Intelligent signal processing for wireless communications is a vital task in modern wireless
systems, but it faces new challenges because of network heterogeneity, diverse service …

Wireless signal recognition plays an important role in cognitive radio, which promises a
broad prospect in spectrum monitoring and management with the coming applications for …

The ability to classify different types of signal modulations in radio transmissions is an
important task with applications in defense, networking, and communications. This process …

With the crowding of the electromagnetic spectrum and the shrinking cell size in wireless
networks, crosstalk between base stations and users is a major problem. Although hand …

Deep learning (DL) has found extensive applications in wireless communication, propelling
DL-based physical layer orthogonal frequency division multiplexing (OFDM) receivers to the …

Deep learning is driving a radical paradigm shift in wireless communications, all the way
from the application layer down to the physical layer. Despite this, there is an ongoing …

We propose a robust spectrum sensing framework based on deep learning. The received
signals at the secondary user's receiver are filtered, sampled and then directly fed into a …

With the rapid development of the communication industry in the fifth generation and the
advance towards the intelligent society of the sixth generation wireless networks, traditional …

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 …

The rapid development of mobile technologies has enabled billions of people worldwide to
stay connected, facilitating a broad spectrum of activities ranging from information access to …