Dynamic radio resource slicing for a two-tier heterogeneous wireless network

In this paper, a dynamic radio resource slicing framework is presented for a two-tier heterogeneous wireless network. Through software-defined networking-enabled wireless network function virtualization, radio spectrum resources of heterogeneous wireless networks are partitioned into different bandwidth slices for different base stations (BSs). This framework facilitates spectrum sharing among heterogeneous BSs and achieves differentiated quality-of-service (QoS) provisioning for data service and machine-to-machine service in the presence of network load dynamics. To determine the set of optimal bandwidth slicing ratios and optimal BS-device association patterns, a network utility maximization problem is formulated with the consideration of different traffic statistics and QoS requirements, location distribution for end devices, varying device locations, load conditions in each cell, and intercell interference. For tractability, the optimization problem is transformed to a biconcave maximization problem. An alternative concave search (ACS) algorithm is then designed to solve for a set of partial optimal solutions. Simulation results verify the convergence property and display low complexity of the ACS algorithm. It is demonstrated that the proposed radio resource slicing framework outperforms the two other resource slicing schemes in terms of low communication overhead, high spectrum utilization, and high aggregate network utility.