Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with AI-Driven O-RAN: A Multi-Granularity Marketplace Framework

• URL: http://arxiv.org/abs/2502.13891v1
• Date: Wed, 19 Feb 2025 17:21:10 GMT
• Title: Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with AI-Driven O-RAN: A Multi-Granularity Marketplace Framework
• Authors: Mehdi Rasti, Elaheh Ataeebojd, Shiva Kazemi Taskooh, Mehdi Monemi, Siavash Razmi, Matti Latva-aho,
• Abstract summary: Current spectrum-sharing frameworks struggle with adaptability, often being either static or insufficiently dynamic.<n>We propose an adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture, integrating discriminative and generative AI (AI) to forecast spectrum needs across multiple timescales and spatial granularities.
• Score: 8.664317254613792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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