• Irina Strelkovskaya Doctor of Technical Sciences, Professor, Director of Educational and Research Institute of Infocommunications and Software Engineering Department of Higher Mathematics O. S. Popov, Odessa national academy of telecommunication, Odesa, Ukraine, Ukraine
  • Irina Solovskaya PhD, Associate Professor, Department of Switching Systems O.S. Popov Odessa National Academy of Telecommunications, Ukraine
  • Anastasiya Makoganiuk Lecturer, Department of Computer Science O.S. Popov Odessa National Academy of Telecommunications,
  • Andrii Balyk Student, second-year Master's degree program of Software engineering O.S. Popov Odessa National Academy of Telecommunications, Ukraine



self-similar traffic, queuing system, QoS characteristics, Weibull distribution, Laplace-Stieltjes transformation, average waiting time for a claim, average number of claims in a queue, the average length of the queue of applications.


Background. At the planning and design stage of modern 3G/UMTS and 4G/LTE mobile networks, it is important to take into account that the traffic they serve is packet-based and has the property of self-similarity, which greatly complicates the determination of QoS characteristics.
Objective. The aim of the paper is to find the quality characteristics of the QoS of self-similar traffic described using the Weibull distribution for the QS of the form WB/M/1/ using software release.
Methods. Methods of functional analysis, queuing theory, linear programming were used for the solution.
Results. The characteristics of the quality of service QoS self-similar traffic, namely the values of the average waiting time for service requests, the average number of requests in the queue and the average length of the queue of applications in the QS WB/M/1/.
Conclusions. At the stage of frequency-territorial planning, design and further operation of 3G/UMTS and 4G/LTE networks, the found characteristics of the quality of service QoS will take into account the real values of traffic served by the base station NodeB (e-NodeB) for their optimal distribution of the service area.


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