Scalable spectrum database construction mechanisms for efficient wideband spectrum access management

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We propose a novel framework for enabling scalable database-driven dynamic spectrum access and sharing of heterogeneous wideband spectrum. The proposed framework consists of two complementary approaches that exploit the merits of compressive sensing theory, low-rank matrix theory, and user cooperation to build an accurate heterogeneous wideband spectrum map by overcoming the time-variability of the number of occupied bands, the need for a high number of measurements per sensing node (SN), the inherent wireless channels’ impairments, and the high reporting network overhead. First, exploiting the fact that close-by SNs have a highly correlated spectrum observation, we leverage distributed compressive sensing to enable cooperative heterogeneous wideband spectrum sensing only from a small number of measurements per each SN. Second, to reduce the network overhead due to the high width of the spectrum of interest, we propose a two-step approach that performs spectrum occupancy recovery using the local low-rank property of occupancy sub-matrices. Then, we combine the completed sub-matrices entries to produce the whole spectrum occupancy matrix. Through simulations, we show that the proposed framework efficiently achieves high detection in the sensing step and minimizes the spectrum occupancy matrix recovery error while reducing the overall network overhead.