IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 8 - Issue 8, August 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Computationally Simpler And Fast Convergence Algorithm For Neural Network Based Ldpc Encoder/ Decoder

[Full Text]

 

AUTHOR(S)

Rajasekar.B, Logashanmugam.E, Nandhitha. N.M

 

KEYWORDS

Back propagation, CODEC, computational complexity, LDPC, Perceptron.

 

ABSTRACT

Soft computing technique for computationally less complex encoder / decoder for LDPC is proposed. The novel learning technique is computationally less complex than Ordinary Gradient Learning (OGLN) and highly accurate than AGL. Performance of the proposed technique is compared with the conventional techniques in terms of maximum error, minimum error and computational complexity. In order to emulate a codec Artificial Neural Network based LDPC encoder/decoder is developed. The performance of the proposed LDPC codec is compared with that conventional codecs in terms of learning algorithm. The proposed learning algorithm has X-L multiplication in contrast to X2 and X multiplication of the conventional techniques.

 

REFERENCES

[1] Alexios Balatsoukas, Andreas Burg (2014), Density Evolution for Min-Sum Decoding of LDPC Codes Under Unreliable Message Storage”, IEEE Communications Letters, Accepted For Publication, IEEE Communications Letters, pp.1-4.
[2] Chu-Hsiang Huang, Yao Li and Lara Dolecek Gallager B. (2014), “LDPC Decoder with Transient and Permanent Errors”, IEEE Transactions on Communications, Vol. 62, No. 1, pp.15-28.D
[3] aisuke, M., Kazunori, H. and Ryo, Y. (2014), “An LDPC Decoder With Time-Domain Analog and Digital Mixed-Signal Processing”, IEEE Journal of Solid-State Circuits, Vol. 49, No. 1, pp. 73-83.
[4] Gallager R. G. (1962), “Low-density parity-check codes,” IRE Trans. Inform. Theory, Vol. IT-8, pp. 21-28.
[5] Rajasekar, B., Logashanmugam, E, (2014), Design and development of an improved split row.
[6] decoding algorithm with reduced BER , Research Journal of Applied Sciences, Engineering and Technology,
[7] Hanghang Qi and Norbert Goertz (2013), “Low-Complexity Encoding of LDPC Codes: A New Algorithm and its Performance”. available at publik. tuwien. ac. at/files/PubDat_166941. pdf,(06. 04. 2011) (2013).
[8] Guohua Zhang, Rong Sun and Xinmei Wang (2013), “Construction of girth-eight QC-LDPC codes from greatest common divisor”, IEEE Communications Letters, Vol. 17, No. 2, pp.369- 372.
[9] Rajasekar, B., Logashanmugam, E. (2014), Euclidean geometry LDPC codes for error.
[10] correction in memory devices, International Journal of Applied Engineering Research.
[11] Hua Xiao and Mehdi Karimi (2013), “Error Rate Estimation of Low-Density Parity-Check Codes Decoded by Quantized Soft-Decision Iterative Algorithms”, IEEE transactions on communications, Vol. 61, No. 2, pp.474.
[12] Reviriego Pedro, Juan A. Maestro and Mark F. Flanagan (2013), “Error detection in majority logic decoding of Euclidean geometry low density parity check (EG-LDPC) codes”, Very Large Scale Integration (VLSI) Systems, IEEE Transactions, Vol.21, No. 1, pp. 156-159.
[13] Michael R. Bastian,(2009),“Neural Networks And The Natural Gradient, Ph.D Dissertation Utah State University, Logan, Utah.