RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models
Learning-based models for radio resource management (RRM) are typically built for a single function and deployment, so each new setting repeats the development pipeline. RRM decisions, however, share a common structure: each is assembled from interdependent fields, defined by the standard, whose values are selected in view of the network state. We propose RRM-GPT, an autoregressive framework for RRM foundation models that generate these decisions as a language model generates text. An encoder maps heterogeneous network observations into a common token representation, and a decoder emits the decision one field at a time, each conditioned on the network state and the fields already committe...
| Authors | Ahmed Aboulfotouh, Akram Bin Sediq, Koosha Pourtahmasi Roshandeh et al. |
|---|---|
| Primary category | eess.SP |
| Published | 3 October 2026 |
| Last updated at source | 14 October 2026 |
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