Trace-Based Sparsity Order Estimation With Sparsely Sampled Random Matrices

Zhang, Zixuan; Liu, Fulai; Du, Ruxu; Huang, Caimei; Sheng, Juan

doi:10.1109/tcomm.2016.2606402

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Within the Multiple Measurement Vector (MMV) framework, the direct SOE method based on the trace of the covariance matrix of the measurements was presented in [23], with the assumption that the signal power is known a priori. By identifying the slope change in the ordered eigenvalues VOLUME 10, 2022 of the covariance matrix of the measurements, SOE was performed in [24].…”

Section: B Related Work On Soementioning

confidence: 99%

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

et al. 2022

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We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where a sparse Binary Sensing Matrix aids Sparsity Order Estimation, and a Gaussian Sensing Matrix aids reconstruction. The Binary Sensing Matrix is deterministic and is adapted according to the varying nature of the sparsity order. We estimate the sparsity order by exploiting the sparse structure of the Binary Sensing Matrix and the statistics of the obtained measurements. We refine the estimates of the sparsity order using a Kalman filter with a discrete Markov model that characterizes the sparsity order variation. A Binary Sensing Matrix-Aided Orthogonal Matching Pursuit is developed for faster recovery of compressible signals. Simulation results on real-world and synthetic data demonstrate the merits of the proposed sparsity order estimation and recovery methods compared to other existing methods. Our proposed methods are practical and recover compressible signals at least 25% faster than the existing methods.

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Section: B Related Work On Soementioning

confidence: 99%

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

et al. 2022

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“…In the context of cognitive radio spectrum sensing, sparsity level estimation using the Monte Carlo simulations is given in [26], and computationally intensive eigenvalue method is presented in [27] assuming that sparsity level is time-invariant. The estimation based on the trace of the covariance matrix of the measurements is given in [28] assuming that the signal statistics is known a priori.…”

Section: B Related Literaturementioning

confidence: 99%

“…Considering the above, there remain issues in sparsity level estimation such as (i) doing away with or minimizing the additional measurements [21]- [23] (ii) overcoming the asssumption that the knowledge on sparse signal statistics [24], [25], [28] is available a priori, and (iii) adapting to the time-varying signal statistics. In addition to this, practical implementation aspects of sparsity level estimators in the CS acquisition and recovery systems are not being addressed in literature thus far, to the best of our knowledge.…”

Section: B Related Literaturementioning

confidence: 99%

“…The performance of the proposed estimator is evaluated by applying it to a model-generated data as well as the real-world signals. The performance measures such as Mean Recovery Error (MRE) =|(k − k)/k|, NRE, and the computational complexity are compared between the proposed and other sparsity level estimators described in [22], [25], and [28] as well as support estimators (size of estimated support gives sparsity level) such as [20] and [17], and Oracle CS estimator which is aware of statistics of signal and noise for support recovery of the sparse signal.…”

Section: Performance Evaluation For the Proposed Ml-mls Estimationmentioning

confidence: 99%

“…The performance of ML-MLS estimation technique is compared in terms of the computational complexity with Lopes method [22], 2-GMM method [24], and Trace based method [28] where sparsity level is estimated, and DCS-AMP method [20], DBBD-Kronecker [29], and KFCS method [17] where support is estimated which gives knowledge on sparsity level for a given m number of measurements, as follows.…”

Section: A Comparison On Computational Complexitymentioning

confidence: 99%

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Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

et al. 2021

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In the field of Compressed Sensing, estimation of sparsity level is very essential as the sparsity level determines the minimum number of (i) measurements to be obtained of a sparse signal during acquisition and (ii) iterations to be performed for many of the greedy techniques for the perfect recovery of the sparse signal from the obtained measurements. In this paper, we propose a Maximum Likelihood (ML) estimator to estimate the instantaneous sparsity level during acquisition and an ML sequence (MLS) estimator of sparsity levels during recovery. As the sparsity level varies in time due to the continuous birth of newer supporting components and death of existing supporting components, this paper models the sparsity level variation as a stochastic birth-death process. The real-world applications of the proposed estimators are presented on the compression of aircraft vibration signals and the estimation of wireless channels. The simulation results on real-world and model-generated data demonstrate the performance merits of the proposed estimators compared to other existing methods.

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SFIM Detector Based on Joint-Sparse Index Removal for MIMO-OFDM-CR System

Zhang

Liu

et al. 2020

IEEE Commun. Lett.

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Trace-Based Sparsity Order Estimation With Sparsely Sampled Random Matrices

Cited by 6 publications

References 25 publications

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

SFIM Detector Based on Joint-Sparse Index Removal for MIMO-OFDM-CR System

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