD06dBa2LnV_BdPLxx85-EmsqJIYx1iSZygVuLCjsmHQ-Vcp6w7x3SnvpcSJ5rp1ClWK0wEAumJZWSTOmXjvqitSzNRjMzmduHYjUXGqtuaMCjxlGCWUKq1KVo4q0lPkhjPrJrkwEjoe6F3XVHjxSWX35uVMF81TRPEN4t-hx0cE2Hmi7Guy3aCcFKs-CDWGzt2cVV-NVlRU0Q6GH2mwI7xc2vjcEesutITb-p_FreBJeu_SzTRg0l3P3BvVKo7eiv_4FkDfgng |
| 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=A+Learning-Aided+Flexible+Gradient+Descent+Approach+to+MISO+Beamforming&rft.jtitle=IEEE+wireless+communications+letters&rft.au=Yang%2C+Zhixiong&rft.au=Xia%2C+Jing-Yuan&rft.au=Luo%2C+Junshan&rft.au=Zhang%2C+Shuanghui&rft.date=2022-09-01&rft.pub=IEEE&rft.issn=2162-2337&rft.volume=11&rft.issue=9&rft.spage=1895&rft.epage=1899&rft_id=info:doi/10.1109%2FLWC.2022.3186160&rft.externalDocID=9805773 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-2337&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-2337&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-2337&client=summon |