Xingqi Wu (University of Michigan-Dearborn), Junaid Farooq (University of Michigan-Dearborn), Yuhui Wang (University of Michigan-Dearborn), Juntao Chen (Fordham University)

The decentralized and modular architecture of open radio access networks (O-RAN) enhances flexibility and interoperability but introduces significant challenges in efficiently managing resource allocation. The disaggregation of network functions across distributed unit, centralized unit, and RAN intelligent controller (RIC) creates complexities in coordinating resources across multiple network slices, each with distinct and dynamic quality of service (QoS) requirements. Traditional machine learning (ML) approaches for resource management often rely on extensive offline training, which is impractical in the highly variable and real-time environments of O-RAN systems. This paper presents LLM-xApp, a novel large language model (LLM)-powered xApp framework for adaptive radio resource management in O-RAN systems. The proposed framework is based on intelligently prompting LLM agents to dynamically optimize resource allocation to different network slices. Experimental evaluations are conducted on the OpenAI Cellular (OAIC) platform showcasing significant improvements in average data rates as well as the reliability of the slices, demonstrating the potential of LLMs to enhance real-time decision-making in next-generation wireless networks.

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Nicolas Badoux (EPFL), Flavio Toffalini (Ruhr-Universität Bochum, EPFL), Yuseok Jeon (UNIST), Mathias Payer (EPFL)

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Jinghua Liu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of…

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SHAFT: Secure, Handy, Accurate and Fast Transformer Inference

Andes Y. L. Kei (Chinese University of Hong Kong), Sherman S. M. Chow (Chinese University of Hong Kong)

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Yue Xiao (IBM Research), Dhilung Kirat (IBM Research), Douglas Lee Schales (IBM Research), Jiyong Jang (IBM Research), Luyi Xing (Indiana University Bloomington), Xiaojing Liao (Indiana University)

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