This paper presents a comprehensive cognitive management framework for spectrum selection in cognitive radio networks. The framework uses a belief vector concept as a means to predict the interference affecting the different spectrum blocks and relies on a smart analysis of the scenario dynamicity to properly determine an adequate observation strategy to balance the trade-off between achievable performance and measurement requirements. In this respect, the paper shows that the interference dynamics in a given spectrum block can be properly characterized through the second highest eigenvalue of the interference state transition matrix. Therefore, this indicator is retained in the proposed framework as a relevant parameter to drive the selection of both the observation strategy and spectrum selection decision-making criterion. The paper evaluates the proposed framework to illustrate the capability to properly choose among a set of possible observation strategies under different scenario conditions. Furthermore, a comparison against other state-of-the-art solutions is presented.

Peer Reviewed