897 research outputs found
Gossip consensus algorithms via quantized communication
This paper considers the average consensus problem on a network of digital
links, and proposes a set of algorithms based on pairwise ''gossip''
communications and updates. We study the convergence properties of such
algorithms with the goal of answering two design questions, arising from the
literature: whether the agents should encode their communication by a
deterministic or a randomized quantizer, and whether they should use, and how,
exact information regarding their own states in the update.Comment: Accepted for publicatio
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
Quantization of Prior Probabilities for Hypothesis Testing
Bayesian hypothesis testing is investigated when the prior probabilities of
the hypotheses, taken as a random vector, are quantized. Nearest neighbor and
centroid conditions are derived using mean Bayes risk error as a distortion
measure for quantization. A high-resolution approximation to the
distortion-rate function is also obtained. Human decision making in segregated
populations is studied assuming Bayesian hypothesis testing with quantized
priors
The Distributed MIMO Scenario: Can Ideal ADCs Be Replaced by Low-resolution ADCs?
This letter considers the architecture of distributed antenna system, which
is made up of a massive number of single-antenna remote radio heads (RRHs),
some with full-resolution but others with low-resolution analog-to-digital
converter (ADC) receivers. This architecture is greatly motivated by its high
energy efficiency and low-cost implementation. We derive the worst-case uplink
spectral efficiency (SE) of the system assuming a frequency-flat channel and
maximum-ratio combining (MRC), and reveal that the SE increases as the number
of quantization bits for the low-resolution ADCs increases, and the SE
converges as the number of RRHs with low-resolution ADCs grows. Our results
furthermore demonstrate that a great improvement can be obtained by adding a
majority of RRHs with low-resolution ADC receivers, if sufficient quantization
precision and an acceptable proportion of high-to-low resolution RRHs are used.Comment: 4 pages, to be published in IEEE Wireless Communications Letter
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian distributed, the conventional principal component
analysis (PCA) cannot yield mutually independent scalar variables. With the two
proposed transformations, a highly negatively correlated neutral vector can be
transformed to a set of mutually independent scalar variables with the same
degrees of freedom. We also evaluate the decorrelation performances for the
vectors generated from a single Dirichlet distribution and a mixture of
Dirichlet distributions. The mutual independence is verified with the distance
correlation measurement. The advantages of the proposed decorrelation
strategies are intensively studied and demonstrated with synthesized data and
practical application evaluations
Quantization and Compressive Sensing
Quantization is an essential step in digitizing signals, and, therefore, an
indispensable component of any modern acquisition system. This book chapter
explores the interaction of quantization and compressive sensing and examines
practical quantization strategies for compressive acquisition systems.
Specifically, we first provide a brief overview of quantization and examine
fundamental performance bounds applicable to any quantization approach. Next,
we consider several forms of scalar quantizers, namely uniform, non-uniform,
and 1-bit. We provide performance bounds and fundamental analysis, as well as
practical quantizer designs and reconstruction algorithms that account for
quantization. Furthermore, we provide an overview of Sigma-Delta
() quantization in the compressed sensing context, and also
discuss implementation issues, recovery algorithms and performance bounds. As
we demonstrate, proper accounting for quantization and careful quantizer design
has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing
and Its Applications", 201
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