Application of Correcting Polynomial Modular Codes in Infotelecommunication Systems

Kalmykov, Igor A.; Chistousov, Nikita Konstantinovich; Александров, А. А.; Provornov, Igor

doi:10.1007/978-3-030-51974-2_38

Cited by 1 publication

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“…It is generally accepted that of the known methods of encoding information in problems of ensuring high noise immunity (Reed-Muller, cyclic redundancy check (CRC), (Bose-Chowdhury-Hockingham (BCH), etc. ), the code in the deduction system (the system of residual classes, RNS, built on the basis of the Chinese remainder theorem) and the Reed-Solomon code have the greatest appeal [12][13][14]. These codes are used with high efficiency in high-speed, digital signal processing algorithms with a large amount of computational operations, but the RNS is most organically combined with a neural network basis [15].…”

Section: Introductionmentioning

confidence: 99%

Error Control Coding Algorithms in High Reliability Telemetry Systems

et al. 2022

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In the oil and gas industry, in the process of drilling support (geosteering) and well telemetry, there is a problem of transmitting reliable information via wireless communication channels. The quality of such communication, as a rule, suffers due to the presence of errors caused by interference. As the depth of the well increases, the problem becomes more extensive. In order to solve the problem, it is proposed to choose noise-resistant coding in the system of residual classes. This system parallelizes the execution of arithmetic operations, has corrective abilities and organically adapts to the neural network basis of intelligent field management. At the same time, there are constraining factors for the mass application of the RNS; for example, difficulties in implementing non-modular procedures, forward and reverse coding, and some difficulties in identifying and correcting errors. That is why the task of improving the RNS seems relevant not only for oil and gas complexes, but also for any digital signal processing applications focused on intelligent neural network management on the basis of non-positional computing. The material of the article is limited to the study of the noise immunity of linear codes of the deduction system and the development of algorithms for detecting and correcting errors.

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