NDLI: Sparsity Update Subspace Pursuit Algorithm for Compressed Spectrum Sensing

Content Provider	IEEE Xplore Digital Library
Author	Li Chang Jen-Ming Wu
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu, Taiwan (Li Chang; Jen-Ming Wu)
Abstract	This paper presents an Sparsity Update Subspace Pursuit (SUSP) algorithm for compressed sparse signal reconstruction with unknown sparsity. From practical point of view, the sparsity information is usually unavailable in many applications. In particular, the compressed spectrum sensing application is considered in this paper . The proposed SUSP algorithm begins with sparsity estimation and iteratively updates the sparsity based on the the residual value with subspace pursuit approach. A termination criterion is developed to facilitate the convergence of the sparse update iteration. Moreover, a tail biting rule is devised to refine the reconstruction. Consequently, the sparse signal is recovered and the reconstruction performance is improved. The recovery rate performance is numerically evaluated for both the known sparsity and the unknown sparsity cases. For each case, the recovery mean square errors are also presented for the noisy noisy environment. The results show that the resulting performance outperforms the popular methods using the maximum pursuit or the subspace pursuit based approaches.
Starting Page	1
Ending Page	5
File Size	133420
Page Count	5
File Format	PDF
e-ISBN	9781479980918
DOI	10.1109/VTCFall.2015.7391149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matching pursuit algorithms Correlation Estimation Sensors Sparse matrices Noise measurement Complexity theory
Content Type	Text
Resource Type	Article

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