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Multi-parameter cognition in a cognitive radio network (CRN) provides a morethorough understanding of the radio environments, and could potentially lead tofar more intelligent and efficient spectrum usage for a secondary user. In thispaper, we investigate the multi-parameter cognition problem for a CRN where theprimary transmitter (PT) radiates multiple transmit power levels, and propose alearning-based two-stage spectrum sharing strategy. We first propose adata-driven/machine learning based multi-level spectrum sensing scheme,including the spectrum learning (Stage I) and prediction (the first part inStage II). This fully blind sensing scheme does not require any prior knowledgeof the PT power characteristics. Then, based on a novel normalized power levelalignment metric, we propose two prediction-transmission structures, namelyperiodic and non-periodic, for spectrum access (the second part in Stage II),which enable the secondary transmitter (ST) to closely follow the PT powerlevel variation. The periodic structure features a fixed prediction interval,while the non-periodic one dynamically determines the interval with a proposedreinforcement learning algorithm to further improve the alignment metric.Finally, we extend the prediction-transmission structure to an online scenario,where the number of PT power levels might change as a consequence of PTadapting to the environment fluctuation or quality of service variation. Thesimulation results demonstrate the effectiveness of the proposed strategy invarious scenarios.

A Learning-Based Two-Stage Spectrum Sharing Strategy with Multiple Primary Transmit Power Levels