Error Exponent Bounds for the Bee-Identification Problem
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.
Cite
@article{arxiv.1905.07868,
title = {Error Exponent Bounds for the Bee-Identification Problem},
author = {Anshoo Tandon and Vincent Y. F. Tan and Lav R. Varshney},
journal= {arXiv preprint arXiv:1905.07868},
year = {2019}
}
Comments
Corrected typos. Restated some asymptotic inequalities as corresponding non-asymptotic statements for cleaner and more transparent presentation