NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models

• URL: http://arxiv.org/abs/2412.10107v1
• Date: Fri, 13 Dec 2024 12:48:15 GMT
• Title: NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models
• Authors: Asmaa Abdallah, Abdullatif Albaseer, Abdulkadir Celik, Mohamed Abdallah, Ahmed M. Eltawil,
• Abstract summary: Large language models (LLMs) have revolutionized various domains by leveraging their sophisticated natural language understanding capabilities. This paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that seamlessly orchestrates diverse wireless-specific models. A comprehensive framework is introduced, demonstrating the practical viability of our approach.
• Score: 11.015852090523229
• License:
• Abstract: The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the complexity of managing and optimizing these next-generation networks presents significant challenges. The advent of large language models (LLMs) has revolutionized various domains by leveraging their sophisticated natural language understanding capabilities. However, the practical application of LLMs in wireless network orchestration and management remains largely unexplored. Existing literature predominantly offers visionary perspectives without concrete implementations, leaving a significant gap in the field. To address this gap, this paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that uses LLMs to seamlessly orchestrate diverse wireless-specific models from wireless communication communities using their language understanding and generation capabilities. A comprehensive framework is introduced, demonstrating the practical viability of our approach and showcasing how LLMs can be effectively harnessed to optimize dense network operations, manage dynamic environments, and improve overall network performance. NETORCHLLM bridges the theoretical aspirations of prior research with practical, actionable solutions, paving the way for future advancements in integrating generative AI technologies within the wireless communications sector.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• WDMoE: Wireless Distributed Mixture of Experts for Large Language Models [68.45482959423323]
  Large Language Models (LLMs) have achieved significant success in various natural language processing tasks. We propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base station (BS) and mobile devices in wireless networks.
  arXiv  Detail & Related papers  (2024-11-11T02:48:00Z)
• WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks [16.722524706176767]
  Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. We introduce WirelessAgent, a novel approach to develop AI agents capable of managing complex tasks in wireless networks.
  arXiv  Detail & Related papers  (2024-09-12T11:48:01Z)
• WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence [16.722524706176767]
  Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. Existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. This paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
  arXiv  Detail & Related papers  (2024-05-27T11:18:25Z)
• Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings [0.21847754147782888]
  Large Language Models (LLMs) have revolutionized language understanding and human-like text generation. This paper explores new techniques to harness the power of LLMs for 6G (6th Generation) wireless communication technologies. We introduce a novel Reinforcement Learning (RL) based framework that leverages LLMs for network deployment in wireless communications.
  arXiv  Detail & Related papers  (2024-05-22T05:19:51Z)
• WDMoE: Wireless Distributed Large Language Models with Mixture of Experts [65.57581050707738]
  We propose a wireless distributed Large Language Models (LLMs) paradigm based on Mixture of Experts (MoE) We decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at base station (BS) and mobile devices. We design an expert selection policy by taking into account both the performance of the model and the end-to-end latency.
  arXiv  Detail & Related papers  (2024-05-06T02:55:50Z)
• NetLLM: Adapting Large Language Models for Networking [36.61572542761661]
  We present NetLLM, the first framework that provides a coherent design to harness the powerful capabilities of LLMs with low efforts to solve networking problems. Specifically, NetLLM empowers the LLM to effectively process multimodal data in networking and efficiently generate task-specific answers.
  arXiv  Detail & Related papers  (2024-02-04T04:21:34Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Distributed Learning in Wireless Networks: Recent Progress and Future Challenges [170.35951727508225]
  Next-generation wireless networks will enable many machine learning (ML) tools and applications to analyze various types of data collected by edge devices. Distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
  arXiv  Detail & Related papers  (2021-04-05T20:57:56Z)
• Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial [64.77665786141166]
  Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks. IRS is capable of dynamically altering wireless channels to enhance the communication performance. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks.
  arXiv  Detail & Related papers  (2020-07-06T13:59:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.