ML-Enabled Outdoor User Positioning in 5G NR Systems via Uplink SRS Channel Estimates

Andre Ráth, Dino Pjanić, Bo Bernhardsson, Fredrik Tufvesson
4/12/2023
eess.SPcs.LG

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Abstract

Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and reducing complexity. In this paper, we investigate ML techniques for positioning using 5G NR fingerprints consisting of uplink channel estimates from the physical layer channel. We show that it is possible to use Sounding Reference Signals (SRS) channel fingerprints to provide sufficient data to infer user position. Furthermore, we show that small fully-connected moderately Deep Neural Networks, even when applied to very sparse SRS data, can achieve successful outdoor user positioning with meter-level accuracy in a commercial 5G environment.

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Cite this paper

@article{rth2023mlenabled,
  title  = {ML-Enabled Outdoor User Positioning in 5G NR Systems via Uplink SRS Channel Estimates},
  author = {Andre Ráth and Dino Pjanić and Bo Bernhardsson and Fredrik Tufvesson},
  year   = {2023},
  eprint = {2304.06514},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.06514v1}
}

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