AI RESEARCH
HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
arXiv CS.LG
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ArXi:2506.13408v2 Announce Type: replace-cross 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.