WIP: Impact of AI/ML Model Adaptation on RAN Control Loop Response Time

Chintapalli, V.R. and Gudepu, V. and Kondepu, K. and Sgambelluri, A. and Franklin, Antony and Tamma, Bheemarjuna Reddy and et al, . (2022) WIP: Impact of AI/ML Model Adaptation on RAN Control Loop Response Time. In: 23rd IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2022, 14 June 2022 through 17 June 2022, Belfast.

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Abstract

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models. © 2022 IEEE.

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IITH Creators:	IITH Creators ORCiD Franklin, Antony UNSPECIFIED Tamma, Bheemarjuna Reddy https://orcid.org/0000-0002-4056-7963
Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	AI/ML, Beyond 5G services, Drift-assistance, O-RAN, RIC control loops
Subjects:	Computer science
Divisions:	Department of Computer Science & Engineering
Depositing User:	. LibTrainee 2021
Date Deposited:	12 Sep 2022 06:03
Last Modified:	12 Sep 2022 06:03
URI:	http://raiithold.iith.ac.in/id/eprint/10533
Publisher URL:	https://doi.org/10.1109/WoWMoM54355.2022.00053.
Related URLs:	Author Google Scholar

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