3,462 research outputs found
Optimal Power Allocation for Parameter Tracking in a Distributed Amplify-and-Forward Sensor Network
We consider the problem of optimal power allocation in a sensor network where
the sensors observe a dynamic parameter in noise and coherently amplify and
forward their observations to a fusion center (FC). The FC uses the
observations in a Kalman filter to track the parameter, and we show how to find
the optimal gain and phase of the sensor transmissions under both global and
individual power constraints in order to minimize the mean squared error (MSE)
of the parameter estimate. For the case of a global power constraint, a
closed-form solution can be obtained. A numerical optimization is required for
individual power constraints, but the problem can be relaxed to a semidefinite
programming problem (SDP), and we show that the optimal result can be
constructed from the SDP solution. We also study the dual problem of minimizing
global and individual power consumption under a constraint on the MSE. As
before, a closed-form solution can be found when minimizing total power, while
the optimal solution is constructed from the output of an SDP when minimizing
the maximum individual sensor power. For purposes of comparison, we derive an
exact expression for the outage probability on the MSE for equal-power
transmission, which can serve as an upper bound for the case of optimal power
control. Finally, we present the results of several simulations to show that
the use of optimal power control provides a significant reduction in either MSE
or transmit power compared with a non-optimized approach (i.e., equal power
transmission).Comment: 28 pages, 6 figures, accepted by IEEE Transactions on Signal
Processing, Jan. 201
Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation
This paper investigates the problem of distributed best linear unbiased
estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless
sensor network (WSN). In particular, the application of limited-feedback
strategies for the optimal power allocation in distributed estimation is
studied. In order to find the BLUE estimator of the unknown parameter, the FC
combines spatially distributed, linearly processed, noisy observations of local
sensors received through orthogonal channels corrupted by fading and additive
Gaussian noise. Most optimal power-allocation schemes proposed in the
literature require the feedback of the exact instantaneous channel state
information from the FC to local sensors. This paper proposes a
limited-feedback strategy in which the FC designs an optimal codebook
containing the optimal power-allocation vectors, in an iterative offline
process, based on the generalized Lloyd algorithm with modified distortion
functions. Upon observing a realization of the channel vector, the FC finds the
closest codeword to its corresponding optimal power-allocation vector and
broadcasts the index of the codeword. Each sensor will then transmit its analog
observations using its optimal quantized amplification gain. This approach
eliminates the requirement for infinite-rate digital feedback links and is
scalable, especially in large WSNs.Comment: 5 Pages, 3 Figures, 1 Algorithm, Forty Seventh Annual Asilomar
Conference on Signals, Systems, and Computers (ASILOMAR 2013
Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI
This paper investigates the problem of adaptive power allocation for
distributed best linear unbiased estimation (BLUE) of a random parameter at the
fusion center (FC) of a wireless sensor network (WSN). An optimal
power-allocation scheme is proposed that minimizes the -norm of the vector
of local transmit powers, given a maximum variance for the BLUE estimator. This
scheme results in the increased lifetime of the WSN compared to similar
approaches that are based on the minimization of the sum of the local transmit
powers. The limitation of the proposed optimal power-allocation scheme is that
it requires the feedback of the instantaneous channel state information (CSI)
from the FC to local sensors, which is not practical in most applications of
large-scale WSNs. In this paper, a limited-feedback strategy is proposed that
eliminates this requirement by designing an optimal codebook for the FC using
the generalized Lloyd algorithm with modified distortion metrics. Each sensor
amplifies its analog noisy observation using a quantized version of its optimal
amplification gain, which is received by the FC and used to estimate the
unknown parameter.Comment: 6 pages, 3 figures, to appear at the IEEE Military Communications
Conference (MILCOM) 201
Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors
In this paper, we design an optimal sensor collaboration strategy among
neighboring nodes while tracking a time-varying parameter using wireless sensor
networks in the presence of imperfect communication channels. The sensor
network is assumed to be self-powered, where sensors are equipped with energy
harvesters that replenish energy from the environment. In order to minimize the
mean square estimation error of parameter tracking, we propose an online sensor
collaboration policy subject to real-time energy harvesting constraints. The
proposed energy allocation strategy is computationally light and only relies on
the second-order statistics of the system parameters. For this, we first
consider an offline non-convex optimization problem, which is solved exactly
using semidefinite programming. Based on the offline solution, we design an
online power allocation policy that requires minimal online computation and
satisfies the dynamics of energy flow at each sensor. We prove that the
proposed online policy is asymptotically equivalent to the optimal offline
solution and show its convergence rate and robustness. We empirically show that
the estimation performance of the proposed online scheme is better than that of
the online scheme when channel state information about the dynamical system is
available in the low SNR regime. Numerical results are conducted to demonstrate
the effectiveness of our approach
Massive MIMO for Wireless Sensing with a Coherent Multiple Access Channel
We consider the detection and estimation of a zero-mean Gaussian signal in a
wireless sensor network with a coherent multiple access channel, when the
fusion center (FC) is configured with a large number of antennas and the
wireless channels between the sensor nodes and FC experience Rayleigh fading.
