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Paper IDT.3.4
Paper Title Bee-Identification Error Exponent with Absentee Bees
Authors Anshoo Tandon, Vincent Y. F. Tan, National University of Singapore, Singapore; Lav R. Varshney, University of Illinois at Urbana-Champaign, United States
Session T.3: Information Theory and Biology
Presentation Lecture
Track Topics in Information Theory
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Abstract The ``bee-identification problem'' was formally defined by Tandon, Tan and Varshney [IEEE Trans. Commun., vol. 67, 2019], and the error exponent was studied. This work extends the results for the ``absentee bees'' scenario, where a small fraction of the bees are absent in the beehive image used for identification. For this setting, we present an exact characterization of the bee-identification error exponent, and show that independent barcode decoding is optimal, i.e., joint decoding of the bee barcodes does not result in a better error exponent relative to independent decoding of each noisy barcode. This is in contrast to the result without absentee bees, where joint barcode decoding results in a significantly higher error exponent than independent barcode decoding. We also define and characterize the `capacity' for the bee-identification problem with absentee bees, and prove the strong converse for the same.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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