Compressive Spectrum Sensing for Wideband Signals Using Improved Matching Pursuit Algorithms

R, Anupama; Kulkarni, S. Y.; Prasad, S. N.

doi:10.1007/978-981-16-8546-0_20

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The reduced data matrix

and the estimated number of active frequency bands

are then used in the next stage to find out the sparse problem solution

through a greedy algorithm, such as OMP (orthogonal matching pursuit) [ 22 , 23 ], or an optimization-based one, such as LASSO (least absolute shrinkage and selection operator) [ 24 , 25 ]. In [ 26 ], the authors showed that LASSO was a suitable choice for compressive spectrum sensing and recovery in wideband 5G cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms

Burel

Rădoi

Gautier

et al. 2023

Sensors

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Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a sub-Nyquist or Xsampling scheme, known as a modulated wideband converter. First, the data reduction at its output is performed in order to enable a highly effective processing scheme for spectrum reconstruction. The impact of this data transformation on the behavior of the most popular sparse reconstruction algorithms is then analyzed. A new mathematical approach is proposed to demonstrate that greedy reconstruction algorithms, such as Orthogonal Matching Pursuit, are invariant with respect to the proposed data reduction. Relying on the same formalism, a data reduction invariant version of the LASSO (least absolute shrinkage and selection operator) reconstruction algorithm was also introduced. It is finally demonstrated that the proposed algorithm provides good reconstruction results in a wideband spectrum sensing scenario, using both synthetic and measured data.

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“…The reduced data matrix

and the estimated number of active frequency bands

are then used in the next stage to find out the sparse problem solution

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms

Burel

Rădoi

Gautier

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Unlike convex optimization, which minimizes the objective function, greedy optimization determines the location of the non-zero sampling points of the sparse channel by multiple iterations. Orthogonal matching pursuit (OMP) [27], block OMP (BOMP) [28], and compressive sampling matching pursuit (CoSaMP) [29] are the most commonly used CS algorithms. Jiang et al [30] proposed a separable CoSaMP (SCoSaMP) algorithm based on the introduction of backtracking idea.…”

Section: Introductionmentioning

confidence: 99%

“…Its channel recovery performance is significantly improved under the sparse channel model with the same partial support sets. However, the above CS algorithms [27][28][29][30][31][32] usually require a large number of iterations to reduce the approximation error, which brings high complexity. Moreover, channel estimation methods based on CS algorithms require a known number of channel common support sets to achieve optimal performance, which limits their scope of application.…”

Section: Introductionmentioning

confidence: 99%

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

Song,

Zhou,

Wang

et al. 2023

Mathematics

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Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in time-varying multipath fading channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

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FPGA enabled ECG signal reconstruction based on an enhanced orthogonal matching pursuit algorithm

Reddy,

Kumar

2025

Integration

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Compressive Spectrum Sensing for Wideband Signals Using Improved Matching Pursuit Algorithms

Cited by 5 publications

References 12 publications

Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms

Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

FPGA enabled ECG signal reconstruction based on an enhanced orthogonal matching pursuit algorithm

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