Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Coupled oscillators are highly complex dynamical systems, and it is an intriguing concept to
use this oscillator dynamics for computation. The idea is not new, but is currently the subject …

We conduct an in depth study on the performance of deep learning based radio signal
classification for radio communications signals. We consider a rigorous baseline method …

We present and discuss several novel applications of deep learning for the physical layer.
By interpreting a communications system as an autoencoder, we develop a fundamental …

This paper looks into the modulation classification problem for a distributed wireless
spectrum sensing network. First, a new data-driven model for automatic modulation …

This paper presents end-to-end learning from spectrum data-an umbrella term for new
sophisticated wireless signal identification approaches in spectrum monitoring applications …

Abstract 6G is expected to support the unprecedented Internet of everything scenarios with
extremely diverse and challenging requirements. To fulfill such diverse requirements …

The spectrum sensing problem has gained new aspects with cognitive radio and
opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive …

With the rapid deployment of new wireless devices and applications, the last decade has
witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum …

Spectrum sensing is a key function of cognitive radio to prevent the harmful interference with
licensed users and identify the available spectrum for improving the spectrum's utilization …