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Improving Noise Immunity And Efficiency Using High-precision Iterative Codes

Improving Noise Immunity And Efficiency Using High-precision Iterative Codes

Abstract

The article discusses the issues of ensuring noise immunity in digital broadcasting systems, shows the importance of the transition to the optimal code and the need to use it in the field of noiseless coding in various areas of telecommunication transmission and reception of digital signals. The previous algorithms and error-correcting coding methods based on the Gray code, which are used in multi-level digital broadcast modulation schemes to minimize the intensity of bit errors, are highlighted. A model of error-correcting coding by the Gray method and methods for estimating the probability of error for the Gray code are presented. Based on computer modeling in the Matlab 7.0 Simulink environment, a model of a noise-resistant coding system was developed, which works on the basis of a parallel-cascade high-precision iterative coding and decoding algorithm, a method for determining and estimating the probability of error is given for the high-precision iterative coding and decoding algorithm, and the complexity of constructing a high-precision iterative code. The study obtained probabilistic-energy characteristics for the Gray code and for a high-precision iterative code in various positions of phase manipulation. A comparative analysis of the energy gain G (dB) of the high-precision iterative coding algorithm with the Gray coding algorithm is performed. The simulation results in a Simulink environment of an error-correcting Gray code and a high-precision iterative code in a digital information transfer system are presented

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold