On Robust Optimal Joint Deployment and Assignment of RAN Intelligent Controllers in O-RANs

The open radio access network (O-RAN) architecture is consolidating the concept of software-defined cellular networks beyond 5G networks, mainly through the introduction of the near-real-time radio access network (RAN) intelligent controller (Near-RT RIC) and the xApps. The deployment of the Near-RT RICs and the assignment of RAN nodes to the deployed RICs play a crucial role in optimizing the performance of O-RANs. In this paper, we develop a robust optimization framework for joint RIC deployment and assignment, considering the uncertainty in user locations. Specifically, our contributions are as follows. First, we develop $\text{C}^{3}\text{P}^{2}$ , a robust static joint RIC placement and RAN node-RIC assignment scheme. The objective of $\text{C}^{3}\text{P}^{2}$ is to minimize the number of RICs needed to control all RAN nodes while ensuring that the response time to each RAN node will not exceed $\delta$ milliseconds with a probability greater than $\beta$ . Second, we develop CPPA, a robust joint RIC placement and adaptive RAN node-RIC assignment scheme. In contrast to $\text{C}^{3}\text{P}^{2}$ , CPPA enjoys a recourse capability, where the RAN node-RIC assignment adapts to the variations in the user locations. We use chance-constrained stochastic optimization combined with several linearization techniques to develop a mixed-integer linear (MIL) formulation for $\text{C}^{3}\text{P}^{2}$ . Two-stage stochastic optimization with recourse, combined with several linearization techniques, is used to develop an MIL formulation for CPPA. The optimal performance of $\text{C}^{3}\text{P}^{2}$ and CPPA has been examined under various system parameter values. Furthermore, sample average approximation has been employed to design efficient approximate algorithms for solving $\text{C}^{3}\text{P}^{2}$ and CPPA. Our results demonstrate the robustness of the proposed stochastic resource allocation schemes for O-RANs compared to existing deterministic allocation schemes. They also show the merits of adapting the allocation of resources to the network uncertainties compared to statically allocating them