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Application of quantization to reduce the decoding difficulty of LDPC codes

DOI 10.18127/j00338486-201906(8)-02


A.V. Bashkirov – Dr.Sc.(Eng.), Associate Professor, Acting Head of Department Radio Equipment Engineering and Manufacturing, Voronezh State Technical University
A.S. Kostyukov – Bachelor, Student, Voronezh State Technical University
L.N. Nikitin – Ph.D.(Eng.), Associate Professor, Voronezh State Technical University
M.A. Sivash – Student, Voronezh State Technical University
I.S. Bobylkin – Ph.D.(Eng.), Associate Professor, Department Radio Equipment Engineering and Manufacturing, Voronezh State Technical University

Anti-interference coding is one of the rapidly developing areas in information technology. Protecting information from errors and interfe-rence when it is transmitted through various communication channels is a very topical task today, which many scientists in the world are fighting to solve. One of the most important problems in noise-resistant coding is to ensure high bandwidth of the communication channel with a small complexity of code implementation. Not all robust algorithms cope with this task, the most preferable for these purposes are such algorithms as LDPC codes, they are also Gallager codes (low density codes). These algorithms with a relative complexity of imple-mentation provide good bandwidth. In connection with all the above information, we can make a simple conclusion that the study of me-thods for modifying and improving Gallager codes, in order to obtain the best indicators of information transfer, is in its essence a prom-ising development for the entire noise-resistant coding in general.
This article provides a description of one of such a way to modify low-density codes, namely, the article describes the use of quantization to reduce the complexity of decoding LDPC codes. For this purpose, the article conducted a study for various methods of quantizing these codes, and carried out experimental modeling of low-density codes under various conditions. The article uses the Monte-Carlo method to analyze approximations for decoding low-density codes. The Gallager code of the LTE standard, with different code lengths, was chosen as the test specimen for various experiments. Also, in order to implement conditions that would be close to real conditions, the channel with additive white Gaussian noise was used during the experimental analysis. As experiments, modeling was performed with different quantization methods, with different Gallager code lengths, with several information decoding models, according to an approximation of the trust propagation algorithm. After all the experiments described above, a comparative analysis of the obtained results was made and actual conclusions were made on the topic of the conducted studies, which are presented and described in detail at the end of the article. Namely, when implementing a LDPC decoder, it is important to consider the discretization and length of the transmitted information. The best performance with normal signal decoding was achieved with a word length of seven bits.

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