Secure Integrated Sensing and Communication Under Correlated Rayleigh Fading

• URL: http://arxiv.org/abs/2408.17050v1
• Date: Fri, 30 Aug 2024 07:16:55 GMT
• Title: Secure Integrated Sensing and Communication Under Correlated Rayleigh Fading
• Authors: Martin Mittelbach, Rafael F. Schaefer, Matthieu Bloch, Aylin Yener, Onur Günlü,
• Abstract summary: We consider a secure integrated sensing and communication (ISAC) scenario, in which a signal is transmitted through a state-dependent wiretap channel. We establish and illustrate an achievable secrecy-distortion region for degraded secure ISAC channels under correlated Rayleigh fading.
• Score: 35.096935840816684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a secure integrated sensing and communication (ISAC) scenario, in which a signal is transmitted through a state-dependent wiretap channel with one legitimate receiver with which the transmitter communicates and one honest-but-curious target that the transmitter wants to sense. The secure ISAC channel is modeled as two state-dependent fast-fading channels with correlated Rayleigh fading coefficients and independent additive Gaussian noise components. Delayed channel outputs are fed back to the transmitter to improve the communication performance and to estimate the channel state sequence. We establish and illustrate an achievable secrecy-distortion region for degraded secure ISAC channels under correlated Rayleigh fading. We also evaluate the inner bound for a large set of parameters to derive practical design insights for secure ISAC methods. The presented results include in particular parameter ranges for which the secrecy capacity of a classical wiretap channel setup is surpassed and for which the channel capacity is approached.

Related papers

• Transmitter Actions for Secure Integrated Sensing and Communication [23.322477552758237]
  This work models a secure integrated sensing and communication (ISAC) system as a wiretap channel with action-dependent channel states and channel output feedback. The transmitted message is split into a common and a secure message, both of which must be reliably recovered at the legitimate receiver. The secure message needs to be kept secret from the eavesdropper.
  arXiv  Detail & Related papers  (2024-08-24T17:37:23Z)
• 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)
• Security for adversarial wiretap channels [4.383205675898942]
  We analyze information-theoretically secure coding schemes which use the inverse of an extractor and an error-correcting code. We show that this also holds for certain channel types with memory.
  arXiv  Detail & Related papers  (2024-04-02T09:22:40Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Interference and noise cancellation for joint communication radar (JCR) system based on contextual information [11.861415744626076]
  This paper examines the separation of wireless communication and radar signals. We show that the optimizing beamforming weights mitigate the interference caused by signals. When the channel responses were unknown, we designed an interference filter as a low-complex noise and interference cancellation autoencoder.
  arXiv  Detail & Related papers  (2023-02-14T02:06:21Z)
• Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments [93.30657979626858]
  We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion. We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
  arXiv  Detail & Related papers  (2022-11-02T22:07:36Z)
• Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance Evaluation [5.203329540700176]
  We design short blocklength codes for the Gaussian wiretap channel under information-theoretic security guarantees. We handle the reliability constraint via an autoencoder, and handle the secrecy constraint with hash functions. For blocklengths smaller than or equal to 16, we evaluate through simulations the probability of error at the legitimate receiver.
  arXiv  Detail & Related papers  (2022-06-07T17:52:46Z)
• Privacy-Aware Communication Over the Wiretap Channel with Generative Networks [34.6578234382717]
  We study privacy-aware communication over a wiretap channel using end-to-end learning. We propose a data-driven approach using variational autoencoder (VAE)-based joint source channel coding (JSCC)
  arXiv  Detail & Related papers  (2021-10-08T12:47:24Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)
• DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection [98.43451011898212]
  In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. We propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear.
  arXiv  Detail & Related papers  (2020-02-08T18:31:00Z)

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.