{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:13:07Z","timestamp":1775239987043,"version":"3.50.1"},"reference-count":197,"publisher":"Emerald","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,25]]},"abstract":"<jats:p>Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these break through sare often difficult to translate into real-world engineering systems because deep learning models require a massive number of training samples, which are costly to obtain in practice. To address labeled data scarcity, few-shot meta-learning optimizes learning algorithms that can efficiently adapt to new tasks quickly. While meta-learning is gaining significant interest in the machine learning literature, its working principles and theoretic fundamentals are not as well understood in the engineering community.<\/jats:p>\n                  <jats:p>This review monograph provides an introduction to metalearning by covering principles, algorithms, theory, and engineering applications. After introducing meta-learning in comparison with conventional and joint learning, we describe the main meta-learning algorithms, as well as a general bilevel optimization framework for the definition of meta-learning techniques. Then, we summarize known results on the generalization capabilities of meta-learning from a statistical learning viewpoint. Applications to communication systems, including decoding and power allocation, are discussed next, followed by an introduction to aspects related to the integration of meta-learning with emerging computing technologies, namely neuromorphic and quantum computing. The monograph is concluded with an overview of open research challenges.<\/jats:p>","DOI":"10.1561\/2000000115","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T06:40:12Z","timestamp":1674628812000},"page":"79-208","source":"Crossref","is-referenced-by-count":28,"title":["Learning with Limited Samples: Meta-Learning and Applications to Communication Systems"],"prefix":"10.1561","volume":"17","author":[{"given":"Lisha","family":"Chen","sequence":"first","affiliation":[{"name":"Rensselaer Polytechnic Institute ,","place":["USA"]}]},{"given":"Sharu Theresa","family":"Jose","sequence":"additional","affiliation":[{"name":"King's College London ,","place":["UK"]}]},{"given":"Ivana","family":"Nikoloska","sequence":"additional","affiliation":[{"name":"King's College London ,","place":["UK"]}]},{"given":"Sangwoo","family":"Park","sequence":"additional","affiliation":[{"name":"King's College London ,","place":["UK"]}]},{"given":"Tianyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute ,","place":["USA"]}]},{"given":"Osvaldo","family":"Simeone","sequence":"additional","affiliation":[{"name":"King's College London ,","place":["UK"]}]}],"member":"140","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"2026040313415135600_ref001","article-title":"Enhancement for Unmanned Aerial Vehicles,","volume-title":"TS 22.289 V17.1.0,","author":"3GPP","year":"2019"},{"key":"2026040313415135600_ref002","article-title":"Study on channel model for frequencies from 0.5 to 100 ghz (3gpp tr 38.901 version 16.1.0 release 16),","volume-title":"TR 38.901,","author":"3GPP"},{"key":"2026040313415135600_ref003","article-title":"Sharp-MAML: Sharpness-aware model-agnostic meta learning,","volume-title":"Proc. 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