Combining AI/ML and PHY Layer Rule Based Inference -- Some First Results
This work addresses the potential for AI/ML to improve efficiency in 3GPP New Radio systems, though it appears incremental as it builds on existing profiling methods.
The paper tackles the problem of integrating AI/ML methods into PHY layer rule-based inference for mobile radio networks, aiming to enhance performance, reduce latency, or lower complexity, with initial results showing applications in noise reduction and model order selection.
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.