Hierarchical Radio Resource Allocation for Network Slicing in Fog Radio Access Networks

Abstract

Network slicing in fog radio access networks is recognized as a cost-efficient solution to support future diverse use cases. However, with the number of user equipments (UE) fast increasing, the centralized resource allocation architecture for network slicing can put heavy burdens on the global radio resource manager (GRRM), and meanwhile slice customization is not easy to achieve. To overcome the two issues, a hierarchical radio resource allocation architecture is proposed in this paper, where the GRRM is responsible for allocating subchannels to local radio resource managers (LRRMs) in slices, which then allocate the assigned resources to their UEs. Under this architecture, a hierarchical resource allocation problem is formulated, and the problem is further modeled as a Stackelberg game with the GRRM as the leader and LRRMs as followers, considering the hierarchy between the GRRM and LRRMs. Due to the NP-hardness of the followers’ problems, a process based on exhaustive search is first proposed to achieve the Stackelberg equilibrium (SE). Nevertheless, when the network scale is large, achieving SE within limited decision making time is impractical for game players. Facing this challenge, the GRRM and LRRMs are seen as bounded rational players, and low complexity algorithms are developed to help them achieve local optimal solutions that lead to a weak version of SE. Simulation results show that there exists a tradeoff between the performance of slices, and the low complexity algorithms achieve close performance to that of exhaustive search and outperform other baselines significantly.