Generalization of probabilistic scheduling models for realizing URLLC applications
Suyong Eum, Shin’ichi Arakawa, Masayuki Murata · 2022
In this paper, we mathematically generalize probabilistic Grant Free (GF) scheduling models so that they can be easily extended to deal with various problems in URLLC use cases. To demonstrate the extensibility of the generalized models, we introduce the reliability parameter α to the models and show that the extended models can be more rigorous against failure scenarios, which provides more flexible options in the design of GF scheduling.