LITERATURE ANALYSIS ON HETEROGENEOUS TRAFFIC MODELING AND RADIO RESOURCE ALLOCATION IN 5G NR NETWORKS
DOI:
https://doi.org/10.47390/ydif-y2026v2i19/n07Keywords:
5G NR, network slicing, traffic model, self-similarity, G/G/1, bandwidth, neural network, artificial intelligence.Abstract
5G NR networks combine eMBB, URLLC and mMTC services on a single physical infrastructure, which raises the problem of allocating radio resources according to the sharply different QoS requirements of heterogeneous traffic. This paper presents an analytical review of 16 scientific sources devoted to this problem. It summarizes the transition from Poisson models to self-similar (fractal) traffic models, the role of heavy-tailed distributions and the Hurst parameter, the GI/G/m queueing apparatus, models for distributing radio channel bandwidth among network slices, and joint scheduling methods for URLLC and eMBB traffic. Two main limitations are identified: the traffic distributions adopted in existing models do not fully match real network measurements, and optimization is performed mainly with offline analytical and numerical solvers. On this basis, a research direction is substantiated towards an adaptive allocation model combining measurement-based traffic distributions, 5G NR numerology and QoS constraints with a neural network approximator and reinforcement learning.
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