47,499 research outputs found
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification
Non-cooperative communications, where a receiver can automatically
distinguish and classify transmitted signal formats prior to detection, are
desirable for low-cost and low-latency systems. This work focuses on the deep
learning enabled blind classification of multi-carrier signals covering their
orthogonal and non-orthogonal varieties. We define two signal groups, in which
Type-I includes signals with large feature diversity while Type-II has strong
feature similarity. We evaluate time-domain and frequency-domain convolutional
neural network (CNN) models in simulation with wireless channel/hardware
impairments. Simulation results reveal that the time-domain neural network
training is more efficient than its frequency-domain counterpart in terms of
classification accuracy and computational complexity. In addition, the
time-domain CNN models can classify Type-I signals with high accuracy but
reduced performance in Type-II signals because of their high signal feature
similarity. Experimental systems are designed and tested, using software
defined radio (SDR) devices, operated for different signal formats to form full
wireless communication links with line-of-sight and non-line-of-sight
scenarios. Testing, using four different time-domain CNN models, showed the
pre-trained CNN models to have limited efficiency and utility due to the
mismatch between the analytical/simulation and practical/real-world
environments. Transfer learning, which is an approach to fine-tune learnt
signal features, is applied based on measured over-the-air time-domain signal
samples. Experimental results indicate that transfer learning based CNN can
efficiently distinguish different signal formats in both line-of-sight and
non-line-of-sight scenarios with great accuracy improvement relative to the
non-transfer-learning approaches
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Radio emission from air showers enables measurements of cosmic particle
kinematics and identity. The radio signals are detected in broadband Megahertz
antennas among continuous background noise. We present two deep learning
concepts and their performance when applied to simulated data. The first
network classifies time traces as signal or background. We achieve a true
positive rate of about 90% for signal-to-noise ratios larger than three with a
false positive rate below 0.2%. The other network is used to clean the time
trace from background and to recover the radio time trace originating from an
air shower. Here we achieve a resolution in the energy contained in the trace
of about 20% without a bias for of the traces with a signal. The
obtained frequency spectrum is cleaned from signals of radio frequency
interference and shows the expected shape.Comment: 20 pages, 13 figures, resubmitted to JINS
Robust Audio Adversarial Example for a Physical Attack
We propose a method to generate audio adversarial examples that can attack a
state-of-the-art speech recognition model in the physical world. Previous work
assumes that generated adversarial examples are directly fed to the recognition
model, and is not able to perform such a physical attack because of
reverberation and noise from playback environments. In contrast, our method
obtains robust adversarial examples by simulating transformations caused by
playback or recording in the physical world and incorporating the
transformations into the generation process. Evaluation and a listening
experiment demonstrated that our adversarial examples are able to attack
without being noticed by humans. This result suggests that audio adversarial
examples generated by the proposed method may become a real threat.Comment: Accepted to IJCAI 201
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