Cognitive RAN slicing resource allocation based on Stackelberg game

The cognitive network has become a promising method to solve the spectrum resources shortage problem. Especially for the optimization of network slicing resources in the cognitive radio access network (RAN), we are interested in the profit of the mobile virtual network operator (MVNO) and the utility of secondary users (SUs). In cognitive RAN, we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs. Since the MVNO and SUs are selfish and the game between the MVNO and SUs is difficult to reach equilibrium, we consider modeling this scheme as a Stackelberg game. Leveraging mathematical programming with equilibrium constraints (MPEC) and Karush-Kuhn-Tucker (KKT) conditions, we can obtain a single-level optimization problem, and then prove that the problem is a convex optimization problem. The simulation results show that the proposed method is superior to the noncooperative game. While guaranteeing the Quality of Service (QoS) of primary users (PUs) and SUs, the proposed method can balance the profit of the MVNO and the utility of SUs.