Skip to content
Free planNo credit card requiredStart writing

article · 2015

Design of structured irregular LDPC codes from structured regular LDPC codes

Vibha Kulkarni, K. Jaya Sankar

Cited by 8Abstract and details only

Abstract

Irregular Low-Density Parity-Check (LDPC) codes exhibit a performance extremely close to the Shannon capacity formula. In this paper, we presented a method of developing structured irregular LDPC codes from structured regular LDPC codes. The design is based on the expansion of the column-weight two LDPC codes. We used our proposed technique of developing a column-weight two structured regular LDPC codes based on Gray code representations and adapted the expansion method proposed in literature to convert regular codes into irregular ones, and it is noticed that there is an improvement in obtained code rates using the proposed technique. It is also found that encoding process is simpler and requires less memory.

Cite this paper

APA 7

Kulkarni, V., & Sankar, K. J. (2015). Design of structured irregular LDPC codes from structured regular LDPC codes. https://doi.org/10.1109/c3it.2015.7060128

MLA 9

Kulkarni, Vibha, and K. Jaya Sankar. “Design of structured irregular LDPC codes from structured regular LDPC codes.” 2015, pp. 1–4, https://doi.org/10.1109/c3it.2015.7060128.

Chicago

Kulkarni, Vibha, and K. Jaya Sankar. 2015. “Design of structured irregular LDPC codes from structured regular LDPC codes.” https://doi.org/10.1109/c3it.2015.7060128.

Harvard

Kulkarni, V. and Sankar, K.J. (2015) ‘Design of structured irregular LDPC codes from structured regular LDPC codes’. Available at: https://doi.org/10.1109/c3it.2015.7060128.

BibTeX
@article{kulkarni2015design,
  title = {{Design of structured irregular LDPC codes from structured regular LDPC codes}},
  author = {Kulkarni, Vibha and Sankar, K. Jaya},
  pages = {1--4},
  year = {2015},
  doi = {10.1109/c3it.2015.7060128}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.