Genetic optimization of a short block length LDPC code accelerated by distributed algorithms

2016 ◽  
Author(s):  
Jan Broulim ◽  
Sima Davarzani ◽  
Vjaceslav Georgiev ◽  
Jan Zich
Keyword(s):  
Distributed Algorithms ◽  
Ldpc Code ◽  
Block Length ◽  
Genetic Optimization

scholarly journals Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 141161-141170 ◽  
Author(s):  
Ahmed Elkelesh ◽  
Moustafa Ebada ◽  
Sebastian Cammerer ◽  
Laurent Schmalen ◽  
Stephan ten Brink
Keyword(s):  
Ldpc Code ◽  
Code Design ◽  
Genetic Optimization

scholarly journals Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

2019 ◽  
Author(s):  
Shubham Chandak ◽  
Kedar Tatwawadi ◽  
Billy Lau ◽  
Jay Mardia ◽  
Matthew Kubit ◽  
...  
Keyword(s):  
Error Correction ◽  
Data Storage ◽  
Coding Theory ◽  
Ldpc Code ◽  
Block Length ◽  
Synthesis Process ◽  
Sequencing Errors ◽  
Insertion And Deletion ◽  
Erasure Correction ◽  
Storage Technologies

AbstractWith the amount of data being stored increasing rapidly, there is significant interest in exploring alternative storage technologies. In this context, DNA-based storage systems can offer significantly higher storage densities (petabytes/gram) and durability (thousands of years) than current technologies. Specifically, DNA has been found to be stable over extended periods of time which has been demonstrated in the analysis of organisms long since extinct. Recent advances in DNA sequencing and synthesis pipelines have made DNA-based storage a promising candidate for the storage technology of the future.Recently, there have been multiple efforts in this direction, focusing on aspects such as error correction for synthesis/sequencing errors and erasure correction for handling missing sequences. The typical approach is to use separate codes for handling errors and erasures, but there is limited understanding of the efficiency of this framework. Furthermore, the existing techniques use short block-length codes and heavily rely on read consensus, both of which are known to be suboptimal in coding theory.In this work, we study the tradeoff between the writing and reading costs involved in DNA-based storage and propose a practical scheme to achieve an improved tradeoff between these quantities. Our scheme breaks with the traditional separation framework and instead uses a single large block-length LDPC code for both erasure and error correction. We also introduce novel techniques to handle insertion and deletion errors introduced by the synthesis process. For a range of writing costs, the proposed scheme achieves 30-40% lower reading costs than state-of-the-art techniques on experimental data obtained using array synthesis and Illumina sequencing.The code, data, and Supplementary Material is available at https://github.com/shubhamchandak94/LDPC_DNA_storage.

scholarly journals Genetic Optimization of LDPC Codes to Improve the Correction of Burst Errors

2020 ◽  
Vol 226 ◽  
pp. 02006
Author(s):  
Jan Broulím ◽  
Alexander Ayriyan ◽  
Hovik Grigorian
Keyword(s):  
Error Correction ◽  
High Performance ◽  
Ldpc Codes ◽  
Ldpc Code ◽  
Error Correcting Codes ◽  
Error Correction Codes ◽  
Genetic Optimization ◽  
Parity Check ◽  
Physical Experiments ◽  
Television Broadcasting

Error correction plays a crucial role when transmitting data from the source to the destination through a noisy channel. It has found many applications in television broadcasting services, data transmission in radiation harsh environment (e. g. space probes or physical experiments) or memory storages influenced by Single Event Effects (SEE). Low Density Parity Check (LDPC) codes provide an important technique to correct these errors. The parameters of error correction depend both on the decoding algorithm and on the LDPC code given by the parity-check matrix. Therefore, a particular design of the paritycheck matrix is necessary. Moreover, with the development of high performance computing, the application of genetic optimization algorithms to design the parity-check matrices has been enabled. In this article, we present the application of the genetic optimization algorithm to produce error correcting codes with special properties, especially the burst types of errors. The results show the bounds of correction capabilities for various code lengths and various redundancies of LDPC codes. This is particularly useful when designing systems under the influence of noise combined with the application of the error correction codes.

Performance Comparison between Turbo Code and Rate-Compatible LDPC Code for Evolved UTRA Downlink OFDM Radio Access

2009 ◽  
Vol E92-B (5) ◽  
pp. 1504-1515 ◽  
Author(s):  
Naoto OKUBO ◽  
Nobuhiko MIKI ◽  
Yoshihisa KISHIYAMA ◽  
Kenichi HIGUCHI ◽  
Mamoru SAWAHASHI
Keyword(s):  
Turbo Code ◽  
Ldpc Code ◽  
Performance Comparison ◽  
Radio Access

Genetic Optimization for (l,d)-Motif Discovery

2012 ◽  
Vol 35 (7) ◽  
pp. 1429
Author(s):  
Hong-Wei HUO ◽  
Dan-Dan GUO ◽  
Qiang YU ◽  
Yi-Pu ZHANG ◽  
Wei NIU
Keyword(s):  
Motif Discovery ◽  
Genetic Optimization
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