Results 1 to 10 of about 10,758 (305)
Error Exponents and α-Mutual Information [PDF]
Entropy, 2021 Over the last six decades, the representation of error exponent functions for data transmission through noisy channels at rates below capacity has seen three distinct approaches: (1) Through Gallager’s E0 functions (with and without cost constraints); (2)
Sergio Verdú
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Trade-offs between Error Exponents and Excess-Rate Exponents of Typical Slepian–Wolf Codes [PDF]
Entropy, 2021 Typical random codes (TRCs) in a communication scenario of source coding with side information in the decoder is the main subject of this work. We study the semi-deterministic code ensemble, which is a certain variant of the ordinary random binning code ...
Ran Tamir (Averbuch), Neri Merhav
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Error Exponents of LDPC Codes under Low-Complexity Decoding [PDF]
Entropy, 2021 This paper deals with the specific construction of binary low-density parity-check (LDPC) codes. We derive lower bounds on the error exponents for these codes transmitted over the memoryless binary symmetric channel (BSC) for both the well-known maximum ...
Pavel Rybin +2 more
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Distributed Hypothesis Testing over a Noisy Channel: Error-Exponents Trade-Off [PDF]
Entropy, 2023 A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by U and V ...
Sreejith Sreekumar, Deniz Gündüz
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Analysis on Optimal Error Exponents of Binary Classification for Source with Multiple Subclasses [PDF]
Entropy, 2022 We consider a binary classification problem for a test sequence to determine from which source the sequence is generated. The system classifies the test sequence based on empirically observed (training) sequences obtained from unknown sources P1 and P2 ...
Hiroto Kuramata, Hideki Yagi
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A Survey on Error Exponents in Distributed Hypothesis Testing: Connections with Information Theory, Interpretations, and Applications [PDF]
EntropyA central challenge in hypothesis testing (HT) lies in determining the optimal balance between Type I (false positive) and Type II (non-detection or false negative) error probabilities.
Sebastián Espinosa +2 more
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Non-Random Coding Error Exponent for Lattices [PDF]
2012 IEEE International Symposium on Information Theory Proceedings, 2012 An upper bound on the error probability of specific lattices, based on their distance-spectrum, is constructed. The derivation is accomplished using a simple alternative to the Minkowski-Hlawka mean-value theorem of the geometry of numbers. In many ways, the new bound greatly resembles the Shulman-Feder bound for linear codes.
Yuval Domb, Meir Feder
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The Method of Types for the AWGN Channel [PDF]
EntropyFor the discrete-time AWGN channel with a power constraint, we give an alternative derivation for the sphere-packing upper bound on the optimal block error exponent and an alternative derivation for the analogous lower bound on the optimal correct ...
Sergey Tridenski, Anelia Somekh-Baruch
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Error Exponents for the Relay Channel
2013 IEEE International Symposium on Information Theory, 2013 Achievable error exponents for the relay channel are derived using the method of types. In particular, two block-Markov coding schemes are analyzed: partial decode-forward and compress-forward. The derivations require combinations of the techniques in the proofs of the packing lemma for the error exponent of channel coding and the covering lemma for ...
Vincent Y. F. Tan
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Error Exponents in Quantum Hypothesis Testing [PDF]
, 2006 This paper has been withdrawn by the author because Lemma 3 is incorrect. This mistake is crucial in this paper.
Masahito Hayashi
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