1990
DOI: 10.1109/18.52470
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Quantization for decentralized hypothesis testing under communication constraints

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Cited by 162 publications
(166 citation statements)
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“…In the case where the joint distribution p(x 1 , x 2 , θ) is known and continuous, necessary conditions for optimal Q 1 , Q 2 , and h for the mse distortion function are given by Lam and Reibman [10]. In order to find the solution, the Cyclic Generalized Lloyd's Algorithm (CGLA) proposed by Longo, et al [13] in the framework of decentralized hypothesis testing under capacity constraints and for a known joint distribution is used [13], [9], [10], [6]. The CGLA is a variation of the Generalized Lloyd Algorithm (GLA) [11], [12], [5].…”
Section: Introductionmentioning
confidence: 99%
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“…In the case where the joint distribution p(x 1 , x 2 , θ) is known and continuous, necessary conditions for optimal Q 1 , Q 2 , and h for the mse distortion function are given by Lam and Reibman [10]. In order to find the solution, the Cyclic Generalized Lloyd's Algorithm (CGLA) proposed by Longo, et al [13] in the framework of decentralized hypothesis testing under capacity constraints and for a known joint distribution is used [13], [9], [10], [6]. The CGLA is a variation of the Generalized Lloyd Algorithm (GLA) [11], [12], [5].…”
Section: Introductionmentioning
confidence: 99%
“…The fusion center estimates the parameter θ based onX b k . Many researchers have studied the problem of quantization for distributed estimation in the case where the joint distribution p(x 1 , x 2 , θ), is known [13], [9], [10], [4], [3], [1], [6]. Here, we consider a more realistic model where the observation statistics are unknown.…”
Section: Introductionmentioning
confidence: 99%
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“…Hence one is lead to renounce to global optimality and to study suboptimal strategies. Joint design of soft (multi-bit) quantizers has been studied by Longo et al in [3], where an alternate optimization technique is proposed. Specifically, the approach in [3] is to maximize the Bhattacharyya distance between the multivariate conditional probabilities of quantized data given the hypotheses.…”
Section: Introductionmentioning
confidence: 99%
“…Joint design of soft (multi-bit) quantizers has been studied by Longo et al in [3], where an alternate optimization technique is proposed. Specifically, the approach in [3] is to maximize the Bhattacharyya distance between the multivariate conditional probabilities of quantized data given the hypotheses. The potential weakness of this approach is that the Bhattacharyya distance is not the natural measure of performance of detection systems.…”
Section: Introductionmentioning
confidence: 99%