Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN
This work addresses performance optimization for AI-based controllers in O-RAN, representing an incremental improvement over existing strategies.
The paper tackles the problem of inefficient AI model training in Open Radio Access Networks (O-RAN) by proposing multiple AI model instances that selectively use data from specific locations, rather than hoarding all available data. Experiments with real-world traces show this approach improves performance over traditional methods.
Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to train a single AI model to be used at the RIC. This paper introduces, discusses, and evaluates the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training. This brings about a flexible relationship between gNBs, the AI models used to control them, and the data such models are trained with. Experiments with real-world traces show how using multiple AI model instances that choose training data from specific locations improve the performance of traditional approaches following the hoarding strategy.