On the Structure of Optimal Real-Time Encoders and Decoders in Noisy Communication

Teneketzis, Demosthenis

doi:10.1109/tit.2006.880067

Cited by 104 publications

(129 citation statements)

References 49 publications

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“…Due to space restrictions, we are unable to provide a detailed account of the literature; see [3], [4] and [5] for a review of some representative work.…”

Section: A Revisiting Structural Results For Finite-horizon Problemsmentioning

confidence: 99%

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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“…Due to space restrictions, we are unable to provide a detailed account of the literature; see [3], [4] and [5] for a review of some representative work.…”

Section: A Revisiting Structural Results For Finite-horizon Problemsmentioning

confidence: 99%

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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“…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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“…Applications of results developed in [30] appeared in [31] and [32]. Structural properties of optimal real-time encoding and decoding strategies for systems with Markov source, noisy channels with no feedback and finite memory at the receiver were presented in [33]. Such structural properties are derived by assuming that either the encoder or the decoder is fixed, considering the communication problem at the other end as a stochastic optimization problem, and identifying an information state sufficient for performance analysis.…”

Section: B Literature Overviewmentioning

confidence: 99%

“…However, optimality equations that determine optimal encoders and decoders for real-time joint source-channel coding with no feedback are unknown. In this paper, we use the structural results of [33] to obtain such optimality equations.…”

Section: B Literature Overviewmentioning

confidence: 99%

Optimal Design of Sequential Real-Time Communication Systems

Mahajan

Teneketzis

2009

IEEE Trans. Inform. Theory

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Abstract-Optimal design of sequential real-time communication of a Markov source over a noisy channel is investigated. In such a system, the delay between the source output and its reconstruction at the receiver should equal a fixed prespecified amount. An optimal communication strategy must minimize the total expected symbol-by-symbol distortion between the source output and its reconstruction. Design techniques or performance bounds for such real-time communication systems are unknown. In this paper a systematic methodology, based on the concepts of information structures and information states, to search for an optimal real-time communication strategy is presented. This methodology trades off complexity in communication length (linear in contrast to doubly exponential) with complexity in alphabet sizes (doubly exponential in contrast to exponential). As the communication length is usually order of magnitudes bigger than the alphabet sizes, the proposed methodology simplifies the search for an optimal communication strategy. In spite of this simplification, the resultant optimality equations cannot be solved efficiently using existing algorithmic techniques. The main idea is to formulate a zero-delay communication problem as a dynamic team with nonclassical information structure. Then, an appropriate choice of information states converts the dynamic team problem into a centralized stochastic control problem in function space. Thereafter, Markov decision theory is used to derive nested optimality equations for choosing an optimal design. For infinite horizon problems, these optimality equations give rise to a fixed point functional equation. Communication systems with fixed finite delay constraint, a higher-order Markov source, and channels with memory are treated in the same manner after an appropriate expansion of the state space. Thus, this paper presents a comprehensive methodology to study different variations of real-time communication.

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On the Structure of Optimal Real-Time Encoders and Decoders in Noisy Communication

Cited by 104 publications

References 49 publications

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

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

Optimal Design of Sequential Real-Time Communication Systems

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