Linear Coherent Decentralized Estimation

Jian, Xi Gao; Cui, Shuguang; Luo, Zhi-Quan; Goldsmith, Andrea

doi:10.1109/tsp.2007.906762

Cited by 269 publications

(394 citation statements)

References 28 publications

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“…Depending on how severe the fading is and how much power is available to counteract it, packets will be dropped more or less frequently. Recently, energy issues have attracted significant attention in the wireless sensor networks literature; see, e.g., Björnemo (2009);Wen et al (2008); Xiao et al (2008); Björnemo et al (2007); Johansson et al (2007). For example, in Xiao et al (2008) convex optimization is used to derive optimal power scheduling for a time-invariant decentralized case.…”

Section: Introductionmentioning

confidence: 99%

On Kalman Filtering with Fading Wireless Channels Governed by Power Control

Quevedo

Åhlén

Leong

et al. 2011

IFAC Proceedings Volumes

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We study stochastic stability for Kalman filtering over fading wireless channels where variable channel gains are counteracted by the use of power control to alleviate the effects of packet drops. The Kalman filter and the controller are located at a single gateway which acquires data from the wireless sensors. We establish sufficient conditions which ensure that the Kalman filter covariance matrix is exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing optimal power allocation laws which minimize the total sensor power budget. In deriving the optimal power allocation laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.

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Section: Introductionmentioning

confidence: 99%

On Kalman Filtering with Fading Wireless Channels Governed by Power Control

Quevedo

Åhlén

Leong

et al. 2011

IFAC Proceedings Volumes

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“…The optimal power allocation strategies for distributed estimation in WSNs have received a great research interest both from analog and digital encoding perspectives [3]- [5], [9]- [13]. Among encoding schemes, the uncoded amplify-and-forward scheme has been extensively studied due to its simplicity and information-theoretic-optimality properties under certain conditions [14].…”

Section: A Previous Workmentioning

confidence: 99%

“…In particular, the authors in [9] studied power allocation for orthogonal multiple access channels (MACs), when the best linear unbiased estimator (BLUE) is adopted. The same problem was considered in [3] for a coherent MAC. The effects of channel estimation error were reported in [10] for orthogonal MACs adopting a linear minimum meansquared error estimator, while in [13], the sensing noise uncertainty was investigated by adopting the BLUE.…”

Section: A Previous Workmentioning

confidence: 99%

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Vien¹,

Shin²,

Ishibashi³

2017

IEEE J. Sel. Top. Signal Process.

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This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplifyand-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the non-convex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for colocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC. Index TermsAmplify-and-forwarding, best linear unbiased estimator (BLUE), distributed estimation, mean-squared error (MSE), wireless power transfer (WPT).

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“…One popular technique is analog amplify and forward [4], where sensors transmit over fading channels a scaled version of their analog measurements to a fusion center (FC), and it has been shown to be optimal in some situations [5]. Analog forwarding has been investigated under different multiple access schemes such as coherent multiple access [4], [6], which is the focus of the current work, and orthogonal multiple access [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO for decentralized estimation over coherent multiple access channels

Shirazinia

Dey

Ciuonzo

et al. 2015

2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Abstract-We consider a decentralized multisensor estimation problem where L sensor nodes observe noisy versions of a possibly correlated random source. The sensors amplify and forward their observations over a fading coherent multiple access channel (MAC) to a fusion center (FC). The FC is equipped with a large array of N antennas, and adopts a minimum mean square error (MMSE) approach for estimating the source. We optimize the amplification factor (or equivalently transmission power) at each sensor node in two different scenarios: 1) with the objective of total power minimization subject to mean square error (MSE) of source estimation constraint, and 2) with the objective of minimizing MSE subject to total power constraint. For this purpose, we apply an asymptotic approximation based on the massive multiple-inputmultiple-output (MIMO) favorable propagation condition (when L ≪ N ). We use convex optimization techniques to solve for the optimal sensor power allocation in 1) and 2). In 1), we show that the total power consumption at the sensors decay as 1/N , replicating the power savings obtained in Massive MIMO mobile communications literature. Through numerical studies, we also illustrate the superiority of the proposed optimal power allocation methods over uniform power allocation.

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Linear Coherent Decentralized Estimation

Cited by 269 publications

References 28 publications

On Kalman Filtering with Fading Wireless Channels Governed by Power Control

On Kalman Filtering with Fading Wireless Channels Governed by Power Control

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Massive MIMO for decentralized estimation over coherent multiple access channels

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