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.

翻译：精确的信道估计对于5G新空口等高性能正交频分复用系统至关重要，尤其是在低信噪比和严苛时延约束条件下。本文提出HELENA——一种紧凑型深度学习模型，该模型将轻量级卷积主干网络与两种高效注意力机制相结合：用于捕获全局依赖的块级多头自注意力机制，以及用于局部特征细化的压缩激励模块。与当前最先进的基于视觉Transformer的估计器CEViT相比，HELENA的推理时间减少45.0\%（0.175毫秒对0.318毫秒），在达到相当精度（$-16.78$分贝对$-17.30$分贝）的同时，参数量仅为前者的八分之一（0.11M对0.88M），充分证明了其在低时延实时部署场景中的适用性。