Leveraging Large Language Models for DRL-Based Anti-Jamming Strategies in Zero Touch Networks

• URL: http://arxiv.org/abs/2308.09376v1
• Date: Fri, 18 Aug 2023 08:13:23 GMT
• Title: Leveraging Large Language Models for DRL-Based Anti-Jamming Strategies in Zero Touch Networks
• Authors: Abubakar S. Ali, Dimitrios Michael Manias, Abdallah Shami, Sami Muhaidat
• Abstract summary: Zero Touch Networks (ZTNs) aim to achieve fully automated, self-optimizing networks with minimal human intervention. Despite the advantages ZTNs offer in terms of efficiency and scalability, challenges surrounding transparency, adaptability, and human trust remain prevalent. This paper explores the integration of Large Language Models (LLMs) into ZTNs, highlighting their potential to enhance network transparency and improve user interactions.
• Score: 13.86376549140248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the dawn of sixth-generation (6G) networking approaches, it promises unprecedented advancements in communication and automation. Among the leading innovations of 6G is the concept of Zero Touch Networks (ZTNs), aiming to achieve fully automated, self-optimizing networks with minimal human intervention. Despite the advantages ZTNs offer in terms of efficiency and scalability, challenges surrounding transparency, adaptability, and human trust remain prevalent. Concurrently, the advent of Large Language Models (LLMs) presents an opportunity to elevate the ZTN framework by bridging the gap between automated processes and human-centric interfaces. This paper explores the integration of LLMs into ZTNs, highlighting their potential to enhance network transparency and improve user interactions. Through a comprehensive case study on deep reinforcement learning (DRL)-based anti-jamming technique, we demonstrate how LLMs can distill intricate network operations into intuitive, human-readable reports. Additionally, we address the technical and ethical intricacies of melding LLMs with ZTNs, with an emphasis on data privacy, transparency, and bias reduction. Looking ahead, we identify emerging research avenues at the nexus of LLMs and ZTNs, advocating for sustained innovation and interdisciplinary synergy in the domain of automated networks.

Related papers

• Personalized Wireless Federated Learning for Large Language Models [75.22457544349668]
  Large Language Models (LLMs) have revolutionized natural language processing tasks. Their deployment in wireless networks still face challenges, i.e., a lack of privacy and security protection mechanisms. We introduce two personalized wireless federated fine-tuning methods with low communication overhead.
  arXiv  Detail & Related papers  (2024-04-20T02:30:21Z)
• Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks [10.981422497762837]
  The integration of Machine Learning and Artificial Intelligence into fifth-generation (5G) networks has made evident the limitations of network intelligence. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking.
  arXiv  Detail & Related papers  (2024-03-04T17:29:57Z)
• 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)
• 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)
• Zero-Touch Networks: Towards Next-Generation Network Automation [21.003217781832923]
  The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks. ZSM frameworks leverage advanced technologies such as Machine Learning (ML) to enable intelligent decision-making and reduce human intervention. This paper presents a survey of Zero-Touch Networks (ZTNs) within the ZSM framework, covering network optimization, traffic monitoring, energy efficiency, and security aspects of next-generational networks.
  arXiv  Detail & Related papers  (2023-12-07T09:21:41Z)
• NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services [25.468894023135828]
  Large language models (LLMs) have triggered tremendous success to empower our daily life by generative information. In this article, we put forward NetGPT to capably synergize appropriate LLMs at the edge and the cloud based on their computing capacity.
  arXiv  Detail & Related papers  (2023-07-12T13:10:08Z)
• Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
  Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenges.
  arXiv  Detail & Related papers  (2023-03-15T02:18:21Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning [115.75967665222635]
  Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
  arXiv  Detail & Related papers  (2020-09-13T14:53:01Z)
• Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications [55.65768284748698]
  Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. This article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.
  arXiv  Detail & Related papers  (2020-08-06T12:37:14Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.