• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 5, Pages: 432-444

Original Article

A comprehensive review of parallel concatenation of LDPC code techniques

Received Date:02 December 2020, Accepted Date:01 February 2021, Published Date:16 February 2021


Objective: In the code theory, parallel concatenation of codes becomes more popular after the introduction of turbo codes. In recent years, the Low Density Parity Check (LDPC) code has found remarkable advancement and has seen them outshine turbo codes in terms of performance especially in the error floor and higher code rate. The main objective of this paper is to address the various techniques of a parallel concatenation of LDPC code in code theory. Method/Finding: To reduce the complexity of encoding and decoding for longer codes various parallel concatenation of LDPC coding methods were introduced and the performance was compared with other work. Novelty: When a longer block length is used, the parallel LDPC decoder is suffered from the complexity of prohibitive implementation. To overcome this issue and to achieve the best performance for longer codes, the different methods for parallel concatenation of LDPC codes were introduced with reduced complexity. This will helps to break the long and complex LDPC code into less complex and smaller LDPC to distribute the decoding and encoding load. Also, this will provides scalability and scope for improving the performance of LDPC codes in practical delay-sensitive and energy-aware applications.

Keywords: Parallel concatenation; LowDensity ParityCheck (LDPC); turbo codes; Parallel Concatenated Gallager Codes (PCGC)


