34,670 research outputs found
Optimal Asymmetric Binary Quantization for Estimation Under Symmetrically Distributed Noise
Estimation of a location parameter based on noisy and binary quantized
measurements is considered in this letter. We study the behavior of the
Cramer-Rao bound as a function of the quantizer threshold for different
symmetric unimodal noise distributions. We show that, in some cases, the
intuitive choice of threshold position given by the symmetry of the problem,
placing the threshold on the true parameter value, can lead to locally worst
estimation performance.Comment: 4 pages, 5 figure
Distributed Estimation of a Parametric Field Using Sparse Noisy Data
The problem of distributed estimation of a parametric physical field is
stated as a maximum likelihood estimation problem. Sensor observations are
distorted by additive white Gaussian noise. Prior to data transmission, each
sensor quantizes its observation to levels. The quantized data are then
communicated over parallel additive white Gaussian channels to a fusion center
for a joint estimation. An iterative expectation-maximization (EM) algorithm to
estimate the unknown parameter is formulated, and its linearized version is
adopted for numerical analysis. The numerical examples are provided for the
case of the field modeled as a Gaussian bell. The dependence of the integrated
mean-square error on the number of quantization levels, the number of sensors
in the network and the SNR in observation and transmission channels is
analyzed.Comment: to appear at Milcom-201
A Joint Model and Data Driven Method for Distributed Estimation
This paper considers the problem of distributed estimation in wireless sensor
networks (WSN), which is anticipated to support a wide range of applications
such as the environmental monitoring, weather forecasting, and location
estimation. To this end, we propose a joint model and data driven distributed
estimation method by designing the optimal quantizers and fusion center (FC)
based on the Bayesian and minimum mean square error (MMSE) criterions. First,
universal mean square error (MSE) lower bound for the quantization-based
distributed estimation is derived and adopted as the design metric for the
quantizers. Then, the optimality of the mean-fusion operation for the FC with
MMSE criterion is proved. Next, by exploiting different levels of the statistic
information of the desired parameter and observation noise, a joint model and
data driven method is proposed to train parts of the quantizer and FC modules
as deep neural networks (DNNs), and two loss functions derived from the MMSE
criterion are adopted for the sequential training scheme. Furthermore, we
extend the above results to the case with multi-bit quantizers, considering
both the parallel and one-hot quantization schemes. Finally, simulation results
reveal that the proposed method outperforms the state-of-the-art schemes in
typical scenarios.Comment: in IEEE Internet of Things Journa
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by HPC applications,
significantly reducing data size is critical because of limited capacity of
storage space and potential bottlenecks on I/O or networks in writing/reading
or transferring data. SZ and ZFP are the two leading lossy compressors
available to compress scientific data sets. However, their performance is not
consistent across different data sets and across different fields of some data
sets: for some fields SZ provides better compression performance, while other
fields are better compressed with ZFP. This situation raises the need for an
automatic online (during compression) selection between SZ and ZFP, with a
minimal overhead. In this paper, the automatic selection optimizes the
rate-distortion, an important statistical quality metric based on the
signal-to-noise ratio. To optimize for rate-distortion, we investigate the
principles of SZ and ZFP. We then propose an efficient online, low-overhead
selection algorithm that predicts the compression quality accurately for two
compressors in early processing stages and selects the best-fit compressor for
each data field. We implement the selection algorithm into an open-source
library, and we evaluate the effectiveness of our proposed solution against
plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results
on three data sets representing about 100 fields show that our selection
algorithm improves the compression ratio up to 70% with the same level of data
distortion because of very accurate selection (around 99%) of the best-fit
compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio
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