Low-Complexity Neural Networks for Denoising Imperfect CSI in Physical Layer Security-Reference-Cited by-同舟云学术

Low-Complexity Neural Networks for Denoising Imperfect CSI in Physical Layer Security

Published:2023-06-06 Issue: Volume: Page:
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Container-title:2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
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Author:

Ajayi Idowu¹,Medjahdi Yahia²,Mroueh Lina¹,Okubadejo Olumide³,Kaddour Fatima⁴

Affiliation:

1. Institut Supérieur d’Électronique de Paris (ISEP),Paris,France

2. IMT Nord Europe, Institut Mines-Télécom, Univ. Lille,Centre for Digital Systems,Lille,France,F-59000

3. École Supérieure d'Ingénieurs en Électrotechnique et Électronique (ESIEE),Paris,France

4. Agence Nationale des Fréquences (ANFR),Maisons-Alfort,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10187889/10188221/10188372.pdf?arnumber=10188372

Reference18 articles.

1. Generalized denoising auto-encoders as generative models;bengio;26th International Conference on Neural Information Processing Systems ser NIPS'13,2013

2. An Overview of Physical Layer Security in Wireless Communication Systems With CSIT Uncertainty

3. Tune: A research platform for distributed model selection and training;liaw;ArXiv Preprint,2018

4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

5. Physical-Layer Security for Industrial Wireless Control Systems: Basics and Future Directions

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1. Inspiring Physical Layer Security With RIS: Principles, Applications, and Challenges;IEEE Open Journal of the Communications Society;2024