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Iterative decoding of binary block and convolutional codes (1996)
| Venue: | IEEE TRANS. INFORM. THEORY |
| Citations: | 610 - 43 self |
BibTeX
@ARTICLE{Hagenauer96iterativedecoding,
author = {Joachim Hagenauer and Elke Offer and Lutz Papke},
title = {Iterative decoding of binary block and convolutional codes},
journal = {IEEE TRANS. INFORM. THEORY},
year = {1996},
volume = {42},
number = {2},
pages = {429--445}
}
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Abstract
Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes hut also for any linear binary systematic block code. The iteration is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes for lower rates less than 213. Any combination of block and convolutional component codes is possible. Several interleaving techniques are described. At a bit error rate (BER) of lo- * the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block/convolutional component codes, interleaver sizes less than 1000 and for three to six iterations.
Keyphrases
iterative decoding convolutional code binary block extrinsic value block code cross entropy product code bit error rate high rate simple block convolutional component code priori values-and delivers soft output soft channel convolutional code hut soft-inlsoft-out decoder cutoff rate stop criterion simple component code priori input next iteration convolutional component code minimal number suboptimal decoder two-dimensional systematic convolutional code reduced complexity simulation result log-likelihood domain abstract iterative decoding linear binary systematic block code index term concatenated code log-likelihood algebra priori value
