Analyze of Scientific Problems Facing Developers of Modern Telecommunication Technologies

Politanskyi, Ruslan; Veryga, Andrij; Vistak, Maria

doi:10.1109/picst51311.2020.9468011

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Traditionally, the most developed and widespread of them are the tensor method of network modeling [1,2], methods based on the Kerners theory of three phases [3], as well as numerous modifications of random graphs theory (Erdos-Reney, Watts-Strogats, Barabashi-Alberta models), percolation theory [4]. This is important for ensuring the security of information systems using telecommunication technologies [5,6], as well as other aspects of protected data transfer [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Entropy calculation for networks with determined values of flows in nodes

Politanskyi¹,

Bobalo²,

Zarytska³

et al. 2022

Math. Model. Comput.

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The paper analyses a network with given input and output flows in each of its nodes. The basis of this analysis is the algorithm for determining the set of solutions of the linear equations system, using the Gaussian method. The power of the set determines the structural entropy of the system. By introducing uncertainty into the value of part of the information flows, the deviation of the network from its equilibrium state is simulated. The set of potential solutions, as a part of the total set of the system solutions, determines the statistical entropy of the system. The probability entropy is calculated for a network with four nodes and a total flow of 10 erlangs with a sampling step of 1 erlang. Calculated entropy values for 1, 2, 3, and 4 uncertain flows out of a total of 16 flows that are transmitted between nodes of the fully connected network. As a result of the conducted statistical analysis of entropy values, the optimal number of statistical intervals for entropy values is determined: 4, 11, 24, and 43 intervals for 1, 2, 3, and 4 uncertain flows, respectively. This makes it possible to highlight the set of flows in the system that have the greatest influence on the entropy value in the system. The obtained results are of practical importance, as they enable the detection of deviations of the network from its equilibrium state by monitoring the passage of traffic on individual branches of a complex telecommunication network. Since, as shown in our previous works, the task of determining the complete set of solutions of the system for the number of nodes greater than 4 has a significant computational complexity, the application of the algorithm to such networks requires an increase in the discretization step of the values of information flows in the network. Another way to reduce computational complexity can be to reduce the set of analysed solutions to a subset of solutions close to the equilibrium state of the system.

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Section: Introductionmentioning

confidence: 99%

Entropy calculation for networks with determined values of flows in nodes

Politanskyi¹,

Bobalo²,

Zarytska³

et al. 2022

Math. Model. Comput.

View full text Add to dashboard Cite

show abstract

High-efficiency Data Transmission Channel Based on Amplitude Modulation of Many Components for Remote Sensing Space Systems

Horbatyi

2022

2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TC

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Research of distribution of information flows in a network

Politanskyi¹,

Zarytska²,

Vistak³

et al. 2021

Math. Model. Comput.

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The algorithm for determining information flows in the network is developed and studied, taking into account restrictions on the input and output flows values in each node of the system. The algorithm is based on the Gaussian method of solving linear equations systems in case when the rank of the equivalent system matrix, is less than the number of unknown variables in the system. Using this algorithm, the capacities of sets of the integer solutions for networks with 2 nodes (flow intensity up to 200), 3 nodes (flow intensity up to 20) and 4 nodes (flow intensity up to 10) are calculated.

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Analyze of Scientific Problems Facing Developers of Modern Telecommunication Technologies

Cited by 3 publications

References 12 publications

Entropy calculation for networks with determined values of flows in nodes

Entropy calculation for networks with determined values of flows in nodes

High-efficiency Data Transmission Channel Based on Amplitude Modulation of Many Components for Remote Sensing Space Systems

Research of distribution of information flows in a network

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