Work

Error Exponent Bounds for the Bee-Identification Problem

Year: 2019

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1905.07868

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

Institutions: National University of Singapore, National Center for Supercomputing Applications, National Center for Supercomputing Applications

Language: English

Topic: Error Correcting Code Techniques

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2946692431

Cites: 13

Cited by:

Related to: 20