Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models

• URL: http://arxiv.org/abs/2408.12193v1
• Date: Thu, 22 Aug 2024 08:16:02 GMT
• Title: Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models
• Authors: Stefanos Bakirtzis, Cagkan Yapar, Marco Fiore, Jie Zhang, Ian Wassell,
• Abstract summary: This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance wireless network operation. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction.
• Score: 6.217047612833474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications.

Related papers

• Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study [59.780800481241066]
  Generative Artificial Intelligence (GenAI) can be an effective alternative to wireless data augmentation. This article explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks. We propose a general generative diffusion model-based data augmentation framework for Wi-Fi gesture recognition.
  arXiv  Detail & Related papers  (2024-11-13T05:15:25Z)
• Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges [2.5571334829357415]
  Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services.
  arXiv  Detail & Related papers  (2024-10-31T14:04:33Z)
• RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments [1.7074276434401858]
  We introduce a novel machine learning-empowered methodology for wireless channel modeling. Key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes.
  arXiv  Detail & Related papers  (2024-06-04T01:06:41Z)
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN [63.90647197249949]
  In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. In this paper, we present a cooperative radio map estimation approach enabled by the generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2024-02-05T05:01:28Z)
• 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)
• In-Network Learning: Distributed Training and Inference in Networks [10.635097939284753]
  We develop a learning algorithm and an architecture that make use of multiple data streams and processing units. In particular, the analysis reveals how inference propagates and fuses across a network.
  arXiv  Detail & Related papers  (2021-07-07T18:35:08Z)
• 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)
• Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services [10.718353079920007]
  This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.
  arXiv  Detail & Related papers  (2021-03-18T14:18:34Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)
• 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)
• Deep Learning for Wireless Communications [3.7506111080592386]
  We first describe how deep learning is used to design an end-to-end communication system using autoencoders. Next, we present the benefits of deep learning in spectrum situation awareness. Finally, we discuss how deep learning applies to wireless communication security.
  arXiv  Detail & Related papers  (2020-05-12T21:58:44Z)

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.