Non-iterative joint channel equalisation and channel decoding

Knickenberg, A.; Yeap, B. L.; Hamorsky, J.; Breiling, Marco; Hanzo, Lajos

doi:10.1109/glocom.1999.831678

Cited by 6 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, this schemes provides further insights into the so-called super-trellis structure of turbo codes and establishes their relationship with convolutional codes, since this scheme may be detected using a modified Viterbi decoder. The same philosophy was invoked also in the context of turbo equalization [33] in [34], but this topic will be detailed during our later discourse. A range of further important milestones in the development of turbo codes were created by Bendetto, Montorsi, and their team [35]- [40], which are addressed in other papers of this special issue.…”

Section: ) Correcting the Approximationvlog-map Algorithmmentioning

confidence: 99%

Turbo Decoding and Detection for Wireless Applications

Hanzo

Woodard²,

Robertson

2007

Proc. IEEE

View full text Add to dashboard Cite

Future iterative receivers will be designed using EXIT charts for near-capacity operation over dispersive wireless channels at the lowest possible complexity and delay. More specifically, we commence by discussing the turbo principles, which have been shown to be capable of performing close to Shannon's capacity limit. We continue by reviewing the classic maximum a posteriori probability decoder. These discussions are followed by studying the effect of a range of system parameters in a systematic fashion, in order to gauge their performance ramifications. In the second part of this treatise, we focus our attention on the family of iterative receivers designed for wireless communication systems, which were partly inspired by the invention of turbo codes. More specifically, the family of iteratively detected joint coding and modulation schemes, turbo equalization, concatenated spacetime and channel coding arrangements, as well as multi-user detection and three-stage multimedia systems are highlighted.

show abstract

Section: ) Correcting the Approximationvlog-map Algorithmmentioning

confidence: 99%

Turbo Decoding and Detection for Wireless Applications

Hanzo

Woodard²,

Robertson

2007

Proc. IEEE

View full text Add to dashboard Cite

show abstract

“…It was shown in [9] that the NI-JED performs optimally and therefore outperforms the turbo equalizer, but its computational complexity grows unimaginably as the interleaver depth and the CIR length increase. Despite the excellent performance of the NI-JED, it is not a viable solution for practical systems.…”

Section: Block Interleavingmentioning

confidence: 99%

“…In [9], it was demonstrated that equalization and decoding can be performed jointly by transforming the convolution decoder trellis into a super-trellis, and then using this super-trellis to perform equalization while decoding. This is a novel idea in the sense that all the available information is processed as a whole, and there is no exchange of information between independent subunits.…”

Section: Block Interleavingmentioning

confidence: 99%

“…In [9], an optimal joint equalizer and decoder is presented, where the equalizer and the decoder are jointly modeled on one trellis, a super-trellis according to [9], and a block interleaver is used to interleave the coded symbols before transmission. The computational complexity of this joint equalizer and decoder, however, is not only exponentially related to the channel memory length and the encoder constraint length, but also to the interleaver depth.…”

Section: Non-iterative Joint Equalization and Decoding (Ni-jed)mentioning

confidence: 99%

“…Joint equalization and decoding can be performed by MLSE or MAP algorithms and by employing a supertrellis, as shown in [9], given that the depth of the interleaver is limited by computational complexity limitations. The joint equalizer and decoder achieves optimal noniterative equalization and decoding.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A primer on equalization, decoding and non-iterative joint equalization and decoding

Myburgh

Olivier

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

In this article, a general model for non-iterative joint equalization and decoding is systematically derived for use in systems transmitting convolutionally encoded BPSK-modulated information through a multipath channel, with and without interleaving. Optimal equalization and decoding are discussed first, by presenting the maximum likelihood sequence estimation and maximum a posteriori probability algorithms and relating them to equalization in single-carrier channels with memory, and to the decoding of convolutional codes. The non-iterative joint equalizer/decoder (NI-JED) is then derived for the case where no interleaver is used, as well as for the case when block interleavers of varying depths are used, and complexity analyses are performed in each case. Simulation results are performed to compare the performance of the NI-JED to that of a conventional turbo equalizer (CTE), and it is shown that the NI-JED outperforms the CTE, although at much higher computational cost. This article serves to explain the state-of-the-art to students and professionals in the field of wireless communication systems, presenting these fundamental topics clearly and concisely.

show abstract