Combining AI/ML and PHY Layer Rule Based Inference -- Some First Results
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 14 Mar 2022.
Builds on
4 of 14 references.
References · 4
- 2017Deep Learning-Based Communication Over the Aircited by 72
- 2021Two-step Machine Learning Approach for Channel Estimation with Mixed Resolution RF Chainscited by 2
- 2021Machine Learning for Model Order Selection in MIMO OFDM Systemscited by 1
- 2021Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreationcited by 1
Built on by
Not cited yet.