Channel Aware Decision Fusion in Wireless Sensor Networks

Chen, Biao; Jiang, Ruixiang; Kasetkasem, Teerasit; Varshney, Pramod K.

doi:10.1109/tsp.2004.837404

Cited by 382 publications

(315 citation statements)

References 14 publications

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“…Such a scenario is investigated in [54], in which the corresponding optimal fusion rule of the received signals is derived. This fusion rule = P fa , and P d > P fa ), for which only the reporting channel SNRs are needed.…”

Section: B Hard Combiningmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances

Axell

Leus

Larsson

et al. 2012

IEEE Signal Process. Mag.

1,001

586

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Section: B Hard Combiningmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances

Axell

Leus

Larsson

et al. 2012

IEEE Signal Process. Mag.

1,001

586

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“…One set deals with the fusion of information observed by a group nodes at a fusion center where the communication links (between the nodes and fusion center) are either rate limited [4]- [12] or subject to channel imperfections such as fading and packet drops [13]- [15]. Our work belongs to the second set, which models the communication network as a directed graph whose vertices/nodes are agents and an edge from node i to j indicates that i may send a message to j with perfect fidelity (the link is a noiseless channel of infinite capacity).…”

Section: A Related Workmentioning

confidence: 99%

Social learning and distributed hypothesis testing

Lalitha

Sarwate

Javidi

2014

2014 IEEE International Symposium on Information Theory

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Abstract-This paper considers a problem of distributed hypothesis testing and social learning. Individual nodes in a network receive noisy local (private) observations whose distribution is parameterized by a discrete parameter (hypotheses). The marginals of the joint observation distribution conditioned on each hypothesis are known locally at the nodes, but the true parameter/hypothesis is not known. An update rule is analyzed in which nodes first perform a Bayesian update of their belief (distribution estimate) of each hypothesis based on their local observations, communicate these updates to their neighbors, and then perform a "non-Bayesian" linear consensus using the log-beliefs of their neighbors. Under mild assumptions, we show that the belief of any node on a wrong hypothesis converges to zero exponentially fast, and the exponential rate of learning is characterized by the nodes' influence of the network and the divergences between the observations' distributions. For a broad class of observation statistics which includes distributions with unbounded support such as Gaussian mixtures, we show that rate of rejection of wrong hypothesis satisfies a large deviation principle i.e., the probability of sample paths on which the rate of rejection of wrong hypothesis deviates from the mean rate vanishes exponentially fast and we characterize the rate function in terms of the nodes' influence of the network and the local observation models.

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“…The channel estimation methods can be found in [19,20] and the references in them. Phase coherent reception can be accomplished either through training-based channel estimation for stationary channels or at a small cost of SNR degradation by employing differential encoding for a fast fading channel [21].…”

Section: Basic Cirmentioning

confidence: 99%

Optimized Local Superposition in Wireless Sensor Networks With T-Average-Mutual-Coherence

Guo¹,

Qu²,

Huang³

et al. 2012

PIER

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Abstract-Compressed sensing (CS) is a new technology for recovering sparse data from undersampled measurements. It shows great potential to reduce energy for sensor networks. First, a basic global superposition model is proposed to obtain the measurements of sensor data, where a sampling matrix is modeled as the channel impulse response (CIR) matrix while the sparsifying matrix is expressed as the distributed wavelet transform (DWT). However, both the sampling and sparsifying matrixes depend on the location of sensors, so this model is highly coherent. This violates the assumption of CS and easily produces high data recovery error. In this paper, in order to reduce the coherence, we propose to control the transmit power of some nodes with the help of t-average-mutual-coherence, and recovery quality are greatly improved. Finally, to make the approach more realistic and energy-efficient, the CIR superposition is restricted in local clusters. Two key parameters, the radius of power control region and the radius of local clusters, are optimized based on the coherence and resource consideration in sensor networks. Simulation results demonstrate that the proposed scheme provides a high recovery quality for networked data and verify that t-average-mutual-coherence is a good criterion for optimizing the performance of CS in our scenario.

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Channel Aware Decision Fusion in Wireless Sensor Networks

Cited by 382 publications

References 14 publications

Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances

Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances

Social learning and distributed hypothesis testing

Optimized Local Superposition in Wireless Sensor Networks With T-Average-Mutual-Coherence

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