Abstract
In this paper, we propose a fast algorithm based on Reweighted Alternating Direction Method of Multiplier (R-ADMM) for cooperative compressive wideband spectrum sensing. The R-ADMM alternately updates the recovered signal matrix, the Lagrangian multiplier and the residue, and all update rules only involve matrix or vector multiplications and summations. To seek joint sparse solutions in a fully distributed scheme, multiple cognitive users collaborate during the sensing period by enforcing consensus among local spectral estimates. Meanwhile, adding weight in the target term and suppressing non-zero elements with large weights for getting the solution close to minimumnorm. The experimental results show that detection probability and detection speed of the algorithm has been improved under low SNR.
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