Coordinated small machine learning models for intelligent radio resource management in 6G and beyond

In this paper, we first review evolution of radio resource management in mobile networks from traditional statistical and numerical methods towards machine learning (ML) and artificial intelligence (AI) driven approaches. We discuss these methods especially from perspective of joint optimization of multiple radio resource management parameters and settings. However, an application of ML in a common way, i.e., each ML model dealing with one radio resource management parameter, leads to an accumulation of natural inaccuracies due to sub-optimality of individual stand-alone models. Multiple radio resource management parameters can also be handled via big AI models or multi-task learning (MTL) consisting in a large model with multiple outputs, each corresponding to one resource management parameter. Nevertheless, the big AI models and MTL require large datasets for training and, like common ML models, struggle to capture mutual relations and dependencies among individual optimized parameters. Thus, we outline an approach for joint optimization of multiple radio resource management parameters consisting in a coordination of dedicated but tightly integrated small ML models. The coordination of the small ML models is facilitated via mutually exchanged feedback tailored to reflect network performance and allowing to capture mutual dependencies among the optimized radio resource management parameters. We demonstrate benefits of the coordinated small ML models against state-of-the-art concepts in terms of increased network performance and stable training.