39 research outputs found
Optimal Quantization in Energy-Constrained Sensor Networks under Imperfect Transmission
This paper addresses the optimization of quantization at local sensors under strict energy constraint and imperfect transmission to improve the reconstruction performance at the fusion center in the wireless sensor networks (WSNs). We present optimized quantization scheme including the optimal quantization bit rate and the optimal transmission power allocation among quantization bits for BPSK signal and binary orthogonal signal with envelope detection, respectively. The optimization of the quantization is formulated as a convex problem and the optimal solution is derived analytically in both cases. Simulation results demonstrate the effectiveness of our proposed quantization schemes
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
Estimation Diversity and Energy Efficiency in Distributed Sensing
Distributed estimation based on measurements from multiple wireless sensors
is investigated. It is assumed that a group of sensors observe the same
quantity in independent additive observation noises with possibly different
variances. The observations are transmitted using amplify-and-forward (analog)
transmissions over non-ideal fading wireless channels from the sensors to a
fusion center, where they are combined to generate an estimate of the observed
quantity. Assuming that the Best Linear Unbiased Estimator (BLUE) is used by
the fusion center, the equal-power transmission strategy is first discussed,
where the system performance is analyzed by introducing the concept of
estimation outage and estimation diversity, and it is shown that there is an
achievable diversity gain on the order of the number of sensors. The optimal
power allocation strategies are then considered for two cases: minimum
distortion under power constraints; and minimum power under distortion
constraints. In the first case, it is shown that by turning off bad sensors,
i.e., sensors with bad channels and bad observation quality, adaptive power
gain can be achieved without sacrificing diversity gain. Here, the adaptive
power gain is similar to the array gain achieved in Multiple-Input
Single-Output (MISO) multi-antenna systems when channel conditions are known to
the transmitter. In the second case, the sum power is minimized under
zero-outage estimation distortion constraint, and some related energy
efficiency issues in sensor networks are discussed.Comment: To appear at IEEE Transactions on Signal Processin
Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators
Optimal and suboptimal decentralized estimators in wireless sensor networks
(WSNs) over orthogonal multiple-access fading channels are studied in this
paper. Considering multiple-bit quantization before digital transmission, we
develop maximum likelihood estimators (MLEs) with both known and unknown
channel state information (CSI). When training symbols are available, we derive
a MLE that is a special case of the MLE with unknown CSI. It implicitly uses
the training symbols to estimate the channel coefficients and exploits the
estimated CSI in an optimal way. To reduce the computational complexity, we
propose suboptimal estimators. These estimators exploit both signal and data
level redundant information to improve the estimation performance. The proposed
MLEs reduce to traditional fusion based or diversity based estimators when
communications or observations are perfect. By introducing a general message
function, the proposed estimators can be applied when various analog or digital
transmission schemes are used. The simulations show that the estimators using
digital communications with multiple-bit quantization outperform the estimator
using analog-and-forwarding transmission in fading channels. When considering
the total bandwidth and energy constraints, the MLE using multiple-bit
quantization is superior to that using binary quantization at medium and high
observation signal-to-noise ratio levels