Low-complexity detection of zero blocks in wideband spectrum sensing

Zeinalkhani, Zeinab; Banihashemi, Amir H.

doi:10.1109/isit.2015.7282922

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…Consider a sparse bipartite graph if ‖y ‖ 2 == 0 then 8is zero-ton. (9) else (10) [flag value location] = SingletonEstimator(y ) (11) if flag then (12) y = y − value ⋅ (:, location),x(location) = value (13) end if (14) end if (15) the locations of nonzero elements in signal are random, the graph is left-regular, as shown in Figure 1. In the SWIFT decoding algorithm, the criterions employed by the peeling decode algorithm depend on the degree of check nodes.…”

Section: Asymptotic Analysis Of Decoding Algorithmmentioning

confidence: 99%

“…For the complexity of recovery algorithm, the ℓ 1 norm minimization has a computation complexity of O( 3 ). To reduce this complexity, with the sparse measurement matrices, various low computational complexity message-passing algorithms have been introduced for reconstruction of sparse signals in CS [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In [9], the authors used the random sparse matrix as a new sparse measurement matrix and proposed an iterative algorithm of complexity of O( log( ) log( )), while it only required = O( log( )) measurements.…”

Section: Introductionmentioning

confidence: 99%

“…These verificationbased (VB) recovery algorithms in the context of CS have been analyzed rigorously via the density evolution in [10,11]. In [12], the VB algorithm was applied to the wideband spectrum sensing, where the measurement matrix is designed based on a block sparse matrix. Based on special nonnegative sparse signals, a new message-passing algorithm, called the Interval-Passing (IP) algorithm, was proposed in [13], which has a better performance than the VB algorithm in [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Peeling Decoding of LDPC Codes with Applications in Compressed Sensing

Zeng

Wang

2016

Mathematical Problems in Engineering

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We present a new approach for the analysis of iterative peeling decoding recovery algorithms in the context of Low-Density Parity-Check (LDPC) codes and compressed sensing. The iterative recovery algorithm is particularly interesting for its low measurement cost and low computational complexity. The asymptotic analysis can track the evolution of the fraction of unrecovered signal elements in each iteration, which is similar to the well-known density evolution analysis in the context of LDPC decoding algorithm. Our analysis shows that there exists a threshold on the density factor; if under this threshold, the recovery algorithm is successful; otherwise it will fail. Simulation results are also provided for verifying the agreement between the proposed asymptotic analysis and recovery algorithm. Compared with existing works of peeling decoding algorithm, focusing on the failure probability of the recovery algorithm, our proposed approach gives accurate evolution of performance with different parameters of measurement matrices and is easy to implement. We also show that the peeling decoding algorithm performs better than other schemes based on LDPC codes.

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Section: Asymptotic Analysis Of Decoding Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Peeling Decoding of LDPC Codes with Applications in Compressed Sensing

Zeng

Wang

2016

Mathematical Problems in Engineering

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“…Moreover, in [49] authors proposed graph construction approaches under VB MPAs to reach to a sub-linear measurement cost. Furthermore recently their applications in fast and accurate spectrum sensing is also addressed in [50]. As a result, we will focus on these algorithms, more precisely, and eventually we will fill the gap that exists in their performance analysis, analytically.…”

Section: Combinatorial Approachesmentioning

confidence: 99%

Finite Length Analysis of Verifcation-Based Message Passing Algorithms in Compressed Sensing

Farhangdoust¹

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Compressed sensing (CS) is a newly developed area in signal processing which generally aims at compressing sparse signals. The most challenging part of a CS problem can be the recovery process in which a signal will be reconstructed from its measurements. In this thesis, we address the finite length analysis of verification-based message-passing algorithms (VB MPAs), analytically. In contrary to asymptotic regime analysis, the finite length analysis is a fairly untouched problem. We begin the analysis by characterizing the problematic combinatorial structures, defined over the measurement graph of the CS system in which VB algorithms are trapped. Then, based on this characterization, we derive exact expressions for the average probability of error of a given ensemble of graphs, and also for a given individual graph.

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Low-complexity detection of zero blocks in wideband spectrum sensing

Cited by 2 publications

References 14 publications

Peeling Decoding of LDPC Codes with Applications in Compressed Sensing

Peeling Decoding of LDPC Codes with Applications in Compressed Sensing

Finite Length Analysis of Verifcation-Based Message Passing Algorithms in Compressed Sensing

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