用AI驱动的O-RAN框架实现多粒度频谱动态交易，提升利用效率。
Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with AI-Driven O-RAN: A Multi-Granularity Marketplace Framework
- 融合判别与生成式AI预测多时空尺度频谱需求
- 通过市场机制实现运营商间频谱实时交易，利用率显著提升
- 模块化设计适合多方协作，适用于5G/6G网络频谱管理
现有频谱共享框架适应性差，多为静态或动态不足，且主要关注时间维度而忽略空间与频谱维度。本文提出一种基于O-RAN架构的自适应AI驱动频谱共享框架，结合判别与生成式AI（GenAI），在多个时间尺度和空间粒度上预测频谱需求。由授权频谱经纪人管理的市场模型，使运营商可动态交易频谱，平衡静态分配与实时交易。GenAI提升流量预测、频谱估算与分配效率，优化资源利用并降低运营成本。该模块化、灵活的方案促进运营商协作，最大化效率与收益。核心研究挑战在于突破现有模型对分配粒度与时空动态性的限制。
原文摘要 · Abstract (English)
Current spectrum-sharing frameworks struggle with adaptability, often being either static or insufficiently dynamic. They primarily emphasize temporal sharing while overlooking spatial and spectral dimensions. We propose an adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture, integrating discriminative and generative AI (GenAI) to forecast spectrum needs across multiple timescales and spatial granularities. A marketplace model, managed by an authorized spectrum broker, enables operators to trade spectrum dynamically, balancing static assignments with real-time trading. GenAI enhances traffic prediction, spectrum estimation, and allocation, optimizing utilization while reducing costs. This modular, flexible approach fosters operator collaboration, maximizing efficiency and revenue. A key research challenge is refining allocation granularity and spatio-temporal dynamics beyond existing models.
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