Opportunistic Admissibility and Resource Allocation For Slicing-based Radio Access Networks

Abstract

Network slicing (NS) is envisioned as a promising technology to meet the extremely diversified service requirements of users for future mobile networks. In radio access networks (RAN) slicing, the service providers (SPs) can rent network slicing instances from the infrastructure provider (InP) to meet the requirements of network services. However, both SPs and InP face the challenges of maintaining the quality of user experience and high profit in a dynamic environment, arising from random arrivals and departures of slice requests, uncertain resource availability, and multi-dimensional resource allocation. Therefore, admissibility and resource allocation become more complicated than that in traditional mobile networks. This paper proposes an opportunistic admissibility and resource allocation (OAR) policy to deal with the above challenges. To cope with the randomness of slice requests and resource availability, we first formulate this issue as a Markov Decision Process (MDP) problem to obtain the optimal admissibility strategy while maximizing the overall reward. Furthermore, we adopt a buyer-seller game-theoretic approach to optimize resource allocation, motivating SPs and InP to maximize their rewards. Numerical results show that the proposed OAR policy makes reasonable decisions effectively and steadily and outperforms the baseline scheme for system reward.