摘要
本文提出基于遗传算法 (GA)的卷积码快速译码 ,进行格图上的单向 (U GA)和双向 (B GA)搜索译码 .它利用遗传算法的群体多样性好、搜索空间宽广 ,具有全局优化能力 ,提高译码质量 .通过模拟计算 ,分析了单向和双向的译码算法和群体规模M对误比特性能Pb 的影响 .模拟结果表明 :在相同译码复杂度下、Pb=10 -6时 ,与MA算法 (编码约束度K =19)相比 ,该译码算法约有 0 5dB性能增益 ;与VA算法 (K =7)相比 ,B GA大约有 1dB增益 .
In this paper,fast decoding algorithms for convolutional codes,which are based on the genetic algorithm (GA),are presented.They search from one side or both sides of the trellis respectively to implement unidirectional decoding (U GA) or bi directional decoding (B GA).With good diversity,wide search region and global optimal ability of GA,the decoding performance can be improved.The effects of U and B GA and population size M on P b are analyzed.Simulation results show that,with the same decoding computation efforts and at P b=10 -6 ,B GA achieves about 0 5 dB coding gains over M algorithm (MA) for the coding constraint length K=19 code,and 1dB gain over Viterbi algorithm (VA) with the K=7 code.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2000年第9期137-139,110,共4页
Acta Electronica Sinica
基金
国家自然科学基金!(No.69972 0 35
69772 0 2 2 )
关键词
卷积码
译码算法
遗传算法
convolutional code
decoding algorithm
genetic algorithm
performance
