2024/12/13 by Asmaa Abdallah, Abdullatif Albaseer, Abdallah, Asmaa +7
Engineering · #IPv6, Mobility, Handover, Networks, Security
paper · pdf · doi:10.48550/arxiv.2412.10107
The transition to 6G networks promises unprecedented advancements in wireless\ncommunication, with increased data rates, ultra-low latency, and enhanced\ncapacity. However, the complexity of managing and optimizing these\nnext-generation networks presents significant challenges. The advent of large\nlanguage models (LLMs) has revolutionized various domains by leveraging their\nsophisticated natural language understanding capabilities. However, the\npractical application of LLMs in wireless network orchestration and management\nremains largely unexplored. Existing literature predominantly offers visionary\nperspectives without concrete implementations, leaving a significant gap in the\nfield. To address this gap, this paper presents NETORCHLLM, a wireless NETwork\nORCHestrator LLM framework that uses LLMs to seamlessly orchestrate diverse\nwireless-specific models from wireless communication communities using their\nlanguage understanding and generation capabilities. A comprehensive framework\nis introduced, demonstrating the practical viability of our approach and\nshowcasing how LLMs can be effectively harnessed to optimize dense network\noperations, manage dynamic environments, and improve overall network\nperformance. NETORCHLLM bridges the theoretical aspirations of prior research\nwith practical, actionable solutions, paving the way for future advancements in\nintegrating generative AI technologies within the wireless communications\nsector.\n