The Bee-Identification Problem: Bounds on the Error Exponent

Anshoo Tandon, Vincent Y.F. Tan, Lav R. Varshney

Research output: Contribution to journalArticle

Abstract

Consider the problem of identifying a massive number of bees, uniquely labeled with barcodes, using noisy measurements. We formally introduce this 'bee-identification problem', define its error exponent, and derive efficiently computable upper and lower bounds for this exponent. We show that joint decoding of barcodes provides a significantly better exponent compared to separate decoding followed by permutation inference. For low rates, we prove that the lower bound on the bee-identification exponent obtained using typical random codes (TRC) is strictly better than the corresponding bound obtained using a random code ensemble (RCE). Further, as the rate approaches zero, we prove that the upper bound on the bee-identification exponent meets the lower bound obtained using TRC with joint barcode decoding.

Original languageEnglish (US)
Article number8795542
Pages (from-to)7405-7416
Number of pages12
JournalIEEE Transactions on Communications
Volume67
Issue number11
DOIs
StatePublished - Nov 2019

Keywords

  • Bee-identification problem
  • error exponent
  • joint decoding
  • noisy channel
  • permutation recovery

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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