Abstract
Compressive sensing (CS) techniques have been proposed for wideband spectrum sensing applications to achieve sub-Nyquist-rate sampling. The complexity of CS recovery algorithm and the detection performance against noise are two of the main challenges of the implementation of compressive spectrum sensing (CSS). Greedy algorithms have been of particular interest in CSS due to low complexity. We firstly propose a novel spectrum sparsity estimation scheme directly from sub-Nyquist measurements, with which the computational effort of greedy pursuit algorithms can be saved and recovery performance improved. Besides, the spectrum sparsity estimates also enable hard detection of channel occupancy where threshold adaption for energy detection is avoided. Moreover, with the detected dimension of signal subspace, we propose to implement joint-block-sparse multiple-measurement-vector (MMV) model of CSS whose dimension can be reduced to minimum and meanwhile a large portion of noise is removed. The proposed MMV model with noise and dimension reduction further improves the detection performance and also keeps the complexity low. Finally, we generalize the hard thresholding pursuit (HTP) algorithm to recover joint-block-sparse signals. In simulations, the detection performance and complexity of the proposed CSS scheme show striking superiority against multiple benchmarking schemes.
Highlights
T HE rapid evolution of wireless communications has demanded increasingly large data rate and service coverage, and the spectrum scarcity appears to be a major challenge of wireless communication applications
This paper presents a novel greedy-pursuit-based compressive spectrum sensing (CSS) scheme with the aid of subspacedecomposition-based spectrum sparsity estimation, where the spectrum sparsity is directly estimated from the output of the sub-Nyquist measurements without recovery operations
A multiple-measurement-vector (MMV) model with noise and dimension reduction is introduced, and a generalized version of hard thresholding pursuit (HTP) for joint-block sparse signals is proposed as the recovery algorithm
Summary
T HE rapid evolution of wireless communications has demanded increasingly large data rate and service coverage, and the spectrum scarcity appears to be a major challenge of wireless communication applications. QI et al.: LOW-COMPLEXITY SUBSPACE-AIDED COMPRESSIVE SPECTRUM SENSING OVER WIDEBAND WHITESPACE Another simple solution is to directly use the channel sparsity information from the geo-location database. Since the proposed spectrum sparsity estimation requires multiple samples of sub-Nyquist measurement vectors, we further propose to use multiple-measurement-vector (MMV) model for joint-block-sparse signals for CS directly instead of singlemeasurement-vector (SMV) model. The merits of the proposed spectrum sparsity estimation scheme and the implementation of dimension-reduced MMV mordeTl hareeessutimmmataiorinzeodf as follows: spectrum sparsity is directly from compressed measurements, which does not require recovery r operations; A valid estimate of spectrum sparsity aids the greedy pursuit algorithm to reduce the complexity and enhance r the recovery performance; Dimension reduction of the MMV model further improves the recovery performance and reduces recovery r complexity.
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