Analysis and Design of Tuned Turbo Codes
Author
Summary, in English
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.
Publishing year
2012
Language
English
Pages
4796-4813
Publication/Series
IEEE Transactions on Information Theory
Volume
58
Issue
7
Document type
Journal article
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Electrical Engineering, Electronic Engineering, Information Engineering
Keywords
- Concatenated codes
- distance growth rates
- extrinsic information
- transfer (EXIT) charts
- Hamming distance
- iterative decoding
- turbo
- codes
Status
Published
ISBN/ISSN/Other
- ISSN: 0018-9448