Learning in SDN-based multi-tenant cellular networks: A game-theoretic perspective

Abstract

In order to cope with the challenges of increasing user bandwidth demands as well as create new revenues by offering innovative services and applications, Mobile Network Operators (MNOs) are willing to increase their networks' capabilities by making it more flexible, programmable and agile. MNOs are also seeking new technologies to benefit from recent advances in cloud for rapid deployments and elastically scaling services that cloud providers are mostly benefiting today. On one hand, Software-Defined Networking (SDN) concept can be helpful for enabling network infrastructure sharing/slicing and elasticity for “softwarization” of network elements. On the other hand, machine learning and game-theoretical concepts can also be utilized to address network management and orchestration needs of services and applications and improve network infrastructure's operational needs. In that regard, joint utilization of machine learning, game theoretical approaches and SDN concepts for network slicing can be beneficial to MNOs as well as infrastructure providers. In this paper, we utilize regret-matching based learning approach for efficient Radio Remote Head (RRH) assignments among MNOs in software-defined based cloud radio access network (C-RAN). Using game-theoretical approach, we demonstrate convergence of RRH allocations to mixed strategy Nash equilibrium and present significant performance improvements compared to traditional assignment approach.