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Модели информационных K-каналов с памятью

Модели информационных K-каналов с памятью

Abstract

Исследование недвоичных (K-ичных, K ≥ 3) информационных каналов (ИК) с памятью как сложных стохастических структур математически достаточно сложная задача. Существенный интерес представляет синтез упрощенных математических моделей таких каналов, позволяющих относительно просто выявить важнейшие закономерности протекающих в них реальных процессов. Моделирование K-ичных ИК (K-каналов) с памятью актуальная задача, решение которой имеет как теоретическое, так и явно выраженное практическое значение. В работе рассмотрены модели дискретных K-каналов с памятью, построены их графы переходных вероятностей для различных режимов работы, оценены вероятности исходов приема символов используемого канального алфавита.

Analysis of non-binary (K-ary, K ≥ 3) information channels (IC) with memory as complex stochastic structures is rather complicated mathematically. Of significant interest is the synthesis of simplified mathematical models for such channels that allow clarifying relatively simply the most important regularities occurring in real processes. Modeling of IC (K-channels) with memory is a vital problem that has both theoretical and practical importance. In this paper models of discrete K-channels with memory are presented, their graphs of transition probabilities for various operating conditions built, and probabilities of outcomes of reception for symbols of the used alphabet estimated.

Keywords

K-КАНАЛ, МАТЕМАТИЧЕСКАЯ МОДЕЛЬ, ПЕРЕХОДНАЯ ВЕРОЯТНОСТЬ, БЕЗУСЛОВНАЯ ВЕРОЯТНОСТЬ

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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