SliceMapper: Intelligent Mapping of O-CU and O-DU onto O-Cloud Sites in 6G O-RAN
This addresses network efficiency for 6G O-RAN operators, but it is incremental as it applies existing Q-learning variants to a specific domain problem.
The paper tackles the problem of optimizing the mapping of O-CU and O-DU onto O-Cloud sites in 6G O-RAN, proposing SliceMapper as an rApp and showing that on-policy function approximation achieves better stability, while tabular methods yield higher average rewards of 5.42 and 5.12.
In this paper, we propose an rApp, named SliceMapper, to optimize the mapping process of the open centralized unit (O-CU) and open distributed unit (O-DU) of an open radio access network (O-RAN) slice subnet onto the underlying open cloud (O-Cloud) sites in sixth-generation (6G) O-RAN. To accomplish this, we first design a system model for SliceMapper and introduce its mathematical framework. Next, we formulate the mapping process addressed by SliceMapper as a sequential decision-making optimization problem. To solve this problem, we implement both on-policy and off-policy variants of the Q-learning algorithm, employing tabular representation as well as function approximation methods for each variant. To evaluate the effectiveness of these approaches, we conduct a series of simulations under various scenarios. We proceed further by performing a comparative analysis of all four variants. The results demonstrate that the on-policy function approximation method outperforms the alternative approaches in terms of stability and lower standard deviation across all random seeds. However, the on-policy and off-policy tabular representation methods achieve higher average rewards, with values of 5.42 and 5.12, respectively. Finally, we conclude the paper and introduce several directions for future research.