70 research outputs found
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A Decentralized Bayesian Algorithm for Distributed Compressive Sensing in Networked Sensing Systems
Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the sampling cost is high, e.g., the Nyquist rate exceeds the current capabilities of analog-to-digital converters (ADCs). Conventional CS, although effective for dealing with one signal, only leverages the intra-signal correlation for reconstruction. This paper develops a decentralized Bayesian reconstruction algorithm for networked sensing systems to jointly reconstruct multiple signals based on the distributed compressive sensing (DCS) model that exploits both intra- and inter-signal correlations. The proposed approach is able to address networked sensing system applications with privacy concerns and/or for a fusion-centre-free scenario, where centralized approaches fail. Simulation results demonstrate that the proposed decentralized approaches have good recovery performance and converge reasonably quicklyThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TWC.2015.248798
Exploiting hidden block sparsity: Interdependent matching pursuit for cyclic feature detection
In this paper, we propose a novel Compressive Sensing (CS)-enhanced spectrum sensing approach for Cognitive Radio (CR) systems. The new framework enables cyclic feature detection with a significantly reduced sampling rate. We associate the new framework with a novel model-based greedy reconstruction algorithm: interdependent matching pursuit (IMP). For IMP, the hidden block sparsity owing to the symmetry present in the cyclic spectrum is exploited which effectively reduces the degree of freedom of problem. Compared with conventional CS with independent support selection, a remarkable spectrum reconstruction improvement is achieved by IMP.The work of Wei Chen is supported by the State Key Laboratory of Rail Traffic Control and Safety (No. RCS2012ZT014), Beijing Jiaotong University, and the Key grant Project of Chinese Ministry of Education (No.313006).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/GLOCOM.2013.683122
Dictionary design for distributed compressive sensing
Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity for a class of signals. In distributed compressive sensing (DCS), in addition to intra-signal correlation, inter-signal correlation is also exploited in the joint signal reconstruction, which goes beyond the aim of the conventional dictionary learning framework. In this letter, we propose a new dictionary learning framework in order to improve signal reconstruction performance in DCS applications. By capitalizing on the sparse common component and innovations (SCCI) model [1], which captures both intra- and inter-signal correlation, the proposed method iteratively finds a dictionary design that
promotes various goals: i) signal representation; ii) intra-signal correlation; and iii) inter-signal correlation. Simulation results show that our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs.This work is supported by EPSRC Research Grant EP/K033700/1 and EP/K033166/1, the Fundamental Research Funds for the Central Universities (No. 2014JBM149), the State Key Laboratory of Rail Traffic Control and Safety (RCS2012ZT014) of Beijing Jiaotong University, the Natural Science Foundation of China (U1334202), the Key Grant Project of Chinese Ministry of Education (313006).This is the published manuscript. It is freely available online from the IEEE website here: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6880772. © 2014 IEE
On the energy self-sustainability of IoT via distributed compressed sensing
This paper advocates the use of the distributed compressed sensing (DCS)
paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for
energy self-sustainability. We consider networks with signal/energy models that
capture the fact that both the collected signals and the harvested energy of
different devices can exhibit correlation. We provide theoretical analysis on
the performance of both the classical compressive sensing (CS) approach and the
proposed distributed CS (DCS)-based approach to data acquisition for EH IoT.
Moreover, we perform an in-depth comparison of the proposed DCS-based approach
against the distributed source coding (DSC) system. These performance
characterizations and comparisons embody the effect of various system phenomena
and parameters including signal correlation, EH correlation, network size, and
energy availability level. Our results unveil that, the proposed approach
offers significant increase in data gathering capability with respect to the
CS-based approach, and offers a substantial reduction of the mean-squared error
distortion with respect to the DSC system
Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm
We propose a novel dynamic reweighted â„“2 (DRâ„“2) algorithm in the regime of dynamic compressive sensing. Our analysis shows that aiming to solve a Type II optimization problem, DRâ„“2 is effectively minimizing a `convex-concave' penalty in the coefficients that transitions from a convex region to a concave function using knowledge of past estimations. DRâ„“2 thus provides superior reconstruction performance compared with state-of-the-art dynamic CS algorithms.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICASSP.2014.685422
On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing
This paper advocates the use of the distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for energy self-sustainability. We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation. We provide theoretical analysis on the performance of both the classical compressive sensing (CS) approach and the proposed distributed CS (DCS)-based approach to data acquisition for EH IoT. Moreover, we perform an in-depth comparison of the proposed DCS- based approach against the distributed source coding (DSC) system. These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach, and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system
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