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Exploiting knowledge management for supporting spectrum selection in Cognitive Radio networks

In order to increase Cognitive Radio operation efficiency, this paper builds up a new knowledge management functional architecture for supporting spectrum management. It integrates the fittingness factor concept proposed by the authors in a prior work and includes a set of advanced statistics capturing the influence of the radio environment. Then, a Knowledge Manager (KM) exploiting these statistics and observed fittingness factor values has been developed to monitor the time-varying suitability of spectrum resources to support heterogeneous services. Based on estimated suitability levels, a new strategy combining Spectrum Selection (SS) and Spectrum Mobility (SM) functionalities has been proposed. Results have shown that the proposed strategy efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains (ranging from 85% to 100%) w.r.t. a pure random selection while exhibiting substantial robustness to changes in interference levels.