Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning

• URL: http://arxiv.org/abs/2004.03056v2
• Date: Sun, 21 Feb 2021 01:54:55 GMT
• Title: Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning
• Authors: Yizhuo Song, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong and Apriana Toding
• Abstract summary: This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time.
• Score: 32.34501171201543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.

Related papers

• Outdoor Environment Reconstruction with Deep Learning on Radio Propagation Paths [5.030571576007511]
  This paper proposes a novel approach harnessing ambient wireless signals for outdoor environment reconstruction. By analyzing radio frequency (RF) data, the paper aims to deduce the environmental characteristics and digitally reconstruct the outdoor surroundings. Two DL-driven approaches are evaluated, with performance assessed using metrics like intersection-over-union (IoU), Hausdorff distance, and Chamfer distance.
  arXiv  Detail & Related papers  (2024-02-27T09:11:10Z)
• Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning [95.31679010587473]
  Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks. This article provides a comprehensive overview of the positive and negative effects of interference on over-the-air-based edge learning systems.
  arXiv  Detail & Related papers  (2023-06-17T09:04:48Z)
• Machine Learning for Metasurfaces Design and Their Applications [20.350142630673197]
  Machine/deep learning (ML/DL) techniques are proving critical in reducing the computational cost and time of RIS inverse design. This chapter provides a synopsis of DL techniques for both inverse RIS design and RIS-assisted wireless systems.
  arXiv  Detail & Related papers  (2022-11-02T17:19:37Z)
• 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)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods [83.98961686408171]
  Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS.
  arXiv  Detail & Related papers  (2022-04-28T09:26:14Z)
• Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces [0.0]
  We present a deep learning (DL) approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment. The proposed method uses the received pilot signals reflected through the IRS to train the deep feedforward network.
  arXiv  Detail & Related papers  (2020-09-29T13:18:24Z)
• A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces [22.51807198305316]
  Intelligent reflecting surfaces (IRSs) have recently received significant attention for wireless communications. Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges. This article provides a synopsis of these techniques for designing DL-based IRS-assisted wireless systems.
  arXiv  Detail & Related papers  (2020-09-05T14:26:48Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)
• 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)

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.