Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach

Khanna, Saurabh; Murthy, Chandra R.

doi:10.1109/tsipn.2016.2612120

Cited by 34 publications

(13 citation statements)

References 45 publications

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“…If the target is a sparse target, Orthogonal Matching Pursuit (OMP) algorithm [22] is more efficient. While the total variation (TV) regularization, such as the TVAL3 algorithm [23] and Sparse Bayesian Learning(SBL) algorithm [24] are worth considering when the target is extended target. The TVAL3 algorithm and SBL algorithm are also very effective for solving sparse targets.…”

Section: Numerical Experiments and Discussionmentioning

confidence: 99%

Antenna Phase Error Compensation for Terahertz Coded-Aperture Imaging

et al. 2020

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Coded-aperture antenna plays an important role in terahertz coded-aperture imaging radar system. However, the performance of a system is inevitably affected by the phase errors introduced by the coded-aperture antenna elements. In this paper, we propose a phase error compensation method by deducing a formula to compute all element phase errors accurately. According to the formula, the phase errors can be calibrated by using a calibrator and can be used to compensate the imaging model of the system. Numerical simulations demonstrate that the proposed method can effectively improve the imaging quality when the elemental phase error exceeds 10 ∘ .

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Section: Numerical Experiments and Discussionmentioning

confidence: 99%

Antenna Phase Error Compensation for Terahertz Coded-Aperture Imaging

et al. 2020

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“…In addition, the unknown variables γ b for neighboring APs are coupled with each other. These obstacles make the problem intractable to solve and existing algorithms, e.g., [31] and [34], are not applicable to such a problem. In this paper, we aim to design a scheme which computes device state vectors of all APs independently to facilitate decentralized implementation.…”

Section: B a Decentralized Approximate Separating Strategymentioning

confidence: 99%

Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms

Shao

Chen

et al. 2020

IEEE Trans. Signal Process.

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This paper investigates the issue of cooperative activity detection for grant-free random access in the sixthgeneration (6G) cell-free wireless networks with sourced and unsourced paradigms. First, we propose a cooperative framework for solving the problem of device activity detection in sourced random access. In particular, multiple access points (APs) cooperatively detect the device activity via exchanging low-dimensional intermediate information with their neighbors. This is enabled by the proposed covariance-based algorithm via exploiting both the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. A decentralized approximate separating approach is introduced based on the forward-backward splitting strategy for addressing the formulated problem. Then, the proposed activity detection algorithm is adopted as a decoder of cooperative unsourced random access, where the multiple APs cooperatively detect the list of transmitted messages regardless of the identity of the transmitting devices. Finally, we provide sufficient conditions on the step sizes that ensure the convergence of the proposed algorithm in the sense of Bregman divergence. Simulation results show that the proposed algorithm is efficient for addressing both sourced and unsourced massive random access problems, while requires a shorter signature sequence and accommodates a significantly larger number of active devices with a reasonable antenna array size, compared with the state-of-art algorithms.

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“…Belief propagation (BP) [ 35 ] is an iterative message passing algorithm that can calculate the marginal distribution or find estimates such as the most probable assignment (MAP) in Bayesian networks [ 36 , 37 ]. In the sparse signal recovery area, belief propagation is implemented on factor graphs and considered a fast decoder in Bayesian compressive sensing frameworks.…”

Section: Preliminaritiesmentioning

confidence: 99%

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Masoum

Meratnia

Havinga

2018

Sensors

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Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. The proposed solution utilizes sparsity distribution of signals to group sensor nodes into several coalitions and then implements localized compressive sensing inside coalitions. This solution improves data-gathering performance in terms of both data accuracy and energy consumption. The approach curbs both data-transmission costs and number of measurements. Coalition-based data gathering cuts transmission costs, and the number of measurements is reduced by scheduling sensor nodes and adjusting their sampling frequency. Our simulation showed that our approach enhances network performance by minimizing energy cost and improving data accuracy.

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Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach

Cited by 34 publications

References 45 publications

Antenna Phase Error Compensation for Terahertz Coded-Aperture Imaging

Antenna Phase Error Compensation for Terahertz Coded-Aperture Imaging

Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

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