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 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.Comment: to appear on IEEE Trans. Information Theor

Joint Estimation and Detection Against Independence

International audienceA receiver in a two-node system is required to make a decision of relevance as to received information, using side information that may or may not be correlated with the received signal. In case the information is judged to be relevant, the receiver is then required to estimate the source with average distortion D. Focusing on the case of testing against independence, a single-letter expression for the rate-error-distortion region is proposed and proven. The resulting region ports a surprising resemblance to a seem-ingly non-associated classification problem, known as the information-bottleneck. The optimal region is then calculated for a binary symmetric example. Results demonstrate an interesting trade-off between the achievable error-exponent for the decision and the distortion at the decoder

Joint estimation and detection against independence

International audienc