Decoding staircase codes with marked bits

Y. Lei, Alex Alvarado, B. Chen, X. Deng, Z. Cao, Jianqiang Li, Kun Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

16 Citations (Scopus)
181 Downloads (Pure)

Abstract

Staircase codes (SCCs) are typically decoded using iterative bounded-distance decoding (BDD) and hard decisions. In this paper, a novel decoding algorithm is proposed, which partially uses soft information from the channel. The proposed algorithm is based on marking certain number of highly reliable and highly unreliable bits. These marked bits are used to improve the miscorrection-detection capability of the SCC decoder and the error-correcting capability of BDD. For SCCs with 2-error-correcting BCH component codes, our algorithm improves upon standard SCC decoding by up to 0.30 dB at a bit-error rate of 10 -7 . The proposed algorithm is shown to achieve almost half of the gain achievable by an idealized decoder with this structure.

Original languageEnglish
Title of host publication2018 IEEE 10th International Symposium on Turbo Codes and Iterative Information Processing, ISTC 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538670484
DOIs
Publication statusPublished - 23 Jan 2019
Event10th IEEE International Symposium on Turbo Codes and Iterative Information Processing, ISTC 2018 - Hong Kong, China
Duration: 3 Dec 20187 Dec 2018

Conference

Conference10th IEEE International Symposium on Turbo Codes and Iterative Information Processing, ISTC 2018
Country/TerritoryChina
CityHong Kong
Period3/12/187/12/18

Funding

This work is in part supported by the NSFC Program (No. 61431003, 61601049, 61625104, and 61701155) and Fund of State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications (No. IPOC2017ZT08). The work of A. Alvarado is supported by the Netherlands Organisation for Scientific Research (NWO) via the VIDI Grant ICONIC (project number 15685) and has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 57791). The author Yi Lei would like to thank China Scholarship Council (CSC) for supporting her study in Netherlands.

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