As the number of devices connected to the internet and the amount of data they generate
increases, the wireless spectrum is becoming an essential and scarce resource. Most …

Traffic Classification (TC) systems allow inferring the application that is generating the traffic
being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have …

With the emergence of new technologies and standards for wireless communications and an
increase in application and user requirements, the number and density of deployed wireless …

Real-time information exchange on traffic and channel selection results among users in
dynamic spectrum access (DSA) system consumes scarce spectrum resources. However, it …

Since the early days of wireless communication, wireless spectrum has been allocated
according to a static frequency plan, whereby most of the spectrum is licensed for exclusive …

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal.
The mechanism employs a binary exponential backoff (BEB) algorithm in the medium …

In this work, we propose an artificial intelligence (AI) based channel quality assessment
algorithm for Bluetooth adaptive frequency hopping (AFH) to avoid interference between …

A novel LEarning-based Spectrum Sensing and Access (LESSA) framework is proposed,
wherein a cognitive radio (CR) learns a time-frequency correlation model underlying …

DARPA, the Defense Advanced Research Projects Agency from the United States, has
started the Spectrum Collaboration Challenge with the aim to encourage research and …

We demonstrate a first example for employing deep learning in predicting frame errors for a
Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation …