The Minimum Distance of Turbo-Like Codes

被引：12

作者：

Bazzi, Louay ^{[1
]}

Mahdian, Mohammad ^{[4
]}

Spielman, Daniel A. ^{[2
,3
]}

机构：

[1] Amer Univ Beirut, Dept Elect & Comp Engn, Beirut, Lebanon

[2] Yale Univ, Dept Comp Sci, New Haven, CT 06520 USA

[3] Yale Univ, Program Appl Math, New Haven, CT 06520 USA

[4] Yahoo Res, Santa Clara, CA 95054 USA

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2009年 / 55卷 / 01期

关键词：

Asymptotic growth; concatenated codes; minimum distance; repeat-accumulate-accumulate (RAA) codes; turbo codes; SERIAL CONCATENATION;

D O I：

10.1109/TIT.2008.2008114

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Worst-case upper bounds are derived on the minimum distance of parallel concatenated turbo codes, serially concatenated convolutional codes, repeat-accumulate codes, repeat-convolute codes, and generalizations of these codes obtained by allowing nonlinear and large-memory constituent codes. It is shown that parallel-concatenated turbo codes and repeat-convolute codes with sub-linear memory are asymptotically bad. It is also shown that depth-two serially concatenated codes with constant-memory outer codes and sublinear-memory inner codes are asymptotically bad. Most of these upper bounds hold even when the convolutional encoders are replaced by general finite-state automata encoders. In contrast, it is proven that depth-three serially concatenated codes obtained by concatenating a repetition code with two accumulator codes through random permutations can be asymptotically good.

引用

页码：6 / 15

页数：10

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