Unconstrained regularized &amp;#x2113;&lt;inf&gt;p&lt;/inf&gt;-norm based algorithm for the reconstruction of sparse signals

Pant, Jeevan K.; Lu, Wu-Sheng; Antoniou, A.

doi:10.1109/iscas.2011.5937919

Cited by 17 publications

(37 citation statements)

References 11 publications

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“…For a fixed value of σ, the problem in Equation 9 is now solved using a quasi-Newton algorithm where an approximation of the inverse of the Hessian is obtained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) update formula [26][27][28]. As such, the algorithm is referred to as the l 2 -Sl 0 -BFGS algorithm.…”

Section: The L 2 -Sl 0 -Bfgs Reconstruction Algorithm For Channel Estmentioning

confidence: 99%

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing

Zhu

Zhang

Yan

2013

J Wireless Com Network

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This paper investigates the sparse channel estimation issue of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Beginning with the formulation of least squares (LS) solution to sparse MIMO-OFDM channel estimation, a compressed channel sensing (CCS) framework based on the new smoothed l 0 -norm-regularized least squares (l 2 -Sl 0 ) algorithm is proposed. Three methods, namely quasi-Newton, conjugate gradient, and optimization in the null and complement spaces of the measurement matrix, are then proposed to solve the l 2 -Sl 0 unconstrained optimization problem. Moreover, the two former are also applied to solve the l 2 -Sl 0 channel estimation. A number of computer simulation-based experiments are conducted showing a better reconstruction accuracy of the l 2 -Sl 0 algorithm as compared with the smoothed l 0 -norm (Sl 0 ) algorithm in the presence of noise. The proposed CCS approach can save nearly 25% pilot signals to maintain the same mean square error (MSE) and bit error rate (BER) performances as given by the conventional LS method.

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Section: The L 2 -Sl 0 -Bfgs Reconstruction Algorithm For Channel Estmentioning

confidence: 99%

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing

Zhu

Zhang

Yan

2013

J Wireless Com Network

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“…The problem in (6) can be solved by using a sequential strategy whereby the problem is solved for a sequence of decreasing values of with λ fixed as was done in [5]. Based on extensive simulations, a better solution can be obtained by solving the problem in (6) by decreasing the values of λ and simultaneously as the sequential optimization proceeds.…”

Section: B Algorithm Based On T V P Normmentioning

confidence: 99%

“…. .. An explicit expression for G(α) is given in Appendix B, and a description of such a line-search can be found in [5]. Based on the preceding principles, the T V p -RLS algorithm summarized in Table I can be constructed.…”

Section: B Algorithm Based On T V P Normmentioning

confidence: 99%

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A new algorithm for compressive sensing based on total-variation norm

Pant

Antoniou

2013

2013 IEEE International Symposium on Circuits and Systems (ISCAS2013)

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Abstract-A new algorithm for the reconstruction of images with sparse gradient is proposed. The algorithm is based on the minimization of the so called total-variation (T V ) regularized squared error and is especially suited for image reconstruction from a small number of measurements. The algorithm is developed based on a generalized T V norm and uses a sequential conjugate-gradient method. Simulation results are presented which demonstrate that the proposed algorithm yields significantly improved reconstruction performance for images with sparse gradient and requires significantly reduced computational effort relative to the log-barrier based T V -regularized leastsquares algorithm.

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“…Signal needs to be reconstructed for further data analysis and medical diagnosis in telehealth systems. The state-ofthe-art signal reconstruction algorithms for CS are mainly based on l 1 -norm minimization, 11 l 0 -norm minimization, 12,13 l p -norm minimization, 14 greedy iterative, 15,16 and Bayesian learning. 17 In Ref.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System

Hua

Zhang

Liu

et al. 2016

J CIRCUIT SYST COMP

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Due to the capacity of compressing and recovering signal with low energy consumption, compressive sensing (CS) has drawn considerable attention in wireless telemonitoring of electrocardiogram (ECG) signals. However, most existing CS methods are designed for reconstructing single channel signal, and hence difficult to reconstruct multichannel ECG signals. In this paper, a spatio-temporal sparse model-based algorithm is proposed for the reconstruction of multichannel ECG signals by not only exploiting the temporal correlation in each individual channel signal, but also the spatial correlation among signals from different channels. In addition, a dictionary learning (DL) approach is developed to enhance the performance of the proposed reconstruction algorithm by using the sparsity of ECG signals in some transformed domain. The approach determines a dictionary by learning local dictionaries for each channel and merging them to form a global dictionary. Extensive simulations were performed to validate the proposed algorithms. Simulation results show that the proposed reconstruction algorithm has a better performance in recovering multichannel ECG signals as compared to the benchmarking methods. Moreover, the reconstruction performance of the algorithm can be further improved by using a dictionary matrix, which is obtained from the proposed DL algorithm.

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Unconstrained regularized ℓ<inf>p</inf>-norm based algorithm for the reconstruction of sparse signals

Cited by 17 publications

References 11 publications

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing

A new algorithm for compressive sensing based on total-variation norm

Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System

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