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

TL;DR

This paper explores integrating AI/ML techniques with traditional PHY layer inference methods in 5G NR, aiming to enhance performance, reduce latency, and lower complexity through initial experimental results.

Contribution

It presents first results on replacing or augmenting PHY layer rule-based functions with AI/ML, focusing on noise reduction, model order selection, and channel prediction.

Findings

AI/ML can improve noise reduction in PHY layer processing

Hybrid schemes for model order selection show promise

Preliminary channel prediction frameworks are feasible

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.