HARNESSING PREDICTIVE MODELS OF DURATIONS OF CHANNEL AVAILABILITY FOR ENHANCED OPPORTUNISTIC ALLOCATION OF RADIO SPECTRUM

摘要

A proactive adaptive radio methodology for the opportunistic allocation of radio spectrum is described wherein radio spectrum resources are allocated by employing machine learning to learn models, via accruing data over time. These models have the ability to predict the context-sensitive durations of the availability of channels. Such predictive models are combined with decision-theoretic cost-benefit analyses to minimize disruptions of service or quality that can be associated with reactive allocation policies by proactively seeking to switch channels in advance of the loss of a channel. These statistical machine learning techniques can also be employed to generate price predictions in order to facilitate a sale or rental of the available frequencies, or in the switching analyses. The methods can be employed in non-cooperating distributed models of allocation, in centralized allocation approaches, and in hybrid spectrum allocation scenarios.