Distributed Binary Detection With Lossy Data Compression

Consider the problem where a statistician in a two-node system receives rate-limited information from a transmitter about marginal observations of a memoryless process generated from two possible distributions. Using its own observations, this receiver is required to first identify the legitimacy of...

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Vydané v:	IEEE transactions on information theory Ročník 63; číslo 8; s. 5207 - 5227
Hlavní autori:	Katz, Gil, Piantanida, Pablo, Debbah, Merouane
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	asymptotic performance central detector Computer Science Data compression Decoding discrete spatially dependent observations Distortion distributed detection Error analysis Error detection error exponent Error probability error statistics Hypotheses Information Theory lossy source coding Mathematics multiterminal detection multiterminal source coding Networking and Internet Architecture Receivers Reconstruction side information signal detection Source coding Statistics Testing type-I error rate type-II error rate signal detection asymptotic performance multiterminal source coding Data compression type-II error rate lossy source coding distributed detection error exponent multiterminal detection central detector side infomation discrete spatially dependent observations error statistics type-I error rate
ISSN:	0018-9448, 1557-9654
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Abstract	Consider the problem where a statistician in a two-node system receives rate-limited information from a transmitter about marginal observations of a memoryless process generated from two possible distributions. Using its own observations, this receiver is required to first identify the legitimacy of its sender by declaring the joint distribution of the process, and then depending on such authentication, it generates adequate reconstruction of the observations, satisfying an average per-letter distortion. The performance of this setup is investigated through the corresponding rate-error-distortion region, describing the tradeoff between: the communication rate, the error exponent induced by the detection, and the distortion incurred by the source reconstruction. In the special case of testing against independence, where the alternative hypothesis implies that the sources are independent, the optimal rate-error-distortion region is characterized. An application example to binary symmetric sources is given subsequently and the explicit expression for the rate-error-distortion region is provided as well. The case of "general hypotheses" is also investigated. A new achievable rate-error-distortion region is derived based on the use of non-asymptotic binning, improving the quality of communicated descriptions. Further improvement of performance in the general case is shown to be possible when the requirement of source reconstruction is relaxed, which stands in contrast to the case of general hypotheses.
AbstractList	Consider the problem where a statistician in a two-node system receives rate-limited information from a transmitter about marginal observations of a memoryless process generated from two possible distributions. Using its own observations, this receiver is required to first identify the legitimacy of its sender by declaring the joint distribution of the process, and then depending on such authentication, it generates adequate reconstruction of the observations, satisfying an average per-letter distortion. The performance of this setup is investigated through the corresponding rate-error-distortion region, describing the tradeoff between: the communication rate, the error exponent induced by the detection, and the distortion incurred by the source reconstruction. In the special case of testing against independence, where the alternative hypothesis implies that the sources are independent, the optimal rate-error-distortion region is characterized. An application example to binary symmetric sources is given subsequently and the explicit expression for the rate-error-distortion region is provided as well. The case of "general hypotheses" is also investigated. A new achievable rate-error-distortion region is derived based on the use of non-asymptotic binning, improving the quality of communicated descriptions. Further improvement of performance in the general case is shown to be possible when the requirement of source reconstruction is relaxed, which stands in contrast to the case of general hypotheses. —Consider the problem where a statistician in a two-node system receives rate-limited information from a transmitter about marginal observations of a memoryless process generated from two possible distributions. Using its own observations, this receiver is required to first identify the legitimacy of its sender by declaring the joint distribution of the process, and then depending on such authentication it generates the adequate reconstruction of the observations satisfying an average per-letter distortion. The performance of this setup is investigated through the corresponding rate-error-distortion region describing the trade-off between: the communication rate, the error exponent induced by the detection and the distortion incurred by the source reconstruction. In the special case of testing against independence, where the alternative hypothesis implies that the sources are independent, the optimal rate-error-distortion region is characterized. An application example to binary symmetric sources is given subsequently and the explicit expression for the rate-error-distortion region is provided as well. The case of " general hypotheses " is also investigated. A new achievable rate-error-distortion region is derived based on the use of non-asymptotic binning, improving the quality of communicated descriptions. Further improvement of performance in the general case is shown to be possible when the requirement of source reconstruction is relaxed, which stands in contrast to the case of general hypotheses.
Author	Debbah, Merouane Katz, Gil Piantanida, Pablo
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SubjectTerms	asymptotic performance central detector Computer Science Data compression Decoding discrete spatially dependent observations Distortion distributed detection Error analysis Error detection error exponent Error probability error statistics Hypotheses Information Theory lossy source coding Mathematics multiterminal detection multiterminal source coding Networking and Internet Architecture Receivers Reconstruction side information signal detection Source coding Statistics Testing type-I error rate type-II error rate
Title	Distributed Binary Detection With Lossy Data Compression
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