{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:26:08Z","timestamp":1778603168774,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Incheon National University Research Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the remarkable advances in vertical-cavity surface-emitting lasers (VCSELs) in recent decades, VCSELs have been considered promising light sources in the field of optical wireless communications. However, off-the-shelf VCSELs still have a limited modulation bandwidth to meet the multi-Gb\/s data rate requirements imposed on the next-generation wireless communication system. Recently, employing machine learning (ML) techniques as a method to tackle such issues has been intriguing for researchers in wireless communication. In this work, through a systematic analysis, it is shown that the ML technique is also very effective in VCSEL-based visible light communication. Using a commercial VCSEL and bidirectional long short-term memory (Bi-LSTM)-based ML scheme, a high-speed visible light communication (VLC) link with a data rate of 13.5 Gbps is demonstrated, which is the fastest single channel result from a cost-effective, off-the-shelf VCSEL device, to the best of the authors\u2019 knowledge.<\/jats:p>","DOI":"10.3390\/s22114145","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link"],"prefix":"10.3390","volume":"22","author":[{"given":"Seoyeon","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minseok","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seonghyeon","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0106-7106","authenticated-orcid":false,"given":"Song","family":"Noh","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3907-4862","authenticated-orcid":false,"given":"Hyunchae","family":"Chun","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"ref_1","first-page":"97","article-title":"Comparison of recent machine learning techniques for gender recognition from facial images","volume":"10","author":"Andonie","year":"2018","journal-title":"MAICS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.procs.2019.01.007","article-title":"Performance of machine learning techniques in the detection of financial frauds","volume":"148","author":"Sadgali","year":"2019","journal-title":"Procedia Comput. 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