Cognitive radio spectrum prediction using dictionary learning

Spatio-temporal spectrum prediction algorithms for cognitive radios (CRs) are developed using the framework of dictionary learning and compressive sensing. The interference power levels at each CR node locations are predicted using the measurements from a subset of CR nodes without a priori knowledge on the primary transmitters. Batch and online alternatives are presented, where the online algorithm features low complexity and memory requirements. Numerical tests verify the performance of the proposed novel methods.