Sparse Bayesian learning using correlated hyperparameters for recovery of block sparse signals

Cui, Hongyu; Huang, Duan

doi:10.1016/j.dsp.2017.05.003

Cited by 8 publications

(11 citation statements)

References 17 publications

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“…where κ is a single fixed constant. In practice, the relevance between the neighbour blocks is inhomogeneous, meaning that κs shall be a set of various coefficients [31]. Thus, in the first layer, we modify Equation (10) and develop an adaptive coupled pattern with the form…”

Section: Probability Distribution Of Xmentioning

confidence: 99%

“…In this part, the expectation-maximisation method is invoked to estimate η 1 and η 2 . The expectation of logarithm distribution about η 1 and η 2 is obtained with Equation ( 12) [31].…”

Section: Estimation Of Coupled Parameters η 1 and ηmentioning

confidence: 99%

“…, and η i iþ1 . The mechanism allows for the closed-form estimation of the parameters [30,31]. In the next section, we will test the proposed algorithm in the application of signal detection.…”

Section: Estimation Of Noise Variance β −1mentioning

confidence: 99%

See 2 more Smart Citations

Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning

Zhang

Sun

Bai

et al. 2022

IET Signal Processing

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Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity.

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Section: Probability Distribution Of Xmentioning

confidence: 99%

Section: Estimation Of Coupled Parameters η 1 and ηmentioning

confidence: 99%

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Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning

Zhang

Sun

Bai

et al. 2022

IET Signal Processing

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“…Experiment 1: This paper compares the reconstructed results of block-sparse Bayesian learning fast edge-likelihood maximization algorithm (BSBL-FM) [23], orthogonal matching pursuit algorithm (OMP) [24], and MHSPCS model for multi-channel HS signals. At the same compression ratio, the running time, PSNR, and reconstruction similarity coefficient of the three algorithms were tested.…”

Section: Pexperiments and Analysismentioning

confidence: 99%

Research on Parallel Compressive Sensing and Application of Multi-Channel Synchronous Acquisition of Heart Sound Signals

Cheng

Shan

et al. 2019

IEEE Access

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The rapid development of wearable biosensors based on the Internet of Things provides a new solution for heart disease analysis and prediction of sudden death from heart failure. However, how to deal with a large amount of data generated by heart sound signals is an urgent problem to be solved. Based on the characteristics of heart sound signals, we propose a parallel compressive sensing model for the multichannel synchronous acquisition of heart sound signals. Furthermore, we provide a series of experiments to assess the performance of the model. The experimental results demonstrate that the reconstruction speed of the proposed model is 9-10 times faster than that of the block sparse Bayesian learning algorithm and the orthogonal matching pursuit algorithm, and the reconstruction effect is better. Meanwhile, the proposed model can effectively reconstruct the normal heart sound signal and abnormal heart sound signal of fourchannel synchronous acquisition. Therefore, the proposed model is feasibility. INDEX TERMS Heart sound signals, compressive sensing, parallel signals, monitoring, L 1 norm, block sparse Bayesian learning. I. INTRODUCTION

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“…A source is sparse in a given representation domain if most of its elements are close to zero. The CS technique requires the sparsity of the sources which restricts its application [9]. In [10], the authors proposed to apply the CS technique to solve underdetermined real-valued finite-alphabet source recovery problems.…”

mentioning

confidence: 99%

Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

Hajji¹,

Bey²,

Amis³

2018

Digital Signal Processing

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In this paper, we consider the problem of finite-alphabet source separation in both determined and underdetermined large-scale systems. First, we address the noiseless case and we propose a linear criterion based on 1 -minimization combined with box constraints. We investigate also the system conditions that ensure successful recovery. Next, we apply the approach to the noisy massive MIMO transmission and we propose a quadratic criterion-based detector. Simulation results show the efficiency of the proposed detection methods for various QAM modulations and MIMO configurations. We mention that there is no change in the computational complexity when the constellation size increases. Moreover, the proposed method outperforms the classical Minimum Mean Square Error (MMSE)-based detection algorithms.

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Sparse Bayesian learning using correlated hyperparameters for recovery of block sparse signals

Cited by 8 publications

References 17 publications

Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning

Phase retrieval for block sparsity based on adaptive coupled variational Bayesian learning

Research on Parallel Compressive Sensing and Application of Multi-Channel Synchronous Acquisition of Heart Sound Signals

Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems

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