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

In 3GPP New Radio (NR) Release 18 we see the first study item starting in May 2022, which will evaluate the potential of artificial intelligence and machine learning (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 an accurate iterative estimation of the parameters of the dominant multipath components, as it promises a large channel prediction horizon. We investigate options to partly or fully replace some functionalities of rule based PHY layer algorithms 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.

I. Introduction

Starting in May 2022, we see in 3GPP NR Release 18 the first study item [1], which will evaluate the potential of AI/ML methods for RAN 1, i.e., for mobile radio PHY and MAC layer applications. This study item "New study item description (SID) on AI/ML for NR Air Interface" will assess the standard impact to limited use cases like channel prediction. For 6G we expect a fully integrated AI/ML air interface, which eventually might integrate all PHY building blocks into one deep transmitter and receiver [2].