HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908968613314560 |
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| author | Botero, Miguel Camelo Beyazit, Esra Aycan Slamnik-Kriještorac, Nina Marquez-Barja, Johann M. |
| author_facet | Botero, Miguel Camelo Beyazit, Esra Aycan Slamnik-Kriještorac, Nina Marquez-Barja, Johann M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention Botero, Miguel Camelo Beyazit, Esra Aycan Slamnik-Kriještorac, Nina Marquez-Barja, Johann M. Signal Processing Machine Learning Networking and Internet Architecture 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. |
| title | HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention |
| topic | Signal Processing Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2506.13408 |