Cargando…
Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ)...
Autores principales: | Kim, Brian, Sagduyu, Yalin, Davaslioglu, Kemal, Erpek, Tugba, Ulukus, Sennur |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407147/ https://www.ncbi.nlm.nih.gov/pubmed/36010711 http://dx.doi.org/10.3390/e24081047 |
Ejemplares similares
-
UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
por: Abdel-Malek, Mai A., et al.
Publicado: (2022) -
Straggler-Aware Distributed Learning: Communication–Computation Latency Trade-Off
por: Ozfatura, Emre, et al.
Publicado: (2020) -
Low-latency and High-Reliability FBMC Modulation scheme using Optimized Filter design for enabling NextG Real-time Smart Healthcare Applications
por: Adarsh, Abhinav, et al.
Publicado: (2022) -
Secure Degrees of Freedom in Networks with User Misbehavior
por: Banawan, Karim, et al.
Publicado: (2019) -
Using Timeliness in Tracking Infections †
por: Bastopcu, Melih, et al.
Publicado: (2022)