On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN

Autor:	Vilà, Irene, Sallent, Oriol, Pérez-Romero, Jordi
Rok vydání:	2022
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The capacity sharing problem in Radio Access Network (RAN) slicing deals with the distribution of the capacity available in each RAN node among various RAN slices to satisfy their traffic demands and efficiently use the radio resources. While several capacity sharing algorithmic solutions have been proposed in the literature, their practical implementation still remains as a gap. In this paper, the implementation of a Reinforcement Learning-based capacity sharing algorithm over the O-RAN architecture is discussed, providing insights into the operation of the involved interfaces and the containerization of the solution. Moreover, the description of the testbed implemented to validate the solution is included and some performance and validation results are presented. Comment: Submitted to IEEE GLOBECOM 2022 workshop on NextGenRAN
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.10390 Zobrazit plný text záznamu View this record from Arxiv