On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN
This work addresses the implementation challenge for network operators in efficiently distributing radio resources among RAN slices, but it is incremental as it focuses on applying existing methods to a specific architecture.
The paper tackles the practical implementation gap for capacity sharing algorithms in Radio Access Network slicing by implementing a Reinforcement Learning-based solution over the O-RAN architecture, presenting performance and validation results from a testbed.
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.