Reliability of universal decoding based on vector-quantized codewords
Neri Merhav
- 01 Jun 2017
pp 1311-1315
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TL;DR: This work proposes a new universal decoder for memoryless sources and memoryless channels with finite alphabets and analyzes its error exponent, which improves on an earlier result by Dasarathy and Draper (2011), who used the classic maximum mutual information universal decoding.
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Abstract: Motivated by applications of biometric identification and content identification systems, we consider the problem of random coding for channels, where each codeword undergoes vector quantization, and where the decoder bases its decision only on the compressed codewords and the channel output, which is in turn, the channel's response to the transmission of an original codeword, before compression. For memoryless sources and memoryless channels with finite alphabets, we propose a new universal decoder and analyze its error exponent, which improves on an earlier result by Dasarathy and Draper (2011), who used the classic maximum mutual information (MMI) universal decoder. We show that our universal decoder provides the same error exponent as that of the optimal, maximum likelihood (ML) decoder, at least as long as all single-letter transition probabilities of the channel are positive.
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TL;DR: This new edition presents unique discussions of information theoretic secrecy and of zero-error information theory, including the deep connections of the latter with extremal combinatorics.
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Achievable Rates for Pattern Recognition
TL;DR: A mathematical model for pattern recognition systems subject to resource constraints is described, and it is shown how the aforementioned resource-complexity tradeoff can be characterized in terms of three rates related to the number of bits available for representing memory and sensory data, and thenumber of patterns populating a given statistical environment.
Universal decoding for arbitrary channels relative to a given class of decoding metrics
TL;DR: In this article, the authors considered the problem of universal decoding for arbitrary unknown channels in the random coding regime, and proposed a generic universal decoder whose average error probability is within a subexponential multiplicative factor, no larger than that of the best decoder within this class of decoders.
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