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Electrical Engineering and Systems Science > Signal Processing

arXiv:2203.08074 (eess)
[Submitted on 14 Mar 2022]

Title:Combining AI/ML and PHY Layer Rule Based Inference -- Some First Results

Authors:Brenda Vilas Boas, Wolfgang Zirwas, Martin Haardt
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Abstract:In 3GPP New Radio (NR) Release 18 we see the first study item starting in May 2022, which will evaluate the potential of AI/ML methods for Radio Access Network (RAN) 1, i.e., for mobile radio PHY and MAC layer applications. We use the profiling method for accurate iterative estimation of multipath component parameters for PHY layer reference, as it promises a large channel prediction horizon. We investigate options to partly or fully replace some functionalities of this rule based PHY layer method by AI/ML inferences, with the goal to achieve either a higher performance, lower latency, or, reduced processing complexity. We provide first results for noise reduction, then a combined scheme for model order selection, compare options to infer multipath component start parameters, and, provide an outlook on a possible channel prediction framework.
Comments: submitted to SPAWC 2022
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:2203.08074 [eess.SP]
  (or arXiv:2203.08074v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2203.08074

Submission history

From: Brenda Vilas Boas [view email]
[v1] Mon, 14 Mar 2022 08:50:51 UTC (295 KB)
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