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FEATURES OF APPLICATION OF TENSOR ANALYSIS TO TELECOMMUNICATION NETWORKS MODELLING

ОСОБЕННОСТИ ПРИМЕНЕНИЯ ТЕНЗОРНОГО АНАЛИЗА К МОДЕЛИРОВАНИЮ ТЕЛЕКОММУНИКАЦИОННЫХ СЕТЕЙ

FEATURES OF APPLICATION OF TENSOR ANALYSIS TO TELECOMMUNICATION NETWORKS MODELLING

Abstract

В работе рассмотрены основные положения применения тензорного анализа к задаче моделирования телекоммуникационных сетей с целью оценки вероятностно-временных характеристик данных сетей, как показателей качества обслуживания информационных потоков в исследуемых сетях. Технологии современных телекоммуникационных сетей предполагают использование сложных по структуре маршрутов передачи информации, что обусловлено большим количеством устройств и постоянно изменяющейся топологией сети. Для моделирования процессов в телекоммуникационных сетях для оценки качественных показателей, таких, как среднее время задержки или вероятность потерь, обычно используются методы теории массового обслуживания, в которых достаточно сложно ввести информацию о структуре маршрутов передачи. Однако, данную информацию широко используют в графовых методах, учитывающих топологию сети. Тензорный анализ сетей позволяет объединить информацию о процессах, происходящих в отдельных системах сети, и информацию о структуре маршрутов передачи информации. В данной работе рассматриваются особенности применения тензорного анализа к задаче моделирования телекоммуникационных сетей. С этой целью сформулированы основные аксиомы предложенного метода, произведена классификация характеристик и параметров телекоммуникационных сетей с точки зрения тензорного анализа, рассмотрены методы и модели применения тензорного анализа к поставленной задаче, разработан алгоритм применения рассмотренного подхода к решению задачи моделирования телекоммуникационных сетей. The paper considers the main provisions of the application of tensor analysis to the telecommunication networks modeling for the probabilistic and time characteristics estimating of these networks as indicators of the level of quality of service. Technologies of modern telecommunication networks assume the use of structurally complicated information transmission routes, which is caused by a large number of devices and the dynamically changing topology of the network. To simulate processes in telecommunication networks, queuing theory methods are commonly used to estimate QoS indicators, such as the average delay time or the probability of loss, but these methods are not allowed use the information about the structure of transmission routes. However, this information is widely used in graph methods with taking into account the topology of the network. Tensor analysis of networks allows to combine information about the processes occurring in individual network systems, and information about the structure of information transmission routes. In this work, we consider the features of the application of tensor analysis to the problem of the telecommunication networks modeling. For this purpose, the main axioms of the proposed method are formulated, the classification of telecommunication network characteristics and parameters is made from the point of view of tensor analysis, methods and models of tensor analysis are considered, the algorithm for applying the approach to solving the problem of modeling telecommunication networks is developed.

Keywords

система массового обслуживания, queuing system, телекоммуникационная сеть, тензорный анализ сетей, quality of service, telecommunication network, вероятностно-временные характеристики, probability and time characteristics, tensor analysis of networks, качество обслуживания

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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
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