Error Exponents in the Bee Identification Problem

نویسندگان

چکیده

The bee identification problem is a of properly recognizing massive amount data (a numerous bees in beehive, for example) which have been mixed and corrupted by noise. We derive various error exponents the under two different decoding rules. Under naïve decoding, decodes each independently others, we analyze general discrete memoryless channel relatively wide family stochastic decoders. Upper lower bounds to random coding exponent are derived proved be equal at high rates. Then, propose bound on typical code, improves upon low also third bound, related expurgated codes, turns out strictly higher than other bounds, show that universal maximum mutual information decoder optimal with respect code code. Moving further, symmetric channels, likelihood decoder. first then, an improved stems from expurgation process. numerically our second intermediate range rates, where previous work no longer holds.

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ژورنال

عنوان ژورنال: IEEE Transactions on Information Theory

سال: 2021

ISSN: ['0018-9448', '1557-9654']

DOI: https://doi.org/10.1109/tit.2021.3091762