Data and traffic models in 5G network

Rossitza Goleva, Rumen Stainov, Desislava Wagenknecht-Dimitrova, Seferin Mirtchev, Dimitar Atamian, Constandinos X. Mavromoustakis, George Mastorakis, Ciprian Dobre, Alexander Savov, Plamen Draganov

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

This chapter presents data and traffic analyses in 5G networks. We setup experiments with Zigbee sensors and measure different traffic patterns by changing the environmental conditions and number of channels. Due to the differences in read, write operations, message fragmentations and backoff of the Carrier Sense Multiple Access/Collision Avoidance algorithm we demonstrated that the traffic flows are changing dynamically. This leads to different behaviour of the network domain and requires special attention to network design. Statistical analyses are performed using Easyfit tool. It allows to find best fitting probability density function of traffic flows, approximation toward selected distributions as Pareto and Gamma and random number generation with selected distribution. Our chapter concludes with future plan for distribution parameters mapping to different traffic patterns, network topologies, different protocols and experimental environment.

Original languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages485-499
Number of pages15
Volume8
DOIs
Publication statusPublished - 2016

Publication series

NameModeling and Optimization in Science and Technologies
Volume8
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

Keywords

  • 5G traffic
  • Best-fitting pdf
  • Sensor measurements

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  • Cite this

    Goleva, R., Stainov, R., Wagenknecht-Dimitrova, D., Mirtchev, S., Atamian, D., Mavromoustakis, C. X., Mastorakis, G., Dobre, C., Savov, A., & Draganov, P. (2016). Data and traffic models in 5G network. In Modeling and Optimization in Science and Technologies (Vol. 8, pp. 485-499). (Modeling and Optimization in Science and Technologies; Vol. 8). Springer Verlag. https://doi.org/10.1007/978-3-319-30913-2_20