LLM-Driven Reward Design for Intelligent RAN Slice Scheduling in Open RAN - Sousa, Pedro; Morte, Frank B.; Oliveira, Andrey; Nahum, Cleverson; Lins, Silvia; Silva, Andrey; Klautau, Aldebaro (Anais do Workshop de Inteligência Artificial para Redes de Computadores (WIARC), 2026)
Radio resource scheduling is a fundamental task in network slicing scenarios, where different services impose heterogeneous performance requirements. This paper proposes the use of large language models (LLMs) to automatically generate reward functions for Reinforcement Learning-based radio resource schedulers in Open Radio Acess Network (RAN) environments. By adapting the reward formulation according to the characteristics and requirements of two use cases, the approach increases the flexibility of the scheduling policy. Simulation results demonstrate the feasibility of the method and indicate that LLM-assisted reward design can support efficient resource allocation with stable throughput while satisfying slice-level requirements.