An Inter-Slice RB Leasing and Association Adjustment Scheme in O-RAN

The slice-based Open Radio Access Network (O-RAN) enables rapid deployment of logical networks and provides personalized services. In designing the inter-slice resource sharing scheme, multiple factors need to be considered. Firstly, resource isolation level (RIL) and interference isolation level (IIL) are two key factors in ensuring slice isolation. Secondly, the new business models brought by network slicing imply the need to consider tenants' resource costs. However, to date, no prior studies have considered RIL, IIL and costs simultaneously, often involving only one or two of them. Accordingly, we propose an inter-slice resource block (RB) leasing and association adjustment scheme (ISRLA) that allows slices to borrow RBs from the shared resource pool and other slices. ISRLA is divided into three modules, namely, prediction module, leasing module, and RB association adjustment module. Firstly, the prediction module uses a Long Short-Term Memory model to predict RB demand and guide subsequent resource optimization. Then, the leasing module balances RIL and costs to determine the number of RBs to be leased in or leased out. This is defined as a nonlinear integer programming problem, which is solved through an iterative strategy based on camp swapping (ISBCS). ISBCS is further proven to be convergent. Finally, the RB association adjustment module determines specific RB adjustment strategies based on the results obtained from the leasing module, aiming to ensure IIL. Here, we design a potential game based on the Lagrangian relaxation (LR) method to obtain an approximate optimal solution while reducing computational complexity. Compared with camp enumeration (CE), solver (MOSEK), and genetic algorithm (GA), the proposed ISRLA reduces simulation time by up to 84%, 95%, and 99%, respectively. When the number of slices is 4, the performance of ISRLA is the same as that of CE. Moreover, the IIL gap between ISRLA and MOSEK is less than 4.5%. The simulation results show that the proposed ISRLA can obtain the approximate optimal solution with low computational complexity.