Results 41 to 50 of about 2,530,585 (292)

A Model for Adversarial Wiretap Channels

open access: yesIEEE Transactions on Information Theory, 2016
In wiretap model of secure communication the goal is to provide (asymptotic) perfect secrecy and reliable communication over a noisy channel that is eavesdropped by an adversary with unlimited computational power. This goal is achieved by taking advantage of the channel noise and without requiring a shared key.
Pengwei Wang 0006, Reihaneh Safavi-Naini
openaire   +2 more sources

A Physical-Layer Secure Coding Scheme for Indoor Visible Light Communication Based on Polar Codes

open access: yesIEEE Photonics Journal, 2018
Visible light communication (VLC) can provide short-range optical wireless communication together with illumination using LED lightings. Since VLC channel is an open wireless channel, physical-layer security is desirable in order to hide secret ...
Zhen Che   +6 more
doaj   +1 more source

Deep Learning Based Precoding for the MIMO Gaussian Wiretap Channel [PDF]

open access: yes2019 IEEE Globecom Workshops (GC Wkshps), 2019
A novel precoding method based on supervised deep neural networks is introduced for the multiple-input multiple-output Gaussian wiretap channel. The proposed deep learning (DL)-based precoding learns the input covariance matrix through offline training ...
Xinliang Zhang, M. Vaezi
semanticscholar   +1 more source

The degraded Gaussian diamond-wiretap channel [PDF]

open access: yes2015 IEEE International Symposium on Information Theory (ISIT), 2015
26 pages, 6 figures, a short version will appear in Proc.
Si-Hyeon Lee, Ashish Khisti
openaire   +2 more sources

Wiretap channel with causal state information [PDF]

open access: yes2010 IEEE International Symposium on Information Theory, 2010
V2: Minor edits. 19 pages, 3 figures V3: Minor edits.
Yeow-Khiang Chia, Abbas El Gamal
openaire   +2 more sources

Deep Learning for the Gaussian Wiretap Channel [PDF]

open access: yesICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2018
End-to-end learning of communication systems with neural networks and particularly autoencoders is an emerging research direction which gained popularity in the last year.
Rick Fritschek   +2 more
semanticscholar   +1 more source

Adversarial wiretap channel with public discussion [PDF]

open access: yes2015 IEEE Conference on Communications and Network Security (CNS), 2015
Wyner's elegant model of wiretap channel exploits noise in the communication channel to provide perfect secrecy against a computationally unlimited eavesdropper without requiring a shared key. We consider an adversarial model of wiretap channel proposed in [18,19] where the adversary is active: it selects a fraction $ρ_r$ of the transmitted codeword to
Pengwei Wang 0006, Reihaneh Safavi-Naini
openaire   +2 more sources

Secret Sharing over Fast-Fading MIMO Wiretap Channels

open access: yesEURASIP Journal on Wireless Communications and Networking, 2009
Secret sharing over the fast-fading MIMO wiretap channel is considered. A source and a destination try to share secret information over a fast-fading MIMO channel in the presence of an eavesdropper who also makes channel observations that are different ...
Bloch Matthieu, Wong TanF, Shea JohnM
doaj   +2 more sources

Cooperation Diversity for Secrecy Enhancement in Cognitive Relay Wiretap Network Over Correlated Fading Channels

open access: yesIEEE Access, 2018
In this paper, we investigate the secrecy performance of dual-hop randomize-and-forward (RaF) cognitive relay multi-channel wiretap networks over correlated fading channels, in which the eavesdropper can wiretap the information from source and relays ...
Mu Li   +4 more
doaj   +1 more source

Randomized Turbo Codes for the Wiretap Channel [PDF]

open access: yesGLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017
We study application of parallel and serially concatenated convolutional codes known as turbo codes to the randomized encoding scheme introduced by Wyner for physical layer security. For this purpose, we first study how randomized convolutional codes can be constructed. Then, we use them as building blocks for developing randomized turbo codes. We also
Nooraiepour, Alireza, Duman, Tolga M.
openaire   +3 more sources

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