Wireless Network Slicing: Generalized Kelly Mechanism-Based Resource Allocation

• Published in: IEEE journal on selected areas in communications 2019-08, Vol.37 (8), p.1794-1807
• Main Authors: Tun, Yan Kyaw, Tran, Nguyen H., Ngo, Duy Trong, Pandey, Shashi Raj, Han, Zhu, Hong, Choong Seon
• Format: Article
• Language: English
• Subjects: Bandwidths; Cellular communication; Cost accounting; Generalized Kelly mechanism; III-V semiconductor materials; Indium phosphide; Interference; Network slicing; Radio equipment; Resource allocation; Resource management; Virtual networks; Virtualization; wireless network slicing; wireless network virtualization; Wireless networks
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2019.2927100

Wireless network slicing (i.e., network virtualization) is one of the potential technologies for addressing the issue of rapidly growing demand in mobile data services related to 5G cellular networks. It logically decouples the current cellular networks into two entities: infrastructure providers (InPs) and mobile virtual network operators (MVNOs). The resources of base stations (e.g., resource blocks, transmission power, and antennas), which are owned by the InP, are shared with multiple MVNOs who need resources for their mobile users. Specifically, the physical resources of an InP are abstracted into multiple isolated network slices, which are then allocated to MVNO's mobile users. In this paper, two-level allocation problem in network slicing is examined while enabling efficient resource utilization, inter-slice isolation (i.e., no interference among slices), and intra-slice isolation (i.e., no interference between users in the same slice). A generalized Kelly mechanism (GKM) is also designed, based on which the upper level of the resource allocation issue (i.e., between the InP and MVNOs) is addressed. The benefit of using such a resource bidding and allocation framework is that the seller (InP) does not need to know the true valuation of the bidders (MVNOs). For solving the lower level of resource allocation issue (i.e., between MVNOs and their mobile users), the optimal resource allocation is derived from each MVNO to its mobile users by using Karush-Kuhn-Tucker (KKT) conditions. Then, bandwidth resources are allocated to the users of MVNOs. Finally, the results of the simulation are presented to verify the theoretical analysis of our proposed two-level resource allocation problem in wireless network slicing.