Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

• URL: http://arxiv.org/abs/2005.08281v2
• Date: Tue, 2 Mar 2021 10:34:33 GMT
• Title: Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
• Authors: Francesc Wilhelmi, Marc Carrascosa, Cristina Cano, Anders Jonsson, Vishnu Ram, Boris Bellalta
• Abstract summary: We devise the role of network simulators for bridging the gap between Machine Learning and communications systems. We present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. We illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.
• Score: 9.390329421385415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.

Related papers

• Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks [11.509880721677156]
  Large language models (LLMs) have recently emerged, demonstrating near-human-level performance in cognitive tasks. We propose the concept of "generative AI-in-the-loop" We believe that combining LLMs and ML models allows both to leverage their respective capabilities and achieve better results than either model alone.
  arXiv  Detail & Related papers  (2024-06-06T17:25:07Z)
• Can LLMs Understand Computer Networks? Towards a Virtual System Administrator [15.469010487781931]
  This paper is the first to conduct an exhaustive study on Large Language Models' comprehension of computer networks. We evaluate our framework on multiple computer networks employing proprietary (e.g., GPT4) and open-source (e.g., Llama2) models.
  arXiv  Detail & Related papers  (2024-04-19T07:41:54Z)
• Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks [49.19502459827366]
  Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-to-server (D2S) interactions.
  arXiv  Detail & Related papers  (2023-03-15T04:41:36Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Towards Intelligent Load Balancing in Data Centers [0.5505634045241288]
  This paper proposes Aquarius to bridge the gap between machine learning and networking systems. It demonstrates its ability of conducting both offline data analysis and online model deployment in realistic systems.
  arXiv  Detail & Related papers  (2021-10-27T12:47:30Z)
• Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs [1.0486135378491268]
  We present the results gathered from Problem Statement13 (PS-013), organized by Universitat Pompeu Fabra (UPF) The primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques.
  arXiv  Detail & Related papers  (2021-05-29T05:33:07Z)
• 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)
• 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)
• From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks [71.23327876898816]
  Federated learning has emerged as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. We advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers.
  arXiv  Detail & Related papers  (2020-06-07T05:11:18Z)
• Machine Learning for Intelligent Optical Networks: A Comprehensive Survey [9.947717243638289]
  It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML) In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical networks monitoring and survivability.
  arXiv  Detail & Related papers  (2020-03-11T13:51:38Z)
• 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.