RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models

Excerpt

arXiv:2610.04296v1 Announce Type: cross Abstract: 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