{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:29:36Z","timestamp":1763458176739,"version":"3.33.0"},"reference-count":0,"publisher":"International Telecommunication Union","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ITU J-FET"],"published-print":{"date-parts":[[2022,9,22]]},"abstract":"<jats:p>Deep learning methods have been shown to be competitive solutions for modulation classification tasks, but suffer from being computationally expensive, limiting their use on embedded devices. We propose a new deep neural network architecture which employs known structures, depth-wise separable convolution and residual connections, as well as a compression methodology, which combined lead to a tiny and fast algorithm for modulation classification. Our compressed model won the first place in ITU's AI\/ML in 5G Challenge 2021, achieving 61.73\u00d7 compression over the challenge baseline and being over 2.6\u00d7 better than the second best submission. The source code of this work is publicly available at github.com\/ITU-AI- ML-in-5G-Challenge\/ITU-ML5G-PS-007-BacalhauNet.<\/jats:p>","DOI":"10.52953\/fywt4006","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T11:39:07Z","timestamp":1664797147000},"page":"252-260","source":"Crossref","is-referenced-by-count":5,"title":["BacalhauNet: A tiny CNN for lightning-fast modulation classification"],"prefix":"10.52953","volume":"3","author":[{"given":"Jose","family":"Rosa","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Granhao","sequence":"additional","affiliation":[]},{"given":"Guilherme","family":"Carvalho","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Monica","family":"Figueiredo","sequence":"additional","affiliation":[]},{"given":"Luis Conde","family":"Bento","sequence":"additional","affiliation":[]},{"given":"Nuno","family":"Paulino","sequence":"additional","affiliation":[]},{"given":"Luis M.","family":"Pessoa","sequence":"additional","affiliation":[]}],"member":"30637","container-title":["ITU Journal on Future and Evolving Technologies"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T09:52:02Z","timestamp":1737625922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.itu.int\/pub\/S-JNL-VOL3.ISSUE2-2022-A22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,22]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.52953\/fywt4006","relation":{},"ISSN":["2616-8375"],"issn-type":[{"type":"print","value":"2616-8375"}],"subject":[],"published":{"date-parts":[[2022,9,22]]}}}