Large Language Models meet Network Slicing Management and Orchestration

• URL: http://arxiv.org/abs/2403.13721v1
• Date: Wed, 20 Mar 2024 16:29:52 GMT
• Title: Large Language Models meet Network Slicing Management and Orchestration
• Authors: Abdulhalim Dandoush, Viswanath Kumarskandpriya, Mueen Uddin, Usman Khalil,
• Abstract summary: This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems. We discuss the challenges associated with implementing this framework and potential solutions to mitigate them.
• Score: 0.3644165342767221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

Related papers

• Telecom Foundation Models: Applications, Challenges, and Future Trends [0.5249805590164903]
  Foundation Models (FMs) show effective generalization capabilities in various domains in language, vision, and decision-making tasks. FMs can be trained on multiple data modalities generated from the telecom ecosystem and leverage specialized domain knowledge. This paper investigates the potential opportunities of using FMs to shape the future of telecom technologies and standards.
  arXiv  Detail & Related papers  (2024-08-02T21:09:13Z)
• On the Multi-turn Instruction Following for Conversational Web Agents [83.51251174629084]
  We introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment. We propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques.
  arXiv  Detail & Related papers  (2024-02-23T02:18:12Z)
• When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment [100.58938424441027]
  We propose a split learning system for AI agents in 6G networks leveraging the collaboration between mobile devices and edge servers. We introduce a novel model caching algorithm for LLMs within the proposed system to improve model utilization in context.
  arXiv  Detail & Related papers  (2024-01-15T15:20:59Z)
• Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration [0.21200026734831154]
  Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications. NS architecture proposals typically fulfill the needs of specific sets of domains with commonalities.
  arXiv  Detail & Related papers  (2023-07-18T11:22:31Z)
• HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction [50.40732146978222]
  Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications. We propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation. HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions.
  arXiv  Detail & Related papers  (2023-03-10T17:24:41Z)
• On Modeling Network Slicing Communication Resources with SARSA Optimization [0.0]
  We present a conceptual model of network slicing, we then formulate some aspects of the model and the optimization problem to address. Next, we proposed to use a SARSA agent to solve the problem and implement a proof of concept prototype.
  arXiv  Detail & Related papers  (2023-01-11T20:00:42Z)
• AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks [7.318997639507269]
  We propose a novel layer-based HetNet architecture which distributes tasks associated with different machine learning approaches across network layers and entities. Such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency.
  arXiv  Detail & Related papers  (2022-06-04T22:03:19Z)
• Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey [68.8204255655161]
  The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms.
  arXiv  Detail & Related papers  (2022-04-02T08:12:08Z)
• 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)
• DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue [0.0]
  We present a new framework, DLGNet-Task, a unified task-oriented dialogue system. Our framework enjoys the controllable, verifiable, and explainable outputs of modular approaches. Using DLGNet-Task in conversational AI systems reduces the level of effort required for developing, deploying, and maintaining intelligent assistants.
  arXiv  Detail & Related papers  (2020-10-04T21:43:17Z)
• Automated Search for Resource-Efficient Branched Multi-Task Networks [81.48051635183916]
  We propose a principled approach, rooted in differentiable neural architecture search, to automatically define branching structures in a multi-task neural network. We show that our approach consistently finds high-performing branching structures within limited resource budgets.
  arXiv  Detail & Related papers  (2020-08-24T09:49:19Z)

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.