Work

On the asymptotic input-output weight distributions and thresholds of convolutional and turbo-like encoders

Year: 2002

Type: article

Abstract: We present a general method for computing the asymptotic input-output weight distribution of convolutional encoders. In some instances, one can derive explicit analytic expressions. In general, though, to determine the growth rate of the input-output weight distribution for a particular normalized input weight /spl kappa/ and output weight /spl omega/, a system of polynomial equations has to be solved. This method is then used to determine the asymptotic weight distribution of various concatenated code ensembles and to derive lower bounds on the thresholds of these ensembles under maximum-likelihood (ML) decoding.

Source: IEEE Transactions on Information Theory

DOI: 10.1109/tit.2002.805065

Authors: I. Sason, E. Telatar, R. Urbanke

Institutions: Technion – Israel Institute of Technology, École Polytechnique Fédérale de Lausanne

Language: English

Topic: Error Correcting Code Techniques

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2120647302

FWCI: 3.183

Cites: 24

Cited by: 45

Related to: 10