On the Structure of Real-Time Source Coders

Witsenhausen, H. S.

doi:10.1002/j.1538-7305.1979.tb02263.x

Cited by 144 publications

(243 citation statements)

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“…Along these lines, for system (1)-(2), if the DMs can agree on a joint beliefs P (x t ∈ ·|I i t , i ∈ L) at every time stage, then the optimal cost that would be achieved under a centralized system could be achieved (see Yüksel and Başar (2013)). As a further important illustrative case, if the problem described in Definition 0.1 is for a realtime estimation problem for a Markov source, then the optimal causal fixed-rate coder minimizing any cost function uses only the last source symbol and the information at the controller's memory, see Witsenhausen (1979). We also note that the optimal design of information channels for optimization under information constraints is a non-convex problem; see Yüksel and Linder (2012) and Yüksel and Başar (2013) for a review of the literature and certain topological properties of the problem.…”

Section: Recommended Readingmentioning

confidence: 99%

Information and Communication Complexity of Networked Control Systems

Yüksel¹

2015

Encyclopedia of Systems and Control

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Information and communication complexity of a networked control system identifies the minimum amount of information exchange needed between the decision makers (such as encoders, controllers and actuators) to achieve a certain objective, which may be in terms of reaching a target state or achieving a given cost threshold. This formulation does not impose any constraints on the computational requirements to perform the communication or control. Both stochastic and deterministic formulations are considered.

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Section: Recommended Readingmentioning

confidence: 99%

Information and Communication Complexity of Networked Control Systems

Yüksel¹

2015

Encyclopedia of Systems and Control

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“…Neuhoff and Gilbert focus on the minimization of entropy rate, as in (5). The work in [13] studied the optimal finite-horizon sequential quantization problem, and showed that the optimal encoder for a k th -order Markov source depends on the last k source symbols and the present state of the decoder's memory (i.e. the history of decoded symbols).…”

Section: B Previous Workmentioning

confidence: 99%

Opportunistic scheduling with limited channel state information: A rate distortion approach

Johnston

Modiano

Polyanskiy

2014

2014 IEEE International Symposium on Information Theory

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Abstract-We consider an opportunistic communication system in which a transmitter selects one of multiple channels over which to schedule a transmission, based on partial knowledge of the network state. We characterize a fundamental limit on the rate that channel state information must be conveyed to the transmitter in order to meet a constraint on expected throughput. This problem is modeled as a causal rate distortion optimization of a Markov source. We introduce a novel distortion metric capturing the impact of imperfect channel state information on throughput. We compute a closed-form expression for the causal information rate distortion function for the case of two channels, as well as an algorithmic upper bound on the causal rate distortion function. Finally, we characterize the gap between the causal information rate distortion and the causal entropic ratedistortion functions.

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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%

“…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]. The result essentially follows from Witsenhausen [53]. Theorem 6.11 [57] For the finite horizon problem, any causal composite quantization policy can be replaced without any loss in performance by one which, at time t = 1, .…”

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 the Structure of Real-Time Source Coders

Abstract: The outputs of a discrete time source with memory are to be encoded ("quantized" or "compressed")

Cited by 144 publications

References 1 publication

Information and Communication Complexity of Networked Control Systems

Information and Communication Complexity of Networked Control Systems

Opportunistic scheduling with limited channel state information: A rate distortion approach

Design of Information Channels for Optimization and Stabilization in Networked Control

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