49 research outputs found
Source-Channel Communication in Sensor Networks
Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed version to the interested party, the goal being not to make any errors. This coding paradigm is inspired by Shannon’s separation theorem for point-to-point communication, but it leads to suboptimal performance in general network topologies. The optimal performance for the general case is not known. In this paper, we propose an alternative coding paradigm based on joint source-channel coding. This coding paradigm permits to determine the optimal performance for a class of sensor networks, and shows how to achieve it. For sensor networks outside this class, we argue that the goal of the coding system could be to approach our condition for op- timal performance as closely as possible. This is supported by examples for which our coding paradigm significantly outperforms the traditional separation-based coding paradigm. In particular, for a Gaussian exam- ple considered in this paper, the distortion of the best coding scheme according to the separation paradigm decreases like 1/logM, while for our coding paradigm, it decreases like 1/M, where M is the total number of sensors
Simultaneous Sparse Approximation Using an Iterative Method with Adaptive Thresholding
This paper studies the problem of Simultaneous Sparse Approximation (SSA).
This problem arises in many applications which work with multiple signals
maintaining some degree of dependency such as radar and sensor networks. In
this paper, we introduce a new method towards joint recovery of several
independent sparse signals with the same support. We provide an analytical
discussion on the convergence of our method called Simultaneous Iterative
Method with Adaptive Thresholding (SIMAT). Additionally, we compare our method
with other group-sparse reconstruction techniques, i.e., Simultaneous
Orthogonal Matching Pursuit (SOMP), and Block Iterative Method with Adaptive
Thresholding (BIMAT) through numerical experiments. The simulation results
demonstrate that SIMAT outperforms these algorithms in terms of the metrics
Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIMAT is
considerably less complicated than BIMAT, which makes it feasible for practical
applications such as implementation in MIMO radar systems
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
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
Estimation in Phase-Shift and Forward Wireless Sensor Networks
We consider a network of single-antenna sensors that observe an unknown
deterministic parameter. Each sensor applies a phase shift to the observation
and the sensors simultaneously transmit the result to a multi-antenna fusion
center (FC). Based on its knowledge of the wireless channel to the sensors, the
FC calculates values for the phase factors that minimize the variance of the
parameter estimate, and feeds this information back to the sensors. The use of
a phase-shift-only transmission scheme provides a simplified analog
implementation at the sensor, and also leads to a simpler algorithm design and
performance analysis. We propose two algorithms for this problem, a numerical
solution based on a relaxed semidefinite programming problem, and a closed-form
solution based on the analytic constant modulus algorithm. Both approaches are
shown to provide performance close to the theoretical bound. We derive
asymptotic performance analyses for cases involving large numbers of sensors or
large numbers of FC antennas, and we also study the impact of phase errors at
the sensor transmitters. Finally, we consider the sensor selection problem, in
which only a subset of the sensors is chosen to send their observations to the
FC.Comment: 28 pages, 5 figures, accepted by IEEE Transactions on Signal
Processing, Apr. 201
Robust Distributed Estimation over Multiple Access Channels with Constant Modulus Signaling
A distributed estimation scheme where the sensors transmit with constant
modulus signals over a multiple access channel is considered. The proposed
estimator is shown to be strongly consistent for any sensing noise distribution
in the i.i.d. case both for a per-sensor power constraint, and a total power
constraint. When the distributions of the sensing noise are not identical, a
bound on the variances is shown to establish strong consistency. The estimator
is shown to be asymptotically normal with a variance (AsV) that depends on the
characteristic function of the sensing noise. Optimization of the AsV is
considered with respect to a transmission phase parameter for a variety of
noise distributions exhibiting differing levels of impulsive behavior. The
robustness of the estimator to impulsive sensing noise distributions such as
those with positive excess kurtosis, or those that do not have finite moments
is shown. The proposed estimator is favorably compared with the amplify and
forward scheme under an impulsive noise scenario. The effect of fading is shown
to not affect the consistency of the estimator, but to scale the asymptotic
variance by a constant fading penalty depending on the fading statistics.
Simulations corroborate our analytical results.Comment: 28 pages, 10 figures, submitted to IEEE Transactions on Signal
Processing for consideratio
One-bit Distributed Sensing and Coding for Field Estimation in Sensor Networks
This paper formulates and studies a general distributed field reconstruction
problem using a dense network of noisy one-bit randomized scalar quantizers in
the presence of additive observation noise of unknown distribution. A
constructive quantization, coding, and field reconstruction scheme is developed
and an upper-bound to the associated mean squared error (MSE) at any point and
any snapshot is derived in terms of the local spatio-temporal smoothness
properties of the underlying field. It is shown that when the noise, sensor
placement pattern, and the sensor schedule satisfy certain weak technical
requirements, it is possible to drive the MSE to zero with increasing sensor
density at points of field continuity while ensuring that the per-sensor
bitrate and sensing-related network overhead rate simultaneously go to zero.
The proposed scheme achieves the order-optimal MSE versus sensor density
scaling behavior for the class of spatially constant spatio-temporal fields.Comment: Fixed typos, otherwise same as V2. 27 pages (in one column review
format), 4 figures. Submitted to IEEE Transactions on Signal Processing.
Current version is updated for journal submission: revised author list,
modified formulation and framework. Previous version appeared in Proceedings
of Allerton Conference On Communication, Control, and Computing 200