  1. Branco P, Mateus P, Salema C, Souto A. Using Low-Density Parity-Check codes to improve the McEliece cryptosystem. Information Sciences. 2020;510:243–255. Available from: https://dx.doi.org/10.1016/j.ins.2019.09.030
  2. Pramanik A, Maity SP, Sarkar S. Compressed sensing image reconstruction by low density parity check codes and soft decoding of space time block codes. Computers & Electrical Engineering. 2018;72:553–565. Available from: https://dx.doi.org/10.1016/j.compeleceng.2018.01.014
  3. Shrinidhia J, Krishnaa PS. Modified Min Sum Decoding Algorithm for Low Density Parity Check Codes. Third International Conference on Computing and Network Communications (CoCoNet’19). 2020;p. 2128–2136. Available from: https://doi.org/10.1016/j.procs.2020.04.230
  4. Oudjani B, Tebbikh H, Doghmane N. Modification of extrinsic information for parallel concatenated Gallager/Convolutional code to improve performance/complexity trade-offs. AEU - International Journal of Electronics and Communications. 2018;83:484–491. Available from: https://dx.doi.org/10.1016/j.aeue.2017.10.033
  5. Behairy H, Chang SC. Analysis and design of parallel concatenated Gallager codes. Electronics Letters. 2002;38(18). Available from: https://dx.doi.org/10.1049/el:20020719
  6. Behairy HM, Benaissa M. Multiple parallel concatenated gallager codes: Code design and decoding techniques. IETE Journal of Research. 2013;59(6):659. Available from: https://dx.doi.org/10.4103/0377-2063.126948
  7. Aswathy GP, Gopakumar K. Performance Comparison of Parallel Concatenated Gallager Codes with Different Types of Interleavers. International CET Conference on Control, Communication, and Computing (IC4). 2018;p. 210–216. Available from: https://doi.org/10.1109/CETIC4.2018.8531011
  8. Mostari L, Taleb-Ahmed A. Serial Concatenation of Binary LDPC Codes with Iterative Decoding. International Journal of Recent Engineering Science (IJRES). 6(6):7–10.
  9. Rankin DM, Gulliver TA, Taylor DP. Parallel and Serial Concatenated Single Parity Check Product Codes. EURASIP Journal on Advances in Signal Processing. 2005;2005(6):775–783. Available from: https://dx.doi.org/10.1155/asp.2005.775
  10. Alfiras M, Hadi AHW. Parallel Concatenation of LDPC Codes with LTE Modulation Schemes. Applied Mathematics & Information Sciences An International Journal. 2018;6:1165–1176. Available from: http://dx.doi.org/10.18576/amis/120611
  11. Liu S, Song A. Optimization of LDPC Codes over the Underwater Acoustic Channel. International Journal of Distributed Sensor Networks. 2016;12(2). Available from: https://dx.doi.org/10.1155/2016/8906985
  12. Paolini E, Flanagan M. Low-Density Parity-Check Code Constructions. Channel Coding: Theory, Algorithms, and Applications. 2014;p. 141–209. Available from: http://dx.doi.org/10.1016/B978-0-12-396499-1.00003-0
  13. Madhu TA, Komala M. Design of fuzzy logic controlled hybrid model for the control of voltage and frequency in microgrid. Journal of Science and Technology. 2020;13(35):3612–3629. Available from: https://doi.org/ 10.17485/IJST/v13i35.1510
  14. Mallikarjunaswamy S, Sharmila N. Implementation of an effective hybrid model for islanded microgrid energy management. Indian Journal of Science and Technology. 2020;13(27):2733–2746. Available from: https://dx.doi.org/10.17485/ijst/v13i27.982
  15. Raj KS, Siddesh GK. Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs”. Journal of Science and Technology. 2020;13(41):4332–4350. Available from: https://doi.org/ 10.17485/IJST/v13i41.687
  16. Zhe Z, Fei G, Hao Z, Xue Y. Area-efficient analog decoder design for low density parity check codes in deep-space applications. The Journal of China Universities of Posts and Telecommunications. 2017;24(4):69–75. Available from: https://dx.doi.org/10.1016/s1005-8885(17)60225-5
  17. Vijayalakshmi S, Nagarajan V. Energy efficient low density parity check scheme for body channel communication using FPGA. Microprocessors and Microsystems. 2019;68:84–91. Available from: https://dx.doi.org/10.1016/j.micpro.2019.04.005
  18. Maier AJ, Cockburn BF. Optimization of Low-Density Parity Check decoder performance for OpenCL designs synthesized to FPGAs. Journal of Parallel and Distributed Computing. 2017;107:134–145. Available from: https://dx.doi.org/10.1016/j.jpdc.2017.04.001
  19. Liu H, Ma L, Chen J. Multistep linear programming approaches for decoding low-density parity-check codes. Tsinghua Science and Technology. 2009;14(5):556–560. Available from: https://dx.doi.org/10.1016/s1007-0214(09)70117-8
  20. Umashankar ML, Mallikarjunaswamy S, Ramakrishna MV. Design of High Speed Reconfigurable Distributed Life Time Efficient Routing Algorithm in Wireless Sensor Network. Journal of Computational and Theoretical Nanoscience. 2020;17(9):3860–3866. Available from: https://dx.doi.org/10.1166/jctn.2020.8975
  21. Umashankar ML, Ramakrishna MV. Design of High Speed Reconfigurable Deployment Intelligent Genetic Algorithm in Maximum Coverage Wireless Sensor Network. 2019 International Conference on Data Science and Communication (IconDSC). 2019;p. 1–6. Available from: https://doi.org/10.1109/IconDSC.2019.8816930
  22. Satish P, Srikantaswamy M, Ramaswamy N. A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring. Traitement du Signal. 2020;37(3):527–539. Available from: https://dx.doi.org/10.18280/ts.370321
  23. Yang Y, Jian-Zhong H. Replica horizontal-shuffled iterative decoding of low-density parity-check codes. The Journal of China Universities of Posts and Telecommunications. 2010;17(6):32–40. Available from: http://dx.doi.org/10.1016/S1005-8885(09)60522-7
  24. Hao Z, Shuyi Z, Lintao L, Yuan G, Liwei S. Probability stopping criterion for analog decoding of LDPC codes. The Journal of China Universities of Posts and Telecommunications. 2017;24:35–39. Available from: https://dx.doi.org/10.1016/s1005-8885(17)60185-7
  25. Mahendra HN, Shivakumar BR, Praveen J. Pixel-based Classification of Multispectral Remotely Sensed Data Using Support Vector Machine Classifier”. International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering. 2015;3:94–98. Available from: http://dx.doi.org/10.17148/IJIREEICE
  26. Mahendra HN, Mallikarjunaswamy S, Rekha V, Puspalatha V, Sharmila N. Performance Analysis of Different Classifier for Remote Sensing Application. International Journal of Engineering and Advanced Technology. 2019;9:7153–7158. Available from: http://dx.doi.org/10.35940/ijeat.A1879.109119
  27. Mahendra HN, Mallikarjunaswamy S, Siddesh GK, Komala M, Sharmila N. Evolution of real-time onboard processing and classification of remotely sensed data. Indian Journal of Science and Technology. 2020;13. Available from: https://doi.org/10.17485/IJST/v13i20.459


© 2021 Chaitra et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.