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Found 5 papers, 1 papers with code
Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration
Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI.
Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices.
An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
Data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization.
A Model Drift Detection and Adaptation Framework for 5G Core Networks
Due to the dynamic nature of next-generation networks and their supported use cases and applications, model drift is a serious concern, which can deteriorate the performance of intelligent models deployed throughout the network.
Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis
Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices.
