{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ultra-lite-convolutional-neural-network-for","title":"Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios","arxiv_id":"2208.04659","date":"2022-08-09","proceeding":null,"authors":["Lantu Guo","Yu Wang","Yun Lin","Haitao Zhao","Guan Gui"],"abstract":"Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy. However, most of the DL-based AMC methods have a large number of parameters and high computational complexity, and they cannot be directly applied to low-resource scenarios with limited computing power and storage space. In this letter, we propose a fast AMC method with lightweight and low-complexity using ultra lite convolutional neural network (ULCNN) consisting of data augmentation, complex-valued convolution, separable convolution, channel attention, and channel shuffle. Simulation results demonstrate that our proposed ULCNN-based AMC method achieves an average accuracy of 62.47% on RML2016.10a and only 9,751 parameters. Moreover, ULCNN is verified on a typical edge device (Raspberry Pi), where the interference time per sample is about 0.775 ms. The reproducible code can be downloaded from GitHub\\footnote{https://github.com/BeechburgPieStar/Ultra-Lite-Convolutional-Neural-Network-for-Automatic-Modulation-Classification}.","url_abs":"https://arxiv.org/abs/2208.04659v2","url_pdf":"https://arxiv.org/pdf/2208.04659v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ultra-lite-convolutional-neural-network-for","repo_url":"https://github.com/beechburgpiestar/ultra-lite-convolutional-neural-network-for-automatic-modulation-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}