Yulin Huang
Georgia Institute of Technology, San Jose, CA 95118, USA.
*Corresponding author: Yulin Huang
Abstract
With the rapid development of 5G and computing power networks, traditional network management and resource scheduling methods face significant challenges. Intelligent knowledge assistants enhanced by large language models (LLMs), leveraging their powerful semantic understanding and generation capabilities, can provide networks with intelligent decision support and fault diagnosis services. This study explores the application of LLMs in 5G and computing power networks and investigates the construction methods of intelligent knowledge assistants, including requirement analysis, LLM model optimization, data processing, and system integration. Case studies demonstrate their practical effectiveness in fault diagnosis, resource scheduling, and cross-domain decision-making. The results indicate that LLM-enhanced intelligent knowledge assistants can significantly improve network management efficiency and resource utilization, offering effective support for intelligent network operations and maintenance.
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How to cite this paper
Construction and Application of LLM-enhanced Intelligent Knowledge Assistants for 5G and Computing Power Networks
How to cite this paper: Yulin Huang. (2026) Construction and Application of LLM-enhanced Intelligent Knowledge Assistants for 5G and Computing Power Networks. Advances in Computer and Communication, 7(1), 1-6.
DOI: http://dx.doi.org/10.26855/acc.2026.03.001