
Summary: This paper is devoted to a Shannon-theoretic study of turbo codes. We prove that ensembles of parallel and serial turbo codes are good in the following sense. For a turbo code ensemble defined by a fixed set of component codes (subject only to mild necessary restrictions), there exists a positive number \(\gamma_0\) such that for any binary-input memoryless channel whose Bhattacharyya noise parameter is less than \(\gamma_0\), the average maximum-likelihood (ML) decoder block error probability approaches zero, at least as fast as \(n^{-\beta}\), where \(\beta\) is the ``interleaver gain'' exponent defined by Benedetto et al. in 1996.
union bound, Decoding, Channel models (including quantum) in information and communication theory, maximum-likelihood decoding (MLD), 004, 620, turbo codes, Coding theorems (Shannon theory), Bhattacharyya parameter, coding theorems, maximum-likelihood decoding (MLD), turbo codes, union bound, Bhattacharyya parameter, coding theorems
union bound, Decoding, Channel models (including quantum) in information and communication theory, maximum-likelihood decoding (MLD), 004, 620, turbo codes, Coding theorems (Shannon theory), Bhattacharyya parameter, coding theorems, maximum-likelihood decoding (MLD), turbo codes, union bound, Bhattacharyya parameter, coding theorems
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 58 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
