Analysis and Design of Tuned Turbo Codes
Artikel i vetenskaplig tidskrift, 2012

It has been widely observed that there exists a fundamental tradeoff between the minimum (Hamming) distance properties and the iterative decoding convergence behavior of turbo-like codes. While capacity-achieving code ensembles typically are asymptotically bad in the sense that their minimum distance does not grow linearly with block length, and they therefore exhibit an error floor at moderate-to-high signal-to-noise ratios, asymptotically good codes usually converge further away from channel capacity. In this paper, we introduce the concept of tuned turbo codes, a family of asymptotically good hybrid concatenated code ensembles, where asymptoticminimum distance growth rates, convergence thresholds, and code rates can be tradedoff using two tuning parameters: lambda and mu By decreasing lambda, the asymptotic minimum distance growth rate is reduced in exchange for improved iterative decoding convergence behavior, while increasing lambda raises the asymptotic minimum distance growth rate at the expense of worse convergence behavior, and thus, the code performance can be tuned to fit the desired application. By decreasing mu, a similar tuning behavior can be achieved for higher rate code ensembles.

Hamming

accumulate codes

ensembles

minimum distance

extrinsic information transfer (EXIT) charts

concatenated codes

distance growth rates

Concatenated codes

Författare

C. Koller

University of Notre Dame

Alexandre Graell i Amat

Chalmers, Signaler och system, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

J. Kliewer

New Mexico State University Las Cruces

F. Vatta

Universita degli Studi di Trieste

K. S. Zigangirov

University of Notre Dame

Russian Academy of Sciences

Lunds universitet

D. J. Costello

University of Notre Dame

IEEE Transactions on Information Theory

0018-9448 (ISSN)

Vol. 58 7 4796-4813 6200859

Ämneskategorier

Signalbehandling

DOI

10.1109/TIT.2012.2195711

Mer information

Senast uppdaterat

2018-04-16