{"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/helena-high-efficiency-learning-based-channel","title":"HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention","arxiv_id":"2506.13408","date":"2025-06-16","proceeding":null,"authors":["Miguel Camelo Botero","Esra Aycan Beyazıt","Nina Slamnik-Kriještorac","Johann M. Marquez-Barja"],"abstract":"Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement. Compared to CEViT, a state-of-the-art vision transformer-based estimator, HELENA reduces inference time by 45.0\\% (0.175\\,ms vs.\\ 0.318\\,ms), achieves comparable accuracy ($-16.78$\\,dB vs.\\ $-17.30$\\,dB), and requires $8\\times$ fewer parameters (0.11M vs.\\ 0.88M), demonstrating its suitability for low-latency, real-time deployment.","url_abs":"https://arxiv.org/abs/2506.13408v1","url_pdf":"https://arxiv.org/pdf/2506.13408v1.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":"helena-high-efficiency-learning-based-channel","repo_url":"https://github.com/miguelhdo/helena_channel_estimation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}