2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | T.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 | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| 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. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
