Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

With the development of 5G, the future wireless communication network tends to be more
and more intelligent. In the face of new service demands of communication in the future such …

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the
transmission capacity. Typically, digital signal processing techniques and hardware are …

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …

This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

Deployment of deep neural networks for applications that require very high throughput or
extremely low latency is a severe computational challenge, further exacerbated by …

Machine learning provides automated means to capture complex dynamics of wireless
spectrum and support better understanding of spectrum resources and their efficient …

Deep learning has great potential for automatic modulation classification (AMC). However,
its performance largely hinges upon the availability of sufficient high-quality labeled data. In …

We present a Trojan (backdoor or trapdoor) attack that targets deep learning applications in
wireless communications. A deep learning classifier is considered to classify wireless …

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have
the ability to address competitive resource allocation problems together with detection and …