Channel Detection in Coded Communication

Weinberger, Nir; Merhav, Neri

doi:10.1109/tit.2017.2732356

Cited by 10 publications

(25 citation statements)

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“…Following [5] and [6], we consider a hypothesis testing problem in which the probability law for the observed random variable Y belongs to one of the two disjoint sets. Only messages that belong to one of the sets are decoded whereas no decoding takes place if a decision is declared in favor of the other set.…”

Section: Problem Formulation and Theoretical Resultsmentioning

confidence: 99%

“…it (see [6] for various motivations). In this framework, the null and alternative hypotheses can be represented, respectively, as…”

Section: Problem Formulation and Theoretical Resultsmentioning

confidence: 99%

“…Therefore, we focus on obtaining the optimal decision rule for the rejection region Γ 0 in the remainder of the manuscript. The following proposition characterizes the optimal rejection region corresponding to the null hypothesis, which in turn yields the form of the solution to the optimization problem proposed in (6).…”

Section: Problem Formulation and Theoretical Resultsmentioning

confidence: 99%

“…More explicitly, the decision rule treats the null hypothesis as another message and decoding is performed among all hypotheses in the presence of prior uncertainty according to arg max k ∈{0,1,...,M } π ls k p k (y). Furthermore, in the case of perfect prior information, the formulation proposed in (6) reduces to that introduced in [5] when equal priors are assumed and the maximum MD constraint is replaced with the average MD constraint. As a result, our formulation can be considered as a generalization of Bayesian and Neyman-Pearson frameworks in the presence of uncertainty.…”

Section: Discussion After the Proof Of Lemma 1]) {γ *mentioning

confidence: 99%

“…In a wireless communications scenario, the channel characteristics may change abruptly due to blockage by a large obstacle or interference from other users, which in turn necessitates the detection of such changes before performing decoding. While the traditional approach has been to separately optimize for different tasks, joint optimization often provides improved performance as demonstrated for various frameworks such as joint detection and estimation [1]- [3], joint detection and source coding [4], and most recently, for joint detection and decoding [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Joint Detection and Decoding in the Presence of Prior Information With Uncertainty

Bayram¹,

Dulek²,

Gezici³

2016

IEEE Signal Process. Lett.

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Abstract-An optimal decision framework is proposed for joint detection and decoding when the prior information is available with some uncertainty. The proposed framework provides tradeoffs between the average inclusive error probability (computed using estimated prior probabilities) and the worst case inclusive error probability according to the amount of uncertainty while satisfying constraints on the probability of false alarm and the maximum probability of miss-detection. Theoretical results that characterize the structure of the optimal decision rule according to the proposed criterion are obtained. The proposed decision rule reduces to some well-known detectors in the case of perfect prior information or when the constraints on the probabilities of miss-detection and false alarm are relaxed. Numerical examples are provided to illustrate the theoretical results.

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Section: Problem Formulation and Theoretical Resultsmentioning

confidence: 99%

“…it (see [6] for various motivations). In this framework, the null and alternative hypotheses can be represented, respectively, as…”

Section: Problem Formulation and Theoretical Resultsmentioning

confidence: 99%

Section: Problem Formulation and Theoretical Resultsmentioning

confidence: 99%

Section: Discussion After the Proof Of Lemma 1]) {γ *mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Detection and Decoding in the Presence of Prior Information With Uncertainty

Bayram¹,

Dulek²,

Gezici³

2016

IEEE Signal Process. Lett.

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On Joint Detection and Decoding in Short-Packet Communications

Lancho

Östman

Durisi

2021

2021 IEEE Global Communications Conference (GLOBECOM)

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We consider a communication problem in which the receiver must first detect the presence of an information packet and, if detected, decode the message carried within it. We present general nonasymptotic upper and lower bounds on the maximum coding rate that depend on the blocklength, the probability of false alarm, the probability of misdetection, and the packet error probability. The bounds, which are expressed in terms of binary-hypothesis-testing performance metrics, generalize finiteblocklength bounds derived previously for the scenario when a genie informs the receiver whether a packet is present. The bounds apply to detection performed either jointly with decoding on the entire data packet, or separately on a dedicated preamble. The results presented in this paper can be used to determine the blocklength values at which the performance of a communication system is limited by its ability to perform packet detection satisfactorily, and to assess the difference in performance between preamblebased detection, and joint detection and decoding. Numerical results pertaining to the binary-input AWGN channel are provided.

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On the Reliability Function of Distributed Hypothesis Testing Under Optimal Detection

Weinberger

Kochman

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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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. Index TermsBinning, channel-detection codes, distributed hypothesis testing, error exponents, expurgated bounds, hierarchical ensembles, multiterminal data compression, random coding, side information, statistical inference, superposition codes. 2 results from [4], [5] to fully characterize Stein's exponent in the testing against independence case (i.e., when the null hypothesis states that (X, Y ) ∼ P XY , whereas the alternative hypothesis states that (X, Y ) ∼ P X × P Y ).Further, they have used quantization-based encoding to derive an achievable Stein's exponent for a general pair of memoryless hypotheses [2, Th. 5], but without a converse bound. Consecutive progress on this problem, as well as on the symmetric case (in which the Y -observations must also be compressed) is summarized in [27, Sec. IV], with notable contributions from [26], [28], [49]. The the zero-rate case was also considered, for which [26], [28], [48] and [27, Th. 5.5] derived matching achievable and converse bounds under various kind of assumptions on the distributions induced each of the hypotheses. In the last decade, a renewed interest in the problem arose, aimed both at tackling more elaborate models, as well as at improving the results on the basic model. As for the former, notable examples include the following. Stein's exponents under positive rates were explored in successive refinement models [56], for multiple encoders [43], for interactive models [31], [67], under privacy constraints [38], combined with lossy compression [33], over noisy channels [54], [58], for multiple decision centers [46], as well as over multi-hop networks [47]. Exponents for the zero-rate problem were studied under restricted detector structure [41] and for multiple encoders [68]. The finite blocklength and second-order regimes were addressed in [61].Notwithstanding the foregoing progress, the encoding approach proposed in [49] is still the best known in general for the basic model we study in this paper. It is based on quantization and binning, just as used, e.g., for distributed lossy compression (the Wyner-Ziv problem [22, Ch. 11] [66]). First, the encoding rate is reduced by quanti...

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Channel Detection in Coded Communication

Cited by 10 publications

References 44 publications

Joint Detection and Decoding in the Presence of Prior Information With Uncertainty

Joint Detection and Decoding in the Presence of Prior Information With Uncertainty

On Joint Detection and Decoding in Short-Packet Communications

On the Reliability Function of Distributed Hypothesis Testing Under Optimal Detection

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