Machine Learning-Based Vector Quantization for Secret Key Generation in Physical Layer Security-Reference-Cited by-同舟云学术

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Machine Learning-Based Vector Quantization for Secret Key Generation in Physical Layer Security

Published:2024-02-19 Issue: Volume: Page:1-7
ISSN:
Container-title:2024 Global Information Infrastructure and Networking Symposium (GIIS)
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Author:

Bagheri Ghazal¹,Boroujeni Ali Khandan²,Köpsell Stefan³

Affiliation:

1. University of Technology,Chair of Privacy and Security Dresden,Dresden,Germany

2. Trustworthy Data Processing, Barkhausen Institute gGmbH,Dresden,Germany

3. University of Technology,Chair of Privacy and Security and also with Centre for Tactile Internet with Human-in-the-Loop (CeTI) Dresden,Dresden,Germany

Funder

Federal Ministry of Education and Research, Germany

CeTI

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10449900/10449724/10449919.pdf?arnumber=10449919

Reference12 articles.

1. A Survey of Security Challenges in Cognitive Radio Networks: Solutions and Future Research Directions

2. The challenges facing physical layer security

3. A survey on secret key generation mechanisms on the physical layer in wireless networks

4. Key Generation From Wireless Channels: A Review

5. High-Rate Uncorrelated Bit Extraction for Shared Secret Key Generation from Channel Measurements

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