A distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel is considered. When the sensor measurements are decreasingly reliable as a function of the sensor index, the conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variances of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify-and-forward technique and its robustness to impulsive sensing noise distributions is highlighted. In contrast to amplify-and-forward schemes, it is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results.

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

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This paper describes the development of an energy-efficient amplify-and-forward distributed estimation scheme using realistic amplifier models. Specifically, a novel algorithm is presented that enables distributed estimation in the presence of amplifier compression resulting from the energy-efficient but non-linear class AB operation. In this system, a digital predistortion scheme is utilized to fit the amplifier at each sensor to a mathematically tractable, soft compression function that roughl...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

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A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covarian...

Last. Xiaodong Wang(Columbia University)H-Index: 123

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We consider decentralized detection through distributed sensors that perform level-triggered sampling and communicate with a fusion center (FC) via noisy channels. Each sensor computes its local log-likelihood ratio (LLR), samples it using the level-triggered sampling, and upon sampling transmits a single bit to the FC. Upon receiving a bit from a sensor, the FC updates the global LLR and performs a sequential probability ratio test (SPRT) step. We derive the fusion rules under various types of ...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

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A sensor network is used for distributed signal-to-noise ratio (SNR) estimation in a single-time snapshot. Sensors observe a signal embedded in noise, and each observation is phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the mean and variance are estimated jointly, using an asymptotically minimum-variance estimator. It is shown that this joint estimator decouples into simple individual estimators ...

We propose a methodology for designing the local mapping rule for fully synchronized but energy-limited sensors in a distributed detection system, where sensors communicate with the fusion center over multiaccess channels. Using the proposed methodology, we come up with the modified detect-and-forward scheme and the modified amplify-and-forward scheme. The performance of the two schemes is analyzed. We show that optimizing the local mapping rule can lead to a larger error exponent under total po...

#1K S Jithin(IISc: Indian Institute of Science)H-Index: 2

#2Vinod Sharma(IISc: Indian Institute of Science)H-Index: 22

This paper considers sequential hypothesis testing in a decentralized framework. We start with two simple decentralized sequential hypothesis testing algorithms. One of which is later proved to be asymptotically Bayes optimal. We also consider composite versions of decentralized sequential hypothesis testing. A novel nonparametric version for decentralized sequential hypothesis testing using universal source coding theory is developed. Finally we design a simple decentralized multihypothesis seq...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

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A sensor network is used for distributed SNR estimation. Sensors observe a signal embedded in noise. These observations are phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the location parameter and scale parameter are estimated using a minimum-variance estimator as well as computationally simple alternatives. Performance evaluated using the asymptotic variance of the estimators shows that the compu...

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radi...

A distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise di...

The paper deals with the problem of data transmission and function computation of the sensed data in wireless sensor networks, in which multiple sensor nodes transmit their data to one sink node over a wireless multiple-access channel. We focus on the problem of computing the geometric mean at the sink node by merging the data transmission and function computation into one step via an explicit utilization of channel collisions caused by simultaneous transmissions of sensor nodes. The paper provi...

In this paper, we aim to design the optimal sensor collaboration strategy for the estimation of time-varying parameters, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. We begin by addressing the sensor collaboration problem for the estimation of uncorrelated parameters. We show that the resulting collaboration problem can be transformed into a special nonconvex optimization problem, where a difference of convex fun...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

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This paper presents the use of nonlinear distributed estimation in a wireless system transmitting over channels with random gains. Specifically, we discuss the development of estimators and analytically determine their attainable variance for two conditions: 1) when full channel state information (CSI) is available at the transmitter and receiver; and 2) when only channel gain statistics and phase information are available. For the case where full CSI is available, we formulate an optimization p...

We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman–Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Con...

We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with circularly-symmetric complex Gaussian distribution under the Neyman-Pearson framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considerin...

A nonparametric distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up is proposed. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain or the...

Last. Iain B. Collings(CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 45

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We develop new and optimal algorithms for distributed detection in sensor networks over fading channels with multiple receive antennas at the Fusion Centre (FC). Sensors observe a hidden physical phenomenon over fading channels and transmit their observations using the amplify-and-forward scheme over fading channels to the FC which is equipped with multiple antennas. We derive the optimal decision rules and the associated probabilities of detection and false alarm for three scenarios of Channel ...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

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This paper describes the development of an energy-efficient amplify-and-forward distributed estimation scheme using realistic amplifier models. Specifically, a novel algorithm is presented that enables distributed estimation in the presence of amplifier compression resulting from the energy-efficient but non-linear class AB operation. In this system, a digital predistortion scheme is utilized to fit the amplifier at each sensor to a mathematically tractable, soft compression function that roughl...