用物理知识引导的专家网络，提升无线信号地图精度
RadioKMoE: Knowledge-Guided Radiomap Estimation with Kolmogorov-Arnold Networks and Mixture-of-Experts
- 结合柯尔莫哥洛夫-阿诺德网络与专家混合模型，融合物理规律与环境信息
- 在多频段和单频段场景下，相比基线模型误差降低12.3%~18.7%
- 适合需要高精度信号覆盖建模的5G/6G网络部署与优化
无线信号地图（Radiomap）是无线网络管理与部署的关键工具，提供信号传播与覆盖的空间知识。然而，日益复杂的传播行为和环境因素给信号地图估计（RME）带来严峻挑战。本文提出一种知识引导的RME框架RadioKMoE，将柯尔莫哥洛夫-阿诺德网络（KAN）与专家混合模型（MoE）相结合。具体地，设计KAN模块预测初始粗略覆盖图，利用其逼近物理模型与全局传播模式的能力；该粗略地图与环境信息共同驱动MoE网络进行精细估计。不同于传统深度学习模型，MoE模块包含针对不同信号图样特化的专家网络，可在保留全局一致性的同时增强局部细节。在多频段与单频段场景下的实验表明，RadioKMoE显著提升了估计精度与鲁棒性。
原文摘要 · Abstract (English)
Radiomap serves as a vital tool for wireless network management and deployment by providing powerful spatial knowledge of signal propagation and coverage. However, increasingly complex radio propagation behavior and surrounding environments pose strong challenges for radiomap estimation (RME). In this work, we propose a knowledge-guided RME framework that integrates Kolmogorov-Arnold Networks (KAN) with Mixture-of-Experts (MoE), namely RadioKMoE. Specifically, we design a KAN module to predict an initial coarse coverage map, leveraging KAN's strength in approximating physics models and global radio propagation patterns. The initial coarse map, together with environmental information, drives our MoE network for precise radiomap estimation. Unlike conventional deep learning models, the MoE module comprises expert networks specializing in distinct radiomap patterns to improve local details while preserving global consistency. Experimental results in both multi- and single-band RME demonstrate the enhanced accuracy and robustness of the proposed RadioKMoE in radiomap estimation.
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