Leveraging IRS Induced Time Delay for Enhanced Physical Layer Security in VLC Systems

• URL: http://arxiv.org/abs/2402.03202v2
• Date: Fri, 10 May 2024 15:03:43 GMT
• Title: Leveraging IRS Induced Time Delay for Enhanced Physical Layer Security in VLC Systems
• Authors: Rashid Iqbal, Mauro Biagi, Ahmed Zoha, Muhammad Ali Imran, Hanaa Abumarshoud,
• Abstract summary: Internal visible light communication (VLC) is considered secure against attackers outside the confined area where the light propagates. New technology, intelligent reflecting surfaces (IRS) has been recently introduced, offering a way to enhance physical layer security (PLS) This paper tackles, for the first time, the effect of time delay on the secrecy rate in VLC systems.
• Score: 8.086305886285146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Indoor visible light communication (VLC) is considered secure against attackers outside the confined area where the light propagates, but it is still susceptible to interception from inside the coverage area. A new technology, intelligent reflecting surfaces (IRS), has been recently introduced, offering a way to enhance physical layer security (PLS). Most research on IRS-assisted VLC assumes the same time of arrival from all reflecting elements and overlooks the effect of time delay and the associated intersymbol interference. This paper tackles, for the first time, the effect of time delay on the secrecy rate in VLC systems. Our results show that, at a fixed light-emitting diode (LED) power of 3W, the secrecy rate can be enhanced by up to 253\% at random positions for the legitimate user when the eavesdropper is located within a 1-meter radius of the LED. Our results also show that careful allocation of the IRS elements can lead to enhanced PLS even when the eavesdropper has a more favourable position and, thus, a better channel gain than the legitimate user.

Related papers

• The Early Bird Catches the Leak: Unveiling Timing Side Channels in LLM Serving Systems [26.528288876732617]
  A set of new timing side channels can be exploited to infer confidential system prompts and those issued by other users. These vulnerabilities echo security challenges observed in traditional computing systems. We propose a token-by-token search algorithm to efficiently recover shared prompt prefixes in the caches.
  arXiv  Detail & Related papers  (2024-09-30T06:55:00Z)
• Physical Layer Deception with Non-Orthogonal Multiplexing [52.11755709248891]
  We propose a novel framework of physical layer deception (PLD) to actively counteract wiretapping attempts. PLD combines PLS with deception technologies to actively counteract wiretapping attempts. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches.
  arXiv  Detail & Related papers  (2024-06-30T16:17:39Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising [94.09442506816724]
  Blind-spot networks (BSN) have been prevalent network architectures in self-supervised image denoising (SSID) We present a transformer-based blind-spot network (TBSN) by analyzing and redesigning the transformer operators that meet the blind-spot requirement. For spatial self-attention, an elaborate mask is applied to the attention matrix to restrict its receptive field, thus mimicking the dilated convolution. For channel self-attention, we observe that it may leak the blind-spot information when the channel number is greater than spatial size in the deep layers of multi-scale architectures.
  arXiv  Detail & Related papers  (2024-04-11T15:39:10Z)
• Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition [14.18507067281377]
  This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided blockage Dynamic Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model.
  arXiv  Detail & Related papers  (2023-06-22T10:10:25Z)
• Seeing Through The Noisy Dark: Toward Real-world Low-Light Image Enhancement and Denoising [125.56062454927755]
  Real-world low-light environment usually suffer from lower visibility and heavier noise, due to insufficient light or hardware limitation. We propose a novel end-to-end method termed Real-world Low-light Enhancement & Denoising Network (RLED-Net)
  arXiv  Detail & Related papers  (2022-10-02T14:57:23Z)
• Low-Light Hyperspectral Image Enhancement [90.84144276935464]
  This work focuses on the low-light HSI enhancement task, which aims to reveal the spatial-spectral information hidden in darkened areas. Based on Laplacian pyramid decomposition and reconstruction, we developed an end-to-end data-driven low-light HSI enhancement (HSIE) approach. The effectiveness and efficiency of HSIE both in quantitative assessment measures and visual effects are demonstrated.
  arXiv  Detail & Related papers  (2022-08-05T08:45:52Z)
• Intelligent Reflecting Surfaces for Enhanced NOMA-based Visible Light Communications [32.95296758110093]
  We investigate the role that IRSs can play in enhancing the link reliability in VLC systems employing non-orthogonal multiple access (NOMA) We propose a framework for the joint optimisation of the NOMA and IRS parameters and show that it provides significant enhancements in link reliability.
  arXiv  Detail & Related papers  (2021-11-08T17:16:00Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• StrObe: Streaming Object Detection from LiDAR Packets [73.27333924964306]
  Rolling shutter LiDARs emitted as a stream of packets, each covering a sector of the 360deg coverage. Modern perception algorithms wait for the full sweep to be built before processing the data, which introduces an additional latency. In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
  arXiv  Detail & Related papers  (2020-11-12T14:57:44Z)
• Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning [32.34501171201543]
  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.
  arXiv  Detail & Related papers  (2020-04-07T00:48:58Z)

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.