Low-density parity-check codes based on finite geometries: A rediscovery and new results

By Yu Kou, Shu Lin, Marc P. C. Fossorier. IEEE Transactions on Information Theory, 2001

1,367 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3d5abc6eb9799ee515e8b65422b14531c65addd1

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Citations in the corpus

Capacity-approaching protograph codes (2009, background): “After 1993 there have been many contributions to the design and analysis of LDPC codes; see for example [4], [5], [6], [7], [8], [9], and references therein.”
The art of error correcting coding (2002, background): “This code is a small member of the family of binary finite geometry LDPC codes recently introduced in Kuo et al. (2001). Also shown in the plot is the performance of the BF algorithm with a threshold equal to 8.”
A Class of Group-Structured LDPC Codes (2001, background): “A projective plane code, for example, has a girth of only six, but projective plane codes are known to perform remarkably well, despite the higher density of their parity check matrices [ 7 ].”
Efficient encoding of quasi-cyclic low-density parity-check codes (2005, background): “Therefore, cyclic LDPC codes [ 11 ] form a subclass of QC-LDPC codes.”
Quasicyclic low-density parity-check codes from circulant permutation matrices (2004, background): “The first category is based on finite geometries [9]–[12], while the second category is based on circulant permutation matrices.”
Regular and irregular progressive edge-growth tanner graphs (2005, methodology): “Construction of LDPC codes based on finite geometries was reported in [22].”
Design of efficiently encodable moderate-length high-rate irregular LDPC codes (2004, methodology): “This work was supported in part by the National Science Foundation under Grant CCR-9814472 and Grant CCR-9979310, in part by NASA under Grant NAG5-10643, and in part by a gift from the NSIC EHDR program.”
Efficient and reliable low-power backscatter networks (2012, methodology): “is devised to effectively decode such a rateless code on a complex constellation graph representing multiple sources, unlike bit flipping decoding in the modulo-2 domain often employed by the coding theory community [33, 44].”