Performance Analysis of Joint-Sparse Recovery from Multiple Measurement Vectors via Convex Optimization: Which Prior Information is Better?

Hu, Shih-Wei; Lin, Gang-Xuan; Hsieh, Sung-Hsien; Lu, Chun-Shien

doi:10.1109/access.2018.2791580

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…This work focuses on the single measurement vector problem. Other works focused on Multiple Measurement Vectors (MMV) can be found in [26]- [29]. Some open research challenges related to sparse signal estimation are also discussed.…”

Section: Introductionmentioning

confidence: 99%

A Review of Sparse Recovery Algorithms

et al. 2019

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Nowadays, a large amount of information has to be transmitted or processed. This implies highpower processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparse recovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented.

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Section: Introductionmentioning

confidence: 99%

A Review of Sparse Recovery Algorithms

et al. 2019

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“…This can be achieved by precisely revealing the locations of the phase transition of the high-dimensional group sparsity estimation problem via solving the convex optimization problem P. Although recent years have seen progresses on structured signal estimation [49], [50], [27], they only provide a success condition for signal recovery without precise phase transition analysis. The recent work [28] provided a principled framework to predict phase transitions (including the location and width of the transition region) for random cone programs [51] via the theory of conic integral geometry. Unfortunately, the approach based on conic integral geometry is only applicable in the real field case, which thus cannot be directly applied for problem P in the complex field.…”

Section: System Model and Problem Formulation A System Model Andmentioning

confidence: 99%

Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

Jiang

Shi

Zhang

et al. 2019

IEEE Internet Things J.

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Massive device connectivity is a crucial communication challenge for Internet of Things (IoT) networks, which consist of a large number of devices with sporadic traffic. In each coherence block, the serving base station needs to identify the active devices and estimate their channel state information for effective communication. By exploiting the sparsity pattern of data transmission, we develop a structured group sparsity estimation method to simultaneously detect the active devices and estimate the corresponding channels. This method significantly reduces the signature sequence length while supporting massive IoT access. To determine the optimal signature sequence length, we study the phase transition behavior of the group sparsity estimation problem. Specifically, user activity can be successfully estimated with a high probability when the signature sequence length exceeds a threshold; otherwise, it fails with a high probability. The location and width of the phase transition region are characterized via the theory of conic integral geometry. We further develop a smoothing method to solve the highdimensional structured estimation problem with a given limited time budget. This is achieved by sharply characterizing the convergence rate in terms of the smoothing parameter, signature sequence length and estimation accuracy, yielding a tradeoff between the estimation accuracy and computational cost. Numerical results are provided to illustrate the accuracy of our theoretical results and the benefits of smoothing techniques.

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Performance Analysis of Joint-Sparse Recovery from Multiple Measurement Vectors via Convex Optimization: Which Prior Information is Better?

Cited by 7 publications

References 33 publications

A Review of Sparse Recovery Algorithms

A Review of Sparse Recovery Algorithms

Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

Harmonic analysis in distributed power system based on IoT and dynamic compressed sensing

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