COMPARATIVE ANALYSIS OF PROBABILITY DISTRIBUTION SELECTION FOR DESCRIBING 5G NR SLICE TRAFFIC
DOI:
https://doi.org/10.47390/ydif-y2026v2i19/n04Keywords:
traffic model, truncated Pareto, Bernoulli, compound Poisson, Hurst parameter, coefficient of variation, G/G/1, network slicing.Abstract
In 5G NR, distributing radio channel bandwidth among eMBB, URLLC and mMTC slices depends on the statistical model of traffic. This paper presents a comparative analysis of 17 sources on selecting distributions describing the number of packets arriving within one transmission time interval (TTI). Standard models, real network measurements and theoretical results are compared, and for each slice a candidate distribution and an analytical expression of the coefficient of variation used in the G/G/1 queueing model are given. A truncated Pareto distribution is substantiated for eMBB, a Bernoulli approximation in the sparse-load regime for URLLC, and a compound Poisson distribution for mMTC. It is shown that the Pareto–Hurst relation is heuristic, results are sensitive to the truncation bound, the compound Poisson process represents burstiness rather than self-similarity, and the two-moment approximation ignores long-range dependence. The obtained expressions can be used to generate training data for an AI-based neural network approximator.
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