Machine Learning in NextG Networks via Generative Adversarial Networks

• URL: http://arxiv.org/abs/2203.04453v1
• Date: Wed, 9 Mar 2022 00:15:34 GMT
• Title: Machine Learning in NextG Networks via Generative Adversarial Networks
• Authors: Ender Ayanoglu and Kemal Davaslioglu and Yalin E. Sagduyu
• Abstract summary: Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems. We investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iv) mitigating security attacks.
• Score: 6.045977607688583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and nonrepeatable. Second, they enable pre-training classifiers by using semi-supervised data. Third, they facilitate increased resolution. Fourth, they enable the recovery of corrupted bits in the spectrum. The paper provides the basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)-iii) above, and future research directions. As a use case of GAN for NextG communications, we show that a GAN can be effectively applied for anomaly detection in signal classification (e.g., user authentication) outperforming another state-of-the-art ML technique such as an autoencoder.

Related papers

• Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks [0.25163931116642785]
  We propose a novel approach for enhancing the performance of an NIDS through the integration of Generative Adversarial Networks (GANs) GANs generate synthetic network traffic data that closely mimics real-world network behavior. Our findings show that the integration of GANs into NIDS can lead to enhancements in intrusion detection performance for attacks with limited training data.
  arXiv  Detail & Related papers  (2024-04-11T04:01:15Z)
• Generative AI for Physical Layer Communications: A Survey [76.61956357178295]
  generative artificial intelligence (GAI) has the potential to enhance the efficiency of digital content production. GAI's capability in analyzing complex data distributions offers great potential for wireless communications. This paper presents a comprehensive investigation of GAI's applications for communications at the physical layer, ranging from traditional issues, including signal classification, channel estimation, and equalization, to emerging topics, such as intelligent reflecting surfaces and joint source channel coding.
  arXiv  Detail & Related papers  (2023-12-09T15:20:56Z)
• Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks [0.0]
  The complexity and diversity of Telecom networks place an increasing strain on maintenance and operation efforts. Strict security and privacy requirements present a challenge for mobile operators to leverage network data. We propose a Bi-level Federated Graph Neural Network anomaly detection and diagnosis model.
  arXiv  Detail & Related papers  (2023-11-24T13:23:54Z)
• Generative adversarial networks for data-scarce spectral applications [0.0]
  We report on an application of GANs in the domain of synthetic spectral data generation. We show that CWGANs can act as a surrogate model with improved performance in the low-data regime.
  arXiv  Detail & Related papers  (2023-07-14T16:27:24Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation [50.779498955162644]
  We propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process. Results show that the proposed framework outperformed the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets.
  arXiv  Detail & Related papers  (2020-06-11T16:15:06Z)
• Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments [9.023847175654602]
  Generative Adversarial Network (GAN) is an effective method to produce samples of large-scale data distribution. GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously.
  arXiv  Detail & Related papers  (2020-05-27T05:56:53Z)
• xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems [16.360144499713524]
  Generative Adversarial Networks (GANs) are a revolutionary class of Deep Neural Networks (DNNs) that have been successfully used to generate realistic images, music, text, and other data. We propose a new class of GAN that leverages recent advances in explainable AI (xAI) systems to provide a "richer" form of corrective feedback from discriminators to generators. We observe xAI-GANs provide an improvement of up to 23.18% in the quality of generated images on both MNIST and FMNIST datasets over standard GANs.
  arXiv  Detail & Related papers  (2020-02-24T18:38:13Z)
• 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)
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications [154.4832792036163]
  Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. This paper provides a review on various GANs methods from the perspectives of algorithms, theory, and applications.
  arXiv  Detail & Related papers  (2020-01-20T01:52:05Z)

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.