On Optimal Causal Coding of Partially Observed Markov Sources in Single and Multiterminal Settings

Yüksel, Serdar

doi:10.1109/tit.2012.2212874

Cited by 35 publications

(5 citation statements)

References 62 publications

(180 reference statements)

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“…This method has been used to identify globally optimal strategies for specific information structures (e.g., stochastically nested information structures [21] and broadcast information structures [36]) and for various applications (e.g., real-time communication [37]- [42], decentralized hypothesis testing and quickest change detection [35], [43]- [49], and networked control systems [50], [51]). The person-by-person approach has also been used to identify person-by-person optimal control strategies for specific information structures (e.g., control sharing information structure [52]).…”

Section: Dynamic Programming Approach To Team Problems and Limitedmentioning

confidence: 99%

Information structures in optimal decentralized control

Mahajan

Martins

Rotkowitz

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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153

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Abstract-This tutorial paper provides a comprehensive characterization of information structures in team decision problems and their impact on the tractability of team optimization. Solution methods for team decision problems are presented in various settings where the discussion is structured in two foci: The first is centered on solution methods for stochastic teams admitting state-space formulations. The second focus is on norm-optimal control for linear plants under information constraints.

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Section: Dynamic Programming Approach To Team Problems and Limitedmentioning

confidence: 99%

Information structures in optimal decentralized control

Mahajan

Martins

Rotkowitz

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

Self Cite

136

153

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“…We wish to highlight the fact that our results for the infinite horizon case do not directly follow from existing results on partially observable Markov decision processes, because in a partially observable Markov decision process the state and the actions as well as the probability measure-valued expanded state and the action always constitute a controlled Markov chain. This is not the case in the problem considered; a quantizer and the state constructed in this paper do not form a controlled Markov chain under an arbitrary quantizer policy; see [15] for further discussions on this topic.…”

Section: A Revisiting Structural Results For Finite-horizon Problemsmentioning

confidence: 97%

Optimality of Walrand-Varaiya type policies and approximation results for zero delay coding of Markov sources

Wood

Linder

Yüksel

2015

2015 IEEE International Symposium on Information Theory (ISIT)

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Abstract-Optimal zero-delay coding (quantization) of a finitestate Markov source is considered. Building on our earlier work and previous literature, using a stochastic control problem formulation, the existence and structure of optimal quantization policies are studied. Our main result establishes, for infinite horizon problems, the optimality of deterministic and stationary (Walrand-Varaiya type) Markov coding policies. In addition, the -optimality of finite-memory quantizers is established and the dependence between the memory length and is quantified. Numerical results are also presented.

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“…There are various structural results for such problems, primarily for control-free sources; see [25,27,49,52,53,58] among others. In the following, we consider the case with control, which have been considered for finite-alphabet source and control action spaces in [51] and [27].…”

Section: Dynamic Channel and Optimal Vector Quantizationmentioning

confidence: 99%

Design of Information Channels for Optimization and Stabilization in Networked Control

Yüksel

2014

Information and Control in Networks

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In stochastic control, typically a partial observation model/channel is given and one looks for a control policy for optimization or stabilization. Consider a single-agent dynamical system described by the discrete-time equations1)for (Borel measurable) functions f, g, with {w t } being an independent and identically distributed (i.i.d.) system noise process and {v t } an i.i.d. measurement disturbance process, which are independent of x 0 and each other. Here, x t ∈ X, y t ∈ Y, u t ∈ U, where we assume that these spaces are Borel subsets of finite dimensional Euclidean spaces. In (6.2), we can view g as inducing a measurement channel Q, which is a stochastic kernel or a regular conditional probability measure from X to Y in the sense that Q(·|x) is a probability measure on the (Borel) σ -algebra B(Y) on Y for every x ∈ X, and Q(A|·) : ] is a Borel measurable function for every A ∈ B(Y).In networked control systems, the observation channel described above itself is also subject to design. In a more general setting, we can shape the channel input by coding and decoding. This chapter is concerned with design and optimization of such channels.We will consider a controlled Markov model given by (6.1). The observation channel model is described as follows: This system is connected over a noisy channel with a finite capacity to a controller, as shown in Fig. 6.1. The controller has

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On Optimal Causal Coding of Partially Observed Markov Sources in Single and Multiterminal Settings

Cited by 35 publications

References 62 publications

Information structures in optimal decentralized control

Information structures in optimal decentralized control

Optimality of Walrand-Varaiya type policies and approximation results for zero delay coding of Markov sources

Design of Information Channels for Optimization and Stabilization in Networked Control

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