44 research outputs found
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
Spectrum coexistence is essential for next generation (NextG) systems to
share the spectrum with incumbent (primary) users and meet the growing demand
for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service
(CBRS) band, where the 5G and beyond communication systems need to sense the
spectrum and then access the channel in an opportunistic manner when the
incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity
classifier based on a deep neural network is needed for low misdetection (to
protect incumbent users) and low false alarm (to achieve high throughput for
NextG). In a dynamic wireless environment, the classifier can only be used for
a limited period of time, i.e., coherence time. A portion of this period is
used for learning to collect sensing results and train a classifier, and the
rest is used for transmissions. In spectrum sharing systems, there is a
well-known tradeoff between the sensing time and the transmission time. While
increasing the sensing time can increase the spectrum sensing accuracy, there
is less time left for data transmissions. In this paper, we present a
generative adversarial network (GAN) approach to generate synthetic sensing
results to augment the training data for the deep learning classifier so that
the sensing time can be reduced (and thus the transmission time can be
increased) while keeping high accuracy of the classifier. We consider both
additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this
GAN-based approach can significantly improve both the protection of the
high-priority user and the throughput of the NextG user (more in Rayleigh
channels than AWGN channels)
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Defense strategies have been well studied to combat Byzantine attacks that
aim to disrupt cooperative spectrum sensing by sending falsified versions of
spectrum sensing data to a fusion center. However, existing studies usually
assume network or attackers as passive entities, e.g., assuming the prior
knowledge of attacks is known or fixed. In practice, attackers can actively
adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by
defense strategies. In this paper, we revisit this security vulnerability as an
adversarial machine learning problem and propose a novel learning-empowered
attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion
center. Based on the black-box nature of the fusion center in cooperative
spectrum sensing, our new perspective is to make the adversarial use of machine
learning to construct a surrogate model of the fusion center's decision model.
We propose a generic algorithm to create malicious sensing data using this
surrogate model. Our real-world experiments show that the LEB attack is
effective to beat a wide range of existing defense strategies with an up to 82%
of success ratio. Given the gap between the proposed LEB attack and existing
defenses, we introduce a non-invasive method named as influence-limiting
defense, which can coexist with existing defenses to defend against LEB attack
or other similar attacks. We show that this defense is highly effective and
reduces the overall disruption ratio of LEB attack by up to 80%