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Joint Base Station Selection and Adaptive Slicing in Virtualized Wireless Networks: A Stochastic Optimization Framework

Wireless network virtualization is a promising avenue of research for next-generation 5G cellular networks. Virtualization focuses on the concept of active resource sharing and the building of a network designed for specific demands, decreasing operational expenditures, and improving demand satisfaction of cellular networks. This work investigates the problem of selecting base stations (BSs) to construct a virtual network that meets the the specific demands of a service provider, and adaptive slicing of the resources between the service provider’s demand points. A two-stage stochastic optimization framework is introduced to model the problem of joint BS selection and adaptive slicing. Two methods are presented for determining an approximation for the two-stage stochastic optimization model. The first method uses a sampling approach applied to the deterministic equivalent program of the stochastic model. The second method uses a genetic algorithm for BS selection and adaptive slicing via a single-stage linear optimization problem. For testing, a number of scenarios were generated using a log-normal model designed to emulate demand from real world cellular networks. Simulations indicate that the first approach can provide a reasonably good solution, but is constrained as the time expense grows exponentially with the number of parameters. The second approach provides a vast improvement in run time with the introduction of some error.