RT Journal Article
T1 Choose, not hoard: Information-to-model matching for Artificial Intelligence in O-RAN
A1 Martín Pérez, Jorge
A1 Molner, Nuria
A1 Malandrino, Francesco
A1 Bernardos Cano, Carlos Jesús
A1 Oliva Delgado, Antonio de la
A1 Gomez-Barquero, David
AB 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.
PB IEEE
SN 0163-6804
YR 2023
FD 2023-04
LK https://hdl.handle.net/10016/37203
UL https://hdl.handle.net/10016/37203
LA eng
NO This work was partially supported by the European Union's Horizon 2020 research and innovation program through the project iNGENIOUS under grant agreement No. 957216, and by the Spanish Ministry of Economic Aff airs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D 6G-EDGEDT and 6G-DATADRIVEN projects.
DS e-Archivo
RD 17 jul. 2024