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

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 article 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.

Ketersediaan

Informasi Detail

Judul Seri

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 58-63

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
DATA MODELS
TRAINING DATA
SERVERS
BRAIN MODELING

Info Detail Spesifik

DOI: 10.1109/MCOM.003.2200401

Pernyataan Tanggungjawab

Jorge Martín-Pérez, Nuria Molner, Francesco Malandrino, Carlos Jesús Bernardos, Antonio de la Oliva, David Gomez-Barquero

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