HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution

Key points

• This paper proposes HankelKANet, a lightweight physics-inspired network for PL estimation that structurally embeds electromagnetic propagation characteristics into the neural architecture.1
• Built on a single residual UNet backbone, the proposed model integrates urban layout features with a convolutional Kolmogorov-Arnold network(KAN).1
• Experimental results demonstrate that HankelKANet achieves state-of-the-art accuracy and efficiency on both the simulated RadioMapSeer dataset and the real-world Huawei RSRPSet.1
• Using discrete Dirichlet energy (DDE) and Jacobian sensitivity heatmaps, we empirically verify that this physical encoding intrinsically constrains the network's spatial derivatives.1

Sources (1)

• [1]HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:42 AM

  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 propagation characteristics into the neural architecture.

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