Multiple Virtual Network Operators Research Articles

A QoS-Aware Joint Power and Subchannel Allocation Algorithm for Mobile Network Virtualization

Mobile network virtualization is a promising technology due to its flexibility and feasibility. Since it enables physical resources abstraction and sharing, the overall resource inefficiency can be reduced dramatically. By means of virtualization, mobile service providers can share their physical resources with multiple virtual network operators. In this paper, a joint power and subchannel allocation algorithm for mobile network virtualization (MNV) with quality of services support is proposed. It presents a resource allocation scheme for orthogonal frequency division multiple access-based MNV with multiple virtual network operators. An optimal solution is provided to maximize the total data rate of both infrastructure providers and virtual network operators. Numerical results have shown that the proposed resource allocation algorithm improves the overall performance.

Price-based Resource Allocation for Virtualized Cognitive Radio Networks

We consider a virtualized cognitive radio (CR) network, where multiple virtual network operators (VNOs) who own different virtual cognitive base stations (VCBSs) share the same physical CBS (PCBS) which is owned by an infrastructure provider (InP), sharing the spectrum with the primary user (PU). The uplink scenario is considered where the secondary users (SUs) transmit to the VCBSs. The PU is protected by constraining the interference power from the SUs. Such constraint is applied by the InP through pricing the interference. A Stackelberg game is formulated to jointly maximize the revenue of the InP and the individual utilities of the VNOs, and then the Stackelberg equilibrium is investigated. Specifically, the optimal interference price and channel allocation for the VNOs to maximize the revenue of the InP and the optimal power allocation for the SUs to maximize the individual utilities of the VNOs are derived. In addition, a low-complexity ±-optimal solution is also proposed for obtaining the interference price and channel allocation for the VNOs. Simulations are provided to verify the proposed strategies. It is shown that the proposed strategies are effective in resource allocation and the ±-optimal strategy achieves practically the same performance as the optimal strategy can achieve. It is also shown that the InP will not benefit from a large interference power limit, and selecting VNOs with higher unit rate utility gain to share the resources of the InP is beneficial to both the InP and the VNOs.

Equilibrium Price and Dynamic Virtual Resource Allocation for Wireless Network Virtualization

Economic and technical features are equally important to radio resource allocation in wireless network virtualization (WNV). Regarding virtual resource (VR) as commodity, this paper proposes an effective VR allocation scheme for WNV from the perspective of the market-equilibrium theory. First, physical meaning clear utility functions are defined to characterize the network benefits of user equipments (UEs), infrastructure providers (InPs) and virtual network operators (VNOs) in WNV. Then, the VR allocation problem between one InP and multiple VNOs is formulated as a multi-objective optimization problem. To reduce the algorithm complexity, the multiple-objective problem is first decoupled into two single-objective sub-problems. The supplier-layer sub-problem aims to maximize the benefit of the unique InP, while the customer-layer sub-problem aims to maximize the benefits of the multiple VNOs. Both of the separated sub-problems are solved by using standard convex optimization method, and are combined by searching for the equilibrium-price (EP) of the VR market. As a result, the Pareto optimal solution of the original multi-objective problem is found, at which no one (the InP or anyone of the VNOs) can increase its benefit by deviating the EP without hurting others’ benefits. The effectiveness of the proposed VR allocation scheme is testified through extensive experiments.