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Correction to 'On the error probability for a class of binary recursive feedback strategies'
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A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information
Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German
Optimal Feedback Communication via Posterior Matching
In this paper we introduce a fundamental principle for optimal communication
over general memoryless channels in the presence of noiseless feedback, termed
posterior matching. Using this principle, we devise a (simple, sequential)
generic feedback transmission scheme suitable for a large class of memoryless
channels and input distributions, achieving any rate below the corresponding
mutual information. This provides a unified framework for optimal feedback
communication in which the Horstein scheme (BSC) and the Schalkwijk-Kailath
scheme (AWGN channel) are special cases. Thus, as a corollary, we prove that
the Horstein scheme indeed attains the BSC capacity, settling a longstanding
conjecture. We further provide closed form expressions for the error
probability of the scheme over a range of rates, and derive the achievable
rates in a mismatch setting where the scheme is designed according to the wrong
channel model. Several illustrative examples of the posterior matching scheme
for specific channels are given, and the corresponding error probability
expressions are evaluated. The proof techniques employed utilize novel
relations between information rates and contraction properties of iterated
function systems.Comment: IEEE Transactions on Information Theor
Functional Bipartite Ranking: a Wavelet-Based Filtering Approach
It is the main goal of this article to address the bipartite ranking issue
from the perspective of functional data analysis (FDA). Given a training set of
independent realizations of a (possibly sampled) second-order random function
with a (locally) smooth autocorrelation structure and to which a binary label
is randomly assigned, the objective is to learn a scoring function s with
optimal ROC curve. Based on linear/nonlinear wavelet-based approximations, it
is shown how to select compact finite dimensional representations of the input
curves adaptively, in order to build accurate ranking rules, using recent
advances in the ranking problem for multivariate data with binary feedback.
Beyond theoretical considerations, the performance of the learning methods for
functional bipartite ranking proposed in this paper are illustrated by
numerical experiments
Sequential Necessary and Sufficient Conditions for Capacity Achieving Distributions of Channels with Memory and Feedback
We derive sequential necessary and sufficient conditions for any channel
input conditional distribution to maximize the
finite-time horizon directed information defined by for channel distributions
and
, where
and are the
channel input and output random processes, and is a finite nonnegative
integer.
\noi We apply the necessary and sufficient conditions to application examples
of time-varying channels with memory and we derive recursive closed form
expressions of the optimal distributions, which maximize the finite-time
horizon directed information. Further, we derive the feedback capacity from the
asymptotic properties of the optimal distributions by investigating the limit
without any \'a priori
assumptions, such as, stationarity, ergodicity or irreducibility of the channel
distribution. The necessary and sufficient conditions can be easily extended to
a variety of channels with memory, beyond the ones considered in this paper.Comment: 57 pages, 9 figures, part of the paper was accepted for publication
in the proceedings of the IEEE International Symposium on Information Theory
(ISIT), Barcelona, Spain 10-15 July, 2016 (Date of submission of the
conference paper: 25/1/2016
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