Theory and Performance of ML Decoding for Turbo Codes using Genetic Algorithm
Date Issued
2007
Date
2007
Author(s)
Hsueh, Tsun-Chih
DOI
en-US
Abstract
Although yielding the lowest error probability, ML decoding of turbo codes has been considered unrealistic so far because efficient ML decoders have not been discovered.
In this thesis, we propose an experimental bounding technique for ML decoding and the Genetic Decoding Algorithm (GDA) for turbo codes. The ML bounding technique
establishes both lower and upper bounds for ML decoding. GDA combines the principles of perturbed decoding and genetic algorithm. In GDA, chromosomes are random
additive perturbation noises. A conventional turbo decoder is used to assign fitness values to the chromosomes in the population. After generations of evolution, good
chromosomes that correspond to decoded codewords of very good likelihood emerge. GDA can be used as a practical decoder for turbo codes in certain contexts. It is
also a natural multiple-output decoder. The most important aspect of GDA, in our opinion, is that one can utilize the ML bounding technique and GDA to empirically
determine a effective lower bound on the error probability with ML decoding. Our results show that, at a word error probability of 10^{-4}, GDA achieves the performance
of ML decoding. Using the ML bounding technique and GDA, we establish that an ML decoder only slightly outperforms a MAP-based iterative decoder at this word error probability for the block size we used and the turbo code defined for WCDMA.
Subjects
渦輪碼
遞回式解碼
最大概度解碼
干擾式解碼
基因演算法
Tubo codes
Iterative decoding
Maximum likelihood decoding
Perturbed decoding
Genetic algorithm
Type
thesis
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