Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN

• URL: http://arxiv.org/abs/2208.04229v1
• Date: Mon, 1 Aug 2022 15:24:27 GMT
• Title: Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN
• Authors: Jorge Mart\'in-P\'erez, Nuria Molner, Francesco Malandrino, Carlos Jes\'us Bernardos, Antonio de la Oliva, David Gomez-Barquero
• Abstract summary: Open Radio Access Network (O-RAN) is an emerging paradigm, whereby network infrastructure elements communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. In this paper we introduce, discuss, and evaluate the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training.
• Score: 8.52291735627073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 use at the RIC. In this paper we introduce, discuss, and evaluate 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.

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