Bridging Semantics and Signals: An LLM-Driven MoE Framework for Radio Map Prediction

Abstract

High-fidelity radio map prediction is essential for wireless network optimization, yet existing single-network models frequently fail to generalize across diverse multi-band and multi-terrain scenarios due to rigid parameter constraints. We introduce LLM-MoE, a framework that employs a divide-and-conquer Mixture-of-Experts strategy to dynamically allocate modeling tasks to matched expert sub-networks. Our approach features a heterogeneous expert pool for characterizing varied physical propagation behaviors, combined with an LLM-driven semantic extractor that provides global physical awareness. A cross-modal fusion module integrates these macroscopic semantics with local spatial features for adaptive routing. Experimental results on the SpectrumNet dataset show that LLM-MoE outperforms top-tier baselines, achieving a 10.0\% reduction in Mean Squared Error (MSE) and 9.7\% in Normalized Mean Squared Error (NMSE), while ensuring robust generalization across heterogeneous environments.