HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention

--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 Ccurate estimation of Channel State Information (CSI) is crucial for the effectiveness of Orthogonal Frequency-Division Multiplexing (OFDM)-based wireless communication systems, such as 5G New Radio (5G-NR), as it enables optimal resource allocation, beamforming, and adaptive modulation, all of which directly impact system capacity and reliability. In this context, Channel Estimation (CE) refers to the process of acquiring or predicting CSI using received signals and known reference signals (e.g., pilot symbols).