For the detection problem, we study the Neyman-Pearson (NP) Detector and Energy
Detector (ED), and find optimal values for the sensor transmission gains. For
the NP detector which requires channel state information (CSI), we show that
detection performance remains asymptotically constant with the number of FC
antennas if the sensor transmit power decreases proportionally with the
increase in the number of antennas. Performance bounds show that the benefit of
multiple antennas at the FC disappears as the transmit power grows. The results
of the NP detector are also generalized to the linear minimum mean squared
error estimator. For the ED which does not require CSI, we derive optimal gains
that maximize the deflection coefficient of the detector, and we show that a
constant deflection can be asymptotically achieved if the sensor transmit power
scales as the inverse square root of the number of FC antennas. Unlike the NP
detector, for high sensor power the multi-antenna ED is observed to empirically
have significantly better performance than the single-antenna implementation. A
number of simulation results are included to validate the analysis.Comment: 32 pages, 6 figures, accepted by IEEE Transactions on Signal
Processing, Feb. 201
Detection in Analog Sensor Networks with a Large Scale Antenna Fusion Center
We consider the distributed detection of a zero-mean Gaussian signal in an
analog wireless sensor network with a fusion center (FC) configured with a
large number of antennas. The transmission gains of the sensor nodes are
optimized by minimizing the ratio of the log probability of detection (PD) and
log probability of false alarm (PFA). We show that the problem is convex with
respect to the squared norm of the transmission gains, and that a closed-form
solution can be found using the Karush-Kuhn-Tucker conditions. Our results
indicate that a constant PD can be maintained with decreasing sensor transmit
gain provided that the number of antennas increases at the same rate. This is
contrasted with the case of a single-antenna FC, where PD is monotonically
decreasing with transmit gain. On the other hand, we show that when the
transmit power is high, the single- and multi-antenna FC both asymptotically
achieve the same PD upper bound.Comment: 4 pages, 2 figures, accepted by the 8th IEEE Sensor Array and
Multichannel Signal Processing Workshop (SAM), Apr. 201
On the Effect of Correlated Measurements on the Performance of Distributed Estimation
We address the distributed estimation of an unknown scalar parameter in
Wireless Sensor Networks (WSNs). Sensor nodes transmit their noisy observations
over multiple access channel to a Fusion Center (FC) that reconstructs the
source parameter. The received signal is corrupted by noise and channel fading,
so that the FC objective is to minimize the Mean-Square Error (MSE) of the
estimate. In this paper, we assume sensor node observations to be correlated
with the source signal and correlated with each other as well. The correlation
coefficient between two observations is exponentially decaying with the
distance separation. The effect of the distance-based correlation on the
estimation quality is demonstrated and compared with the case of unity
correlated observations. Moreover, a closed-form expression for the outage
probability is derived and its dependency on the correlation coefficients is
investigated. Numerical simulations are provided to verify our analytic
results.Comment: 5 page
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