Prediction Based Adaptive RF Spectrum Reservation in Wireless Virtualization

With wireless virtualization, Wireless Infrastructure Providers (WIPs) are able to sublease out RF spectrum to multiple Wireless Virtual Network Operators (WVNO) who in turn offer services to their customers while sharing the same physical infrastructure. WVNOs are capable of leasing through a reservation process which may be accompanied by some strict guarantees, usually discouraging overbooking through certain penalties. On a global scale, it is important for WIPs to also be able to proactively reserve spectrum resources for consumer usage based on informed estimates. As part of the educated estimation, predictions are made from data of previous spectrum allocations and harmonized with aggregation of crowd-sourced data for events in a bid to reduce the probability of overbooking. The data aggregation effort relies on the the reliability of workers to generate highly accurate results using a community-based aggregation model. Also in this paper, a novel spectrum reservation prediction algorithm, namely Volume-conditioned Spectrum Selective Moving Average (VSSMA) is proposed using the trend similarity of spectrum allocation. The simulation results show that the relative mean error of the VSSMA algorithm is much lower than the Exponential Weighted Moving Average (EWMA) algorithm which is widely used now. We validate the desirable properties of the proposed approach through theoretical analysis, as well as simulations.