{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:51:56Z","timestamp":1776275516628,"version":"3.50.1"},"reference-count":114,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271024"],"award-info":[{"award-number":["62271024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB2902002"],"award-info":[{"award-number":["2022YFB2902002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Open J. Commun. Soc."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/ojcoms.2024.3380512","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T18:58:43Z","timestamp":1711047523000},"page":"1987-2015","source":"Crossref","is-referenced-by-count":13,"title":["Meta-Learning for Wireless Communications: A Survey and a Comparison to GNNs"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5654-5136","authenticated-orcid":false,"given":"Baichuan","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2813-3249","authenticated-orcid":false,"given":"Jiajun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9579-4103","authenticated-orcid":false,"given":"Yang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0058-0765","authenticated-orcid":false,"given":"Chenyang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Beihang University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2022.3210648"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.731"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019956318069"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/6GSUMMIT49458.2020.9083856"},{"key":"ref6","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. ICML","author":"Finn"},{"key":"ref7","article-title":"Modular meta-learning","author":"Alet","year":"2018","journal-title":"arXiv:1806.10166"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3094162"},{"key":"ref9","first-page":"1","article-title":"Gradient-based hyperparameter optimization over long horizons","volume-title":"Proc. NeurIPS","author":"Micaelli"},{"key":"ref10","first-page":"1","article-title":"Bilevel programming for hyperparameter optimization and metalearning","volume-title":"Proc. ICML","author":"Franceschi"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3204763"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3241841"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3035843"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2020.3007198"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3195352"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2022-Fall57202.2022.10012705"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1561\/2000000115"},{"key":"ref18","article-title":"Federated and Meta learning over non-wireless and wireless networks: A tutorial","author":"Liu","year":"2022","journal-title":"arXiv:2210.13111"},{"key":"ref19","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018","journal-title":"arXiv:1806.01261"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.003.21003437"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2023.3305124"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC51071.2022.9771688"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3100133"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2988255"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036965"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3191344"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3045199"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3193138"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3178213"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2023-Fall60731.2023.10333362"},{"key":"ref31","article-title":"Size generalization for resource allocation with graph neural networks","author":"Wu","year":"2023","journal-title":"arXiv:2204.13972"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2023-Spring57618.2023.10200544"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3282220"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2023.3316114"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3292257"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps56602.2022.10008701"},{"key":"ref37","article-title":"Hyper-parameter optimization: A review of algorithms and applications","author":"Yu","year":"2020","journal-title":"arXiv:2003.05689"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1137\/20M134695X"},{"key":"ref39","first-page":"1","article-title":"Learning to learn by gradient descent by gradient descent","volume-title":"Proc. NeurIPS","author":"Andrychowicz"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511624216"},{"key":"ref42","article-title":"L2 regularization versus batch and weight normalization","author":"Laarhoven","year":"2017","journal-title":"arXiv:1706.05350"},{"key":"ref43","first-page":"1","article-title":"How important is the train-validation split in metalearning?","volume-title":"Proc. ICML","author":"Bai"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3134634"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref49","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018","journal-title":"arXiv:1803.02999"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref51","first-page":"1","article-title":"Learning feed-forward one-shot learners","volume-title":"Proc. NeurIPS","author":"Bertinetto"},{"key":"ref52","first-page":"1","article-title":"HyperNetworks","volume-title":"Proc. ICLR","author":"Ha"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.23919\/JCIN.2023.10387243"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC48557.2020.9154283"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref56","article-title":"A brief review of hypernetworks in deep learning","author":"Chauhan","year":"2023","journal-title":"arXiv:2306.06955"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM54140.2023.10437273"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3126064"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3163249"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3030882"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2022.3197158"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3098854"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761319"},{"key":"ref64","article-title":"Study on channel model for frequencies from 0.5 to 100 GHz, version 17.0.0","year":"2022"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3019077"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3204835"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3269643"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3043879"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/EuCNC\/6GSummit58263.2023.10188320"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC.2019.8815537"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/spawc48557.2020.9154322"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053252"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3271993"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413598"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2022.3223066"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3146439"},{"key":"ref77","first-page":"1562","article-title":"Wireless power control via metareinforcement learning","volume-title":"Proc. IEEE ICC","author":"Lu"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3300839"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2023.3332809"},{"key":"ref80","first-page":"1","article-title":"Dynamic channel access via metareinforcement learning","volume-title":"Proc. IEEE GLOBECOM","author":"Lu"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/icassp43922.2022.9746361"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2023.3287240"},{"key":"ref83","article-title":"A meta-learning based gradient descent algorithm for MU-MIMO beamforming","author":"Xia","year":"2022","journal-title":"arXiv:2210.13279"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3168885"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3322766"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3163626"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2021.3128637"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3253126"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2023.3325735"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10278986"},{"key":"ref91","article-title":"Scalable predictive beamforming for IRS-assisted multi-user communications: A deep learning approach","author":"Liu","year":"2022","journal-title":"arXiv:2211.12644"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3313192"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3229948"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ISWCS56560.2022.9940416"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3215666"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3244096"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2022.3158646"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3091551"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3255547"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC55385.2023.10118984"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746240"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746313"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3204486"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3222781"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2024.3354967"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2023.3270361"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2023.3338481"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3284263"},{"key":"ref109","article-title":"Understanding the performance of learning precoding policy with GNN and CNNs","author":"Zhao","year":"2022","journal-title":"arXiv:2211.14775"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2312183"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2147784"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.3390\/e23010126"},{"key":"ref113","first-page":"1","article-title":"Meta-learning by adjusting priors based on extended PAC-Bayes theory","volume-title":"Proc. ICML","author":"Amit"},{"key":"ref114","first-page":"1","article-title":"Bridging the gap between practice and PAC-Bayes theory in fewshot meta-learning","volume-title":"Proc. NeurIPS","author":"Ding"}],"container-title":["IEEE Open Journal of the Communications Society"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8782661\/10362961\/10477590.pdf?arnumber=10477590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T19:13:06Z","timestamp":1712862786000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10477590\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":114,"URL":"https:\/\/doi.org\/10.1109\/ojcoms.2024.3380512","relation":{},"ISSN":["2644-125X"],"issn-type":[{"value":"2644-125X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}