article · IEEE Transactions on Communications · 2002
Soft decision decoding of Reed-Solomon codes
V. Ponnampalam, Branka Vucetic
Cited by 46Abstract and details only
Abstract
This paper presents a maximum-likelihood decoding (MLD) and a suboptimum decoding algorithm for Reed-Solomon (RS) codes. The proposed algorithms are based on the algebraic structure of the binary images of RS codes. Theoretical bounds on the performance are derived and shown to be consistent with simulation results. The proposed suboptimum algorithm achieves near-MLD performance with significantly lower decoding complexity. It is also shown that the proposed suboptimum, algorithm has better performance compared with generalized minimum distance decoding, while the proposed MLD algorithm has significantly lower decoding complexity than the well-known Vardy-Be'ery (1991) MLD algorithm.
Cite this paper
Ponnampalam, V., & Vucetic, B. (2002). Soft decision decoding of Reed-Solomon codes. IEEE Transactions on Communications, 50(11), 1758–1768. https://doi.org/10.1109/tcomm.2002.805279
Ponnampalam, V., and Branka Vucetic. “Soft decision decoding of Reed-Solomon codes.” IEEE Transactions on Communications, vol. 50, no. 11, 2002, pp. 1758–68, https://doi.org/10.1109/tcomm.2002.805279.
Ponnampalam, V., and Branka Vucetic. 2002. “Soft decision decoding of Reed-Solomon codes.” IEEE Transactions on Communications 50 (11): 1758–68. https://doi.org/10.1109/tcomm.2002.805279.
Ponnampalam, V. and Vucetic, B. (2002) ‘Soft decision decoding of Reed-Solomon codes’, IEEE Transactions on Communications, 50(11), pp. 1758–1768. Available at: https://doi.org/10.1109/tcomm.2002.805279.
@article{ponnampalam2002soft,
title = {{Soft decision decoding of Reed-Solomon codes}},
author = {Ponnampalam, V. and Vucetic, Branka},
journal = {IEEE Transactions on Communications},
volume = {50},
number = {11},
pages = {1758--1768},
year = {2002},
doi = {10.1109/tcomm.2002.805279}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.