A Lower Bound Analysis of Hamming Distortion for a Binary CEO Problem With Joint Source-Channel Coding

A two-node binary chief executive officer (CEO) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian noise (AWGN) channels. We first reduce the binary CEO problem to a binary multitermin...

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Published in:IEEE transactions on communications Vol. 64; no. 1; pp. 343 - 353
Main Authors: Xin He, Xiaobo Zhou, Komulainen, Petri, Juntti, Markku, Matsumoto, Tad
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:0090-6778, 1558-0857
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Abstract A two-node binary chief executive officer (CEO) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian noise (AWGN) channels. We first reduce the binary CEO problem to a binary multiterminal source coding problem, of which an outer bound for the rate-distortion region is derived. The distortion function is then established by evaluating the relationship between the binary CEO and multiterminal source coding problems. A lower bound approximation on the Hamming distortion (HD) is obtained by minimizing a distortion function subject to constraints obtained based on the source-channel separation theorem. Encoding/decoding algorithms using concatenated convolutional codes and a joint decoding scheme are used to verify the lower bound on the HD. It is found that the theoretical lower bounds on the HD and the computer simulation-based bit error rate performance curves have the same tendencies. The differences in the threshold signal-to-noise ratio between the theoretical lower bounds and those obtained by simulations are around 1.5 dB in AWGN channel. The theoretical lower bound on the HD in block Rayleigh fading channel is also evaluated by performing Monte Carlo simulation.
AbstractList A two-node binary chief executive officer (CEO) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian noise (AWGN) channels. We first reduce the binary CEO problem to a binary multiterminal source coding problem, of which an outer bound for the rate-distortion region is derived. The distortion function is then established by evaluating the relationship between the binary CEO and multiterminal source coding problems. A lower bound approximation on the Hamming distortion (HD) is obtained by minimizing a distortion function subject to constraints obtained based on the source-channel separation theorem. Encoding/decoding algorithms using concatenated convolutional codes and a joint decoding scheme are used to verify the lower bound on the HD. It is found that the theoretical lower bounds on the HD and the computer simulation-based bit error rate performance curves have the same tendencies. The differences in the threshold signal-to-noise ratio between the theoretical lower bounds and those obtained by simulations are around 1.5 dB in AWGN channel. The theoretical lower bound on the HD in block Rayleigh fading channel is also evaluated by performing Monte Carlo simulation.
Author Juntti, Markku
Matsumoto, Tad
Xin He
Komulainen, Petri
Xiaobo Zhou
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Hamming distortion lower bound
binary multiterminal source coding
binary CEO problem
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Snippet A two-node binary chief executive officer (CEO) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single...
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SubjectTerms binary CEO problem
binary multiterminal source coding
Channels
Coding
Computer simulation
Decoding
Distortion
Hamming distortion lower bound
High definition video
Joints
Lower bounds
Mathematical analysis
Mathematical models
Rate-distortion
rate-distortion outer bound
Source coding
Title A Lower Bound Analysis of Hamming Distortion for a Binary CEO Problem With Joint Source-Channel Coding
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