TY - GEN
T1 - Large Language Model for 5G Network
T2 - 16th International Conference on Information and Communication Technology Convergence, ICTC 2025
AU - Wee, Seongryool
AU - Park, Heejae
AU - Song, Seungyeop
AU - Park, Laihyuk
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 5G networks provide high-speed data transfer, ultra-low latency, and large-scale connectivity, making them suitable for a wide range of industrial applications. Despite these advantages, 5G networks face new challenges in network management and optimization due to the increasing complexity of heterogeneous network components, massive device connectivity, and dynamic service requirements. LLMs (Large Language Models) offer a promising approach to address these challenges due to their outstanding capabilities in language understanding and data processing. This paper reviews the latest applications of LLMs in 5G networks and specifically focuses on resource allocation, QoS (Quality of Service), anomaly detection, and automated network configuration. It analyzes research studies using models such as GPT-3.5, Llama2, and Mobile LLAMA and evaluates how LLMs contribute to maximizing efficiency and performance in 5G environments.
AB - 5G networks provide high-speed data transfer, ultra-low latency, and large-scale connectivity, making them suitable for a wide range of industrial applications. Despite these advantages, 5G networks face new challenges in network management and optimization due to the increasing complexity of heterogeneous network components, massive device connectivity, and dynamic service requirements. LLMs (Large Language Models) offer a promising approach to address these challenges due to their outstanding capabilities in language understanding and data processing. This paper reviews the latest applications of LLMs in 5G networks and specifically focuses on resource allocation, QoS (Quality of Service), anomaly detection, and automated network configuration. It analyzes research studies using models such as GPT-3.5, Llama2, and Mobile LLAMA and evaluates how LLMs contribute to maximizing efficiency and performance in 5G environments.
KW - 5G
KW - LLM
KW - Recent Research
UR - https://www.scopus.com/pages/publications/105035067688
U2 - 10.1109/ICTC66702.2025.11388740
DO - 10.1109/ICTC66702.2025.11388740
M3 - Conference contribution
AN - SCOPUS:105035067688
T3 - International Conference on ICT Convergence
SP - 870
EP - 871
BT - 2025 16th International Conference on Information and Communication Technology Convergence, ICTC 2025
PB - IEEE Computer Society
Y2 - 14 October 2025 through 17 October 2025
ER -