# 5G NR Scheduling

The authors propose RRM-GPT, an autoregressive framework that generates radio resource management decisions field by field as a language model generates text: an encoder maps heterogeneous network observations into common tokens and a decoder emits each decision conditioned on the network state and the fields already committed; in a 5G NR case study, a single model generates complete scheduling grants spanning user selection, timing, link adaptation, resource allocation, and control signaling, and transfers learned behavior to an unseen scenario without adaptation.

The authors propose RRM-GPT, an autoregressive framework that generates radio resource management decisions field by field as a language model generates text: an encoder maps heterogeneous network observations into common tokens and a decoder emits each decision conditioned on the network state and the fields already committed; in a 5G NR case study, a single model generates complete scheduling grants spanning user selection, timing, link adaptation, resource allocation, and control signaling, and transfers learned behavior to an unseen scenario without adaptation.