English(EN) HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution

新型AI模型HankelKANet改进无线路径损耗估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了HankelKANet，这是一种新颖的神经网络，旨在实现无线网络中高效且准确的路径损耗估计。该轻量级模型将受物理启发的原理（特别是使用Hankel函数在残差U-Net骨干网络和卷积Kolmogorov-Arnold网络（KAN）中编码距离和衰减特性）整合。与PEFNet等现有方法相比，HankelKANet表现出更优越的性能，参数更少的情况下实现了更高的准确性，并在不同数据集上显示出强大的泛化能力。 AI

影响该模型可能带来更高效、更准确的无线网络规划和优化。

排序理由该集群包含一篇详细介绍新AI模型及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yue Ruan, Xiaoxiao Cheng, Yunan Zhu, Wenlong Huang, Meng Hua · 2026-10-15 04:00

HankelKANet：通过受物理启发的Hankel卷积进行轻量级路径损耗估计

arXiv:2610.19524v1 Announce Type: new Abstract: Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-inspired network for PL estimation that structurally embeds electromagnetic propa…

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报道来源 [1]

HankelKANet：通过受物理启发的Hankel卷积进行轻量级路径损耗估计

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