Smith Institute&Spectrivity：2025年人工智能（AI）在频谱管理中的应用研究报告（英文版）

ML-based experimental design can detect license violations, optimize sensor placement for interference management, and flag suspicious activity through anomaly detection.AI-driven dynamic spectrum allocation can predict and mitigate interference while maximizing idle frequency usage. These automated decision-making techniques can improve policymaking and workload efficiency, enhancing signal propagation and spectrum utilization.Although in the long-term, the deployment of digital twins extend