RadioKMoE: Knowledge-Guided Radiomap Estimation with Kolmogorov-Arnold Networks and Mixture-of-Experts

TL;DR

RadioKMoE introduces a novel framework combining Kolmogorov-Arnold Networks and Mixture-of-Experts to improve the accuracy and robustness of radiomap estimation by integrating physics-based modeling with expert specialization.

Contribution

This work presents the first integration of KAN and MoE for radiomap estimation, enhancing accuracy by leveraging physics models and expert specialization.

Findings

Improved radiomap estimation accuracy in experiments.

Enhanced robustness against environmental variations.

Effective combination of physics-based and data-driven methods.

Abstract

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.