Knowledge Graph-Based Multi-Objective Recommendation for a 6G Air Interface: A Digital Twin Empowered Approach

In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, sub-optimal trade-offs among multi-objective performance metrics, outdated configurations due to fast-varying channels, etc. In this paper, the relevant user behaviors, communication environment, and system are virtualized via the digital twinning technique. Then, a knowledge graph-based multi-objective recommendation framework is proposed to configure the digital twinning air interface to adapt to channel conditions, while balancing various service requirements. First, the knowledge graph is applied to reveal complex dependencies between the air interface and the service requirements, and more importantly, to reconcile possibly contradictory performance targets. Furthermore, the air interface configuration, empowered by the digital twin technique, is able to exploit predicted prior knowledge about user behavior and the channel characteristics, thus improving the utilization efficiency of wireless resources promptly. Moreover, the digital twin technique allows the candidate air interfaces to be virtually verified and compared with little effort. Finally, two case studies are presented to demonstrate the potential of the knowledge graph-based recommendation method for the digital twinning air interface.