Reference : A Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/46771
A Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures
English
Mayouche, Abderrahmane mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Alves Martins, Wallace mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Tsinos, Christos G. mailto []
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2021
IEEE Wireless Communications and Networking Conference (WCNC), Najing 29 March to 01 April 2021
Yes
Yes
International
IEEE Wireless Communications and Networking Conference (WCNC)
From 29-03-2021 to 01-04-2021
[en] Physical-layer security ; symbol-level precoding ; machine learning ; channel coding ; multi-user interference
[en] In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users.
http://hdl.handle.net/10993/46771
FnR ; FNR11607830 > Bjorn Ottersten > CI-PHY > Exploiting Interference For Physical Layer Security In 5g Networks > 01/02/2018 > 31/01/2021 > 2017

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