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Compressive sampling has great potential for making wideband spectrum sensingpossible at sub-Nyquist sampling rates. As a result, there have recently beenresearch efforts that leverage compressive sampling to enable efficientwideband spectrum sensing. These efforts consider homogenous wideband spectrum,where all bands are assumed to have similar PU traffic characteristics. Inpractice, however, wideband spectrum is not homogeneous, in that differentspectrum bands could present different PU occupancy patterns. In fact, thenature of spectrum assignment, in which applications of similar types are oftenassigned bands within the same block, dictates that wideband spectrum is indeedheterogeneous. In this paper, we consider heterogeneous wideband spectrum, andexploit its inherent, block-like structure to design efficient compressivespectrum sensing techniques that are well suited for heterogeneous widebandspectrum. We propose a weighted $\ell_1-$minimization sensing informationrecovery algorithm that achieves more stable recovery than that achieved byexisting approaches while accounting for the variations of spectrum occupancyacross both the time and frequency dimensions. In addition, we show that ourproposed algorithm requires a lesser number of sensing measurements whencompared to the state-of-the-art approaches.

Efficient Spectrum Availability Information Recovery for Wideband DSA Networks: A Weighted Compressive Sampling Approach