Contrastive Learning in Wireless Communications: A Short Overiew

Contrastive Learning in Wireless Communications: A Short Overiew | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 September 2025 V1 Latest version Share on Contrastive Learning in Wireless Communications: A Short Overiew Author : Martin Smith 0009-0003-0571-6876 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175736063.39864395/v1 197 views 128 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Contrastive learning, a form of self-supervised representation learning, has emerged as a powerful technique in machine learning. This survey provides a comprehensive overview of how contrastive learning is being applied to wireless communications, bridging theoretical foundations with practical applications. We cover semantic communication systems-where contrastive learning improves the transmission of meaning over raw data-as well as physical-layer tasks such as channel estimation, modulation classification, and MIMO beamforming. Recent advances demonstrate that contrastive approaches can leverage unlabeled or weakly-labeled data to boost performance in tasks ranging from wireless image transmission to robust beamforming under imperfect channel knowledge. We identify key trends, including the integration of contrastive objectives for task-oriented and privacy-aware communications, and discuss challenges like designing appropriate augmentations for wireless signals. Finally, we outline future research directions for applying contrastive learning in next-generation wireless networks, aiming for more efficient, intelligent, and secure communication systems Supplementary Material File (contrastive learning in wireless communications a short overiew.pdf) Download 92.05 KB Information & Authors Information Version history V1 Version 1 08 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords channel estimation communications contrastive learning mimo self-supervised learning semantic communication surveys wireless networks Authors Affiliations Martin Smith 0009-0003-0571-6876 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 197 views 128 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Martin Smith. Contrastive Learning in Wireless Communications: A Short Overiew. Authorea . 08 September 2025. DOI: https://doi.org/10.22541/au.175736063.39864395/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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