Anytime Reliable Transmission of Real-Valued Information through Digital Noisy Channels

The problem of reliably transmitting a real-valued random vector through a digital noisy channel is relevant for the design of distributed estimation and control techniques over networked systems. One important example consists in the remote state estimation under communication constraints. In this...

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Bibliographic Details
Published in:SIAM journal on control and optimization Vol. 48; no. 6; pp. 3903 - 3924
Main Authors: Como, Giacomo, Fagnani, Fabio, Zampieri, Sandro
Format: Journal Article
Language:English
Published: Philadelphia, PA Society for Industrial and Applied Mathematics 01.01.2010
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Channels
Coders
Coding
Coding theory
Complex systems
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Control Engineering
Control systems
Convergence
Data transmission
Electrical Engineering, Electronic Engineering, Information Engineering
Elektroteknik och elektronik
Encoders
Engineering and Technology
Exact sciences and technology
Mathematical analysis
Mean square errors
Reglerteknik
Software
Studies
Teknik
Theoretical computing
Vector space
Vectors (mathematics)
Exponential convergence
unequal error protection
Electronic document
Updating
digital fountain codes
Mean square error
Digital information
Convergence rate
94B70
94A34
State estimation
Numerical convergence
state estimation with communication constraints
State constraint
Probabilistic approach
Source coding
anytime transmission
Transmission channel
Decoding
Real time
real-time communication
Computational complexity
93E10
Noisy channel
Distributed control
ISSN:0363-0129, 1095-7138, 1095-7138
Online Access:Get full text
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Summary:The problem of reliably transmitting a real-valued random vector through a digital noisy channel is relevant for the design of distributed estimation and control techniques over networked systems. One important example consists in the remote state estimation under communication constraints. In this case, an anytime transmission scheme consists of an encoder--which maps the real vector into a sequence of channel inputs--and a decoder--which sequentially updates its estimate of the vector as more and more channel outputs are observed. The encoder performs both source and channel coding of the data. Assuming that no channel feedback is available at the transmitter, this paper studies the rates of convergence to zero of the mean squared error. Two coding strategies are analyzed: the first one has exponential convergence rate but is expensive in terms of its encoder/decoder computational complexity, while the second one has a convenient computational complexity but subexponential convergence rate. General bounds are obtained describing the convergence properties of these classes of methods. [PUBLICATION ABSTRACT]
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ISSN:0363-0129
1095-7138
1095-7138
DOI:10.1137/09074601X