Deep learning at the physical layer: System challenges and applications to 5G and beyond
F Restuccia, T Melodia - IEEE Communications Magazine, 2020 - ieeexplore.ieee.org
The unprecedented requirements of IoT have made fine-grained optimization of spectrum
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments
K Sankhe, M Belgiovine, F Zhou… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Due to the unprecedented scale of the Internet of Things, designing scalable, accurate,
energy-efficient and tamper-proof authentication mechanisms has now become more …
energy-efficient and tamper-proof authentication mechanisms has now become more …
Threats of adversarial attacks in DNN-based modulation recognition
With the emergence of the information age, mobile data has become more random,
heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly …
heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly …
DeepRadioID: Real-time channel-resilient optimization of deep learning-based radio fingerprinting algorithms
Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by
leveraging the unique hardware-level imperfections imposed on the received wireless …
leveraging the unique hardware-level imperfections imposed on the received wireless …
AI assisted PHY in future wireless systems: Recent developments and challenges
Nowadays, the rapid development of artificial intelligence (AI) provides a fresh perspective
in designing future wireless communication systems. Innumerable attempts exploiting AI …
in designing future wireless communication systems. Innumerable attempts exploiting AI …
Deepsense: Fast wideband spectrum sensing through real-time in-the-loop deep learning
Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz
bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly …
bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly …
Joint detection and classification of RF signals using deep learning
With the rapid expansion of wireless technologies, monitoring and regulating the Radio
Frequency (RF) spectrum usage becomes more important than ever. In this paper, we …
Frequency (RF) spectrum usage becomes more important than ever. In this paper, we …
Arena: A 64-antenna SDR-based ceiling grid testing platform for sub-6 GHz 5G-and-Beyond radio spectrum research
Arena is an open-access wireless testing platform based on a grid of antennas mounted on
the ceiling of a large office-space environment. Each antenna is connected to programmable …
the ceiling of a large office-space environment. Each antenna is connected to programmable …
DeepBeam: Deep waveform learning for coordination-free beam management in mmWave networks
Highly directional millimeter wave (mmWave) radios need to perform beam management to
establish and maintain reliable links. To achieve this objective, existing solutions mostly rely …
establish and maintain reliable links. To achieve this objective, existing solutions mostly rely …
Generalized wireless adversarial deep learning
Deep learning techniques can classify spectrum phenomena (eg, waveform modulation)
with accuracy levels that were once thought impossible. Although we have recently seen …
with accuracy levels that were once thought impossible. Although we have recently seen …