The problem of identification via channels concerns a decoder that needs to provide a reliable answer to the question of whether or not a specific message (unknown in advance) was transmitted. The achievability result of Ahlswede and Dueck who introduced this problem, relied on a universal identification decoder. This decoder assigns a channel output vector yn to a decision region Dm if the empirical mutual information between yn and an input vector that could have been transmitted given message m exceeds a certain threshold. We study a generalized class of identification decoders that determine the decision regions by comparing a type-dependent 'metric' to a threshold. We introduce the notion of identification capacity with respect to a given decoding metric as the supremum of achievable normalized iterated logarithms of the number of messages that can be identified reliably using these metrics. We characterize achievable identification rates and error exponents using a type-dependent metric. In the case of an additive metric we show that the random coding achievable rate for classical mismatched decoding is an achievable identification rate.
|Title of host publication||2017 IEEE International Symposium on Information Theory, ISIT 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 9 Aug 2017|
|Event||2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany|
Duration: 25 Jun 2017 → 30 Jun 2017
|Name||IEEE International Symposium on Information Theory - Proceedings|
|Conference||2017 IEEE International Symposium on Information Theory, ISIT 2017|
|Period||25/06/17 → 30/06/17|
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