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distributed hypothesis testing Over Discrete Memoryless Channels

IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第4期66卷 2044-2066页

作者： Sreekumar, Sreejith Gunduz, Deniz Imperial Coll London London SW7 2AZ England

A distributed binary hypothesis testing (HT) problem involving two parties, one referred to as the observer and the other as the detector is studied. The observer observes a discrete memoryless source (DMS) and communicates its observations to the detector over a discrete memoryless channel (DMC). The detector observes another DMS correlated with that at the observer, and performs a binary hypothesis test on the joint distribution of the two DMS's using its own observed data and the information received from the observer. The trade-off between the type I error probability and the type II error-exponent of the HT is explored. Single-letter lower bounds on the optimal type II error-exponent are obtained by using two different coding schemes, a separate HT and channel coding scheme and a joint HT and channel coding scheme based on hybrid coding for the matched bandwidth case. Exact single-letter characterization of the same is established for the special case of testing against conditional independence, and it is shown to be achieved by the separate HT and channel coding scheme. An example is provided where the joint scheme achieves a strictly better performance than the separation based scheme.

关键词： distributed hypothesis testing noisy channel error-exponents reliability statistical inference separate scheme unequal error protection joint scheme hybrid coding

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distributed hypothesis testing With Variable-Length Coding

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2020年第3期1卷 681-694页

作者： Salehkalaibar, Sadaf Wigger, Michele Univ Tehran Dept Elect & Comp Engn Coll Engn Tehran *** Iran Telecom Paris LTCI F-91120 Paris France

The problem of distributed testing against independence with variable-length coding is considered when the average and not the maximum communication load is constrained as in previous works. The paper characterizes the optimum type-II error exponent of a single-sensor single-decision center system given a maximum type-I error probability when communication is either over a noise-free rate-R link or over a noisy discrete memoryless channel (DMC) with stop-feedback. Specifically, let epsilon denote the maximum allowed type-I error probability. Then the optimum exponent of the system with a rate-R link under a constraint on the average communication load coincides with the optimum exponent of such a system with a rate R/(1 - epsilon) link under a maximum communication load constraint. A strong converse thus does not hold under an average communication load constraint. A similar observation also holds for testing against independence over DMCs. With variable-length coding and stop-feedback and under an average communication load constraint, the optimum type-II error exponent over a DMC of capacity C equals the optimum exponent under fixed-length coding and a maximum communication load constraint when communication is over a DMC of capacity C/(1 - epsilon).

关键词： Variable-length coding distributed hypothesis testing discrete memoryless channels strong converse

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On the Optimality of Binning for distributed hypothesis testing

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IEEE TRANSACTIONS ON INFORMATION THEORY 2012年第10期58卷 6282-6303页

作者： Rahman, Md Saifur Wagner, Aaron B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

We study a hypothesis testing problem in which data are compressed distributively and sent to a detector that seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related problems in distributed source coding, schemes based on random binning perform well and are often optimal. For distributed hypothesis testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in the binning process. We show that despite this complication, binning is optimal for a class of problems in which the goal is to "test against conditional independence." We then use this optimality result to give an outer bound for a more general class of instances of the problem.

关键词： Binning distributed hypothesis testing Gaussian many-help-one hypothesis testing against independence Gel'fand and Pinsker hypothesis testing against independence outer bound Quantize-Bin-Test scheme rate-exponent region test against conditional independence

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Byzantine-Resilient distributed hypothesis testing With Time-Varying Network Topology

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第7期67卷 3243-3258页

作者： Wu, Bo Carr, Steven Bharadwaj, Suda Xu, Zhe Topcu, Ufuk Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

In this article, we study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively. In particular, we consider a scenario where there is an unknown subset of compromised agents that may deliberately share altered information to undermine the team objective. We propose two distributed algorithms where each agent maintains and updates two sets of beliefs (i.e., probability distributions over the hypotheses), namely local and actual beliefs (LB and AB, respectively, for brevity). In both algorithms, at every time step, each agent shares its AB with other agents within its communication range and makes a local observation to update its LB. Then, both algorithms can use the shared information to update ABs under certain conditions. One requires receiving a certain number of shared ABs at each time instant;the other accumulates shared ABs over time and updates after the number of shared ABs exceeds a prescribed threshold. Otherwise, both algorithms rely on the agent's current LB and AB to update the new AB. We prove under mild assumptions that the AB for every noncompromised agent converges almost surely to the true hypothesis, without requiring connectivity in the underlying time-varying network topology. Using a simulation of a team of unmanned aerial vehicles aiming to classify adversarial agents among themselves, we illustrate and compare the proposed algorithms. Finally, we show experimentally that the second algorithm consistently outperforms the first algorithm in terms of the speed of convergence.

关键词： testing Network topology Trajectory Convergence Mobile agents Task analysis Surveillance Byzantine attacks distributed hypothesis testing multiagent system

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On the Reliability Function of distributed hypothesis testing Under Optimal Detection

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第8期65卷 4940-4965页

作者： Weinberger, Nir Kochman, Yuval MIT LIDS 77 Massachusetts Ave Cambridge MA 02139 USA MIT IDSS 77 Massachusetts Ave Cambridge MA 02139 USA Hebrew Univ Jerusalem Sch CSE Jerusalem Israel

The distributed hypothesis testing problem with full side-information is studied. The trade-off (reliability function) between the two types of error exponents under limited rate is studied in the following way. First, the problem is reduced to the problem of determining the reliability function of channel codes designed for detection (in analogy to a similar result which connects the reliability function of distributed lossless compression and ordinary channel codes). Second, a single-letter random-coding bound based on a hierarchical ensemble, as well as a single-letter expurgated bound, are derived for the reliability of channel-detection codes. Both bounds are derived for a system which employs the optimal detection rule. We conjecture that the resulting random-coding bound is ensemble-tight, and consequently optimal within the class of quantization-and-binning schemes.

关键词： Binning channel-detection codes distributed hypothesis testing error exponents expurgated bounds hierarchical ensembles multiterminal data compression random coding side information statistical inference superposition codes

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Benefits of Rate-Sharing for distributed hypothesis testing

Benefits of Rate-Sharing for Distributed Hypothesis Testing

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IEEE International Symposium on Information Theory (ISIT)

作者： Hamad, Mustapha Sarkiss, Mireille Wigger, Michele LTCI Telecom Paris IP Paris F-91120 Palaiseau France SAMOVAR Telecom SudParis IP Paris F-91011 Evry France

ISBN: (纸本)9781665421607;9781665421591

We study distributed binary hypothesis testing with a single sensor and two remote decision centers that are also equipped with local sensors. The communication between the sensor and the two decision centers takes place over three links: a shared link to both centers and an individual link to each of the two centers. All communication links are subject to expected rate constraints. This paper characterizes the optimal exponents region of the type-II error for given type-I error thresholds at the two decision centers and further simplifies the expressions in the special case of having only the single shared link. The exponents region illustrates a gain under expected rate constraints compared to equivalent maximum rate constraints. Moreover, it exhibits a tradeoff between the exponents achieved at the two centers.

关键词： Broadcast channel distributed hypothesis testing error exponents expected rate constraints IoT decision centers

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Resilient distributed hypothesis testing under time-varying multi-agent networks with multiple types of adversarial agents

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NEUROCOMPUTING 2023年第1期545卷

作者： Shi, Chong-Xiao Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

In this paper, the resilient distributed hypothesis testing problem under multi-agent networks is studied, where the normal agents that are not attacked aim to cooperatively learn a true state from a set of hypotheses. Different from the existing works, the considered network is heterogeneous and time -varying, and it is allowed that multiple types of agents can be attacked. Within this framework, by designing a novel filtering mechanism, an effective resilient distributed algorithm is developed to solve the considered hypothesis testing problem. In the convergence analysis of the algorithm, a key definition about the robustness of heterogeneous time-varying networks is firstly introduced, then by combining the designed filtering mechanism with a delay-based analysis approach, it is proven that the beliefs of the true state held by all normal agents converge to 1 under the proposed algorithm, which means that the normal agents successfully learn the true state. Finally, the theoretical findings are verified through simulations, and a comparison is provided to show that the proposed algorithm by utilizing the network heterogeneity may tolerate more adversarial agents than the algorithm designed in homogeneous networks.(c) 2023 Elsevier B.V. All rights reserved.

关键词： distributed hypothesis testing Heterogeneous time-varying networks Resilient algorithm Robustness

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distributed M-ary hypothesis testing with binary local decisions

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Information Fusion 2004年第3期5卷 157-167页

作者： Zhu, Xiaoxun Yuan, Yingqin Rorres, Chris Kam, Moshe Metrologic Instruments Inc. Blackwood NJ 08012 United States Data Fusion Laboratory Department of Electrical Engineering Drexel University Philadelphia PA 19104 3141 Chestnut Street United States Department of Clinical Studies School of Veterinary Medicine University of Pennsylvania Kenneth Square PA 19348 United States

Parallel distributed detection schemes for M-ary hypothesis testing often assume that for each observation the local detector transmits at least log 2M bits to a data fusion center (DFC). However, it is possible for l... 详细信息

Parallel distributed detection schemes for M-ary hypothesis testing often assume that for each observation the local detector transmits at least log ₂M bits to a data fusion center (DFC). However, it is possible for less than log₂M bits to be available, and in this study we consider 1-bit local detectors with M>2. We develop conditions for asymptotic detection of the correct hypothesis by the DFC, formulate the optimal decision rules for the DFC, and derive expressions for the performance of the system. Local detector design is demonstrated in examples, using genetic algorithm search for local decision thresholds. We also provide an intuitive geometric interpretation for the partitioning of the observations into decision regions. The interpretation is presented in terms of the joint probability of the local decisions and the hypotheses. © 2003 Elsevier B.V. All rights reserved.

关键词： Decision fusion distributed hypothesis testing

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RATE-CONSTRAINED distributed DISTANCE testing AND ITS APPLICATIONS

RATE-CONSTRAINED DISTRIBUTED DISTANCE TESTING AND ITS APPLIC...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yeo, Chuohao Ahammad, Parvez Zhang, Hao Ramchandran, Kannan Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Janelia Farm Res Campus Howard Hughes Med Inst Ashburn VA USA

ISBN: (纸本)9781424423538

We investigate a practical approach to solving one instantiation of a distributed hypothesis testing problem under severe rate constraints that shows up in a wide variety of applications such as camera calibration, biometric authentication and video hashing: given two distributed continuous-valued random sources, determine if they satisfy a certain Euclidean distance criterion. We show a way to convert the problem from continuous-valued to binary-valued using binarized random projections and obtain rate savings by applying a linear syndrome code. In finding visual correspondences, our approach uses just 49% of the rate of scalar quantization to achieve the same level of retrieval performance. To perform video hashing, our approach requires only a hash rate of 0.0142 bpp to identify corresponding groups of pictures correctly.

关键词： random projections distributed hypothesis testing camera calibration video hashing

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Information Centrality and Ordering of Nodes for Accuracy in Noisy Decision-Making Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第4期61卷 1040-1045页

作者： Poulakakis, Ioannis Young, George F. Scardovi, Luca Leonard, Naomi Ehrich Univ Delaware Dept Mech Engn Newark DE 19711 USA Princeton Univ Dept Mech & Aerosp Engn Princeton NJ 08544 USA Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

This technical note considers a network of stochastic evidence accumulators, each represented by a drift-diffusion model accruing evidence towards a decision in continuous time by observing a noisy signal and by exchanging information with other units according to a fixed communication graph. These network dynamics model distributed sequential hypothesis testing as well as collective decision making. We prove the relationship between the location of each unit in the graph and its certainty as measured by the inverse of the variance of its state. Under mild connectivity assumptions, we show that only in balanced directed graphs do the node variances remain within a bounded constant from the minimum possible variance. We then prove that, for these digraphs, node ranking based on certainty is governed by information centrality, which depends on the notion of effective resistance suitably generalized to directed graphs. Our results, which describe the certainty of each unit as a function of the structural properties of the graph, can guide the selection of leaders in problems that involve the observation of noisy external signals by a cooperative multi-agent network.

关键词： Decision making distributed hypothesis testing drift diffusion information centrality leader selection noisy networks

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