Network Management Assistance through Large Language Models (LLMs)
| dc.contributor.author | Fatima, Baadji | |
| dc.contributor.author | Supervisor: Hichem, Debbi | |
| dc.date.accessioned | 2025-07-08T08:12:17Z | |
| dc.date.available | 2025-07-08T08:12:17Z | |
| dc.date.issued | 2025-06-15 | |
| dc.description.abstract | This thesis explores the transformative role of Large Language Models (LLMs) in the domain of network optimization, particularly within the context of 5G communication technologies. It starts by studying deep learning fundamentals and neural network ar chitectures, emphasizing the evolution and impact of Models such as GPT, BERT, and DeepSeek. The study then examines the integration of these models into modern net working workflows, focusing on their applications in network security, task classification as well as answering telecom-domain questions. A core challenge addressed in this work lies in the sheer volume and complexity of technical data in the telecommunications industry—particularly across 3GPP (3rd Gen eration Partnership Project) standards, which has overseen the development of universal standards for Mobile Wireless Networks (MWNs). 3GPP continuously publishes a large number of intricate documents, making it difficult for engineers and researchers to stay updated and extract relevant information efficiently. This creates a need for advanced methods to process, analyze, and understand these documents to ensure network relia bility and performance. To address this, the thesis aiming to be a roadmap for researchers and practitioners to leverage LLMs in solving various telecom tasks. Special attention is given to the appli cation of fine-tuning and Retrieval-Augmented Generation (RAG) techniques to improve technical comprehension and automate knowledge extraction from3GPP specifications. A practical evaluation is conducted using the TSpec-LLM dataset, and chatbot are de veloped to classify telecom tasks and respond to domain-specific queries. Finally, The research demonstrates how LLMs will become increasingly important for telecom-specific operations by enhancing information retrieval accuracy and efficiency and accessibility in complex technical domains. | |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/46737 | |
| dc.language.iso | en | |
| dc.publisher | Mohamed Boudiaf University of M'sila | |
| dc.subject | Chatbot | |
| dc.subject | 3GPP Documents | |
| dc.subject | Large Language Models (LLMs) | |
| dc.subject | Network Optimization | |
| dc.subject | Retrieval-Augmented Generation (RAG) | |
| dc.subject | Fine-tuning | |
| dc.title | Network Management Assistance through Large Language Models (LLMs) | |
| dc.type | Thesis |