发表机构
University of Antwerp; imec（安特卫普大学; 比利时微电子研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评估了为地面信道设计的HELENA模型在LEO NTN信道估计中的表现，通过重训练和与多个模型对比，证明其在精度和延迟上优于NTN专用模型，无需重新设计，但嵌入式设备尾部延迟仍是挑战。
AI 中文摘要
基于深度学习（DL）的信道估计在地面5G NR中已展现出高精度和低延迟，但低地球轨道（LEO）非地面网络（NTN）引入了多普勒和同步损伤，可能需要针对NTN的专用架构。我们测试了最初为地面信道设计的使用双神经注意力的高效学习信道估计（HELENA）在NTN重训练后是否仍然有效，并适用于高性能和功耗受限的推理平台。其未改变的架构在成对的接收端补偿（NTN-1）和残余损伤（NTN-2）数据集上训练，并与在同一NTN数据上训练的八个地面来源模型以及NTN专用的MDELAN-SISO进行比较。HELENA在两种条件下均实现了DL估计器中最低的观测信噪比平均NMSE，包括比MDELAN-SISO低55.8-62.7%的线性尺度NMSE。所有DL模型在NTN-2中均出现性能下降，展示了残余多普勒及其相关损伤带来的挑战。在RTX PRO 4500上，HELENA实现了0.0595毫秒的99百分位（P99）推理延迟，比0.5毫秒预算低88.1%，且能耗低于其最接近的基于注意力的竞争对手。在10瓦的Jetson Orin NX上，它保持了良好的精度-能耗权衡，但没有模型满足P99预算。因此，对于所评估的任务，HELENA无需针对NTN的重新设计，而嵌入式尾部延迟仍是一个开放的挑战。
英文摘要
Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Doppler and synchronization impairments that may require NTN-specific architectures. We test whether High-Efficiency Learning-based channel Estimation using dual Neural Attention (HELENA), originally designed for terrestrial channels, remains effective after NTN retraining and suitable across high-performance and power-constrained inference platforms. Its unchanged architecture is trained on paired receiver-compensated (NTN-1) and residual-impaired (NTN-2) datasets and compared with eight terrestrial-origin models trained on the same NTN data and the NTN-specific MDELAN-SISO. HELENA achieves the lowest observed SNR-averaged NMSE among the DL estimators in both conditions, including 55.8-62.7% lower linear-scale NMSE than MDELAN-SISO. All DL models degrade in NTN-2, demonstrating the challenge posed by residual Doppler and its associated impairments. On an RTX PRO 4500, HELENA achieves 0.0595 ms 99th-percentile (P99) inference latency, 88.1% below the 0.5 ms budget, with lower energy than its closest attention-based competitors. On a 10 W Jetson Orin NX, it retains a favorable accuracy-energy trade-off, but no model meets the P99 budget. Thus, HELENA needs no NTN-specific redesign for the evaluated task, while embedded tail latency remains an open challenge